This document discusses various architectures for big data solutions, including traditional, event/stream processing, lambda, kappa, unified, and microservices architectures. It provides technology mappings for Hadoop, Spark, and open source and Oracle tools. The document also outlines considerations for choosing an architecture based on latency, data volume and velocity, and other factors. Finally, it maps the big data ecosystem into categories of building blocks.
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events.
So far this mostly a development experience, with frameworks such as Oracle Event Processing, Apache Storm or Spark Streaming. With Oracle Stream Analytics, analytics on event streams can be put in the hands of the business analyst. It simplifies the implementation of event processing solutions so that every business analyst is able to graphically and decleratively define event stream processing pipelines, without having to write a single line of code or continous query language (CQL). Event Processing is no longer “complex”! This session presents Oracle Stream Analytics directly on some selected demo use cases.
Fast Data: A Customer’s Journey to Delivering a Compelling Real-Time SolutionGuido Schmutz
This is my part of the Open World 2014 presentation on Fast Data and Oracle Event Processing (OEP) 12c.
It contains an architecture discussion with some architecture patterns of where Events are useful. The 2nd part is a demo showcase showing OEP12c and BAM12c in action, analyzing the live OOW2014 twitter feed.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Oracle Stream Explorer - Simplifying Event/Stream ProcessingGuido Schmutz
The announcement of the Oracle StreamXplorer was a major step forward for bringing event processing to the masses. It so much simplyfies the implementation of event processing solutions: any business analyst will be able to graphically and decleratively define event stream processing pipelines, without having to write a single line of code or CQL. Event Processing is no longer “complex”! This session will present what Oracle StreamXplorer is and how it simplifies the development of event processing solutions compared to the Event Processing framework of the Oracle SOA Suite.
Big Data and Fast Data - Lambda Architecture in ActionGuido Schmutz
Big Data (volume) and real-time information processing (velocity) are two important aspects of Big Data systems. At first sight, these two aspects seem to be incompatible. Are traditional software architectures still the right choice? Do we need new, revolutionary architectures to tackle the requirements of Big Data?
This presentation discusses the idea of the so-called lambda architecture for Big Data, which acts on the assumption of a bisection of the data-processing: in a batch-phase a temporally bounded, large dataset is processed either through traditional ETL or MapReduce. In parallel, a real-time, online processing is constantly calculating the values of the new data coming in during the batch phase. The combination of the two results, batch and online processing is giving the constantly up-to-date view.
This talk presents how such an architecture can be implemented using Oracle products such as Oracle NoSQL, Hadoop and Oracle Event Processing as well as some selected products from the Open Source Software community. While this session mostly focuses on the software architecture of BigData and FastData systems, some lessons learned in the implementation of such a system are presented as well.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Blueprints for the analysis of social mediaGuido Schmutz
Presentation about analysis of social media in near real-time using Open Source software such as Kafka, Storm, Cassandra Titan. The architecture presented is a Lambda Architecture, where the speed layer itself is implementing using a unfied log/message architecture with Kafka as the event bus.
Reliable Data Intestion in BigData / IoTGuido Schmutz
Many of the Big Data and IoT use cases are based on combing data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache Flume, Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events.
So far this mostly a development experience, with frameworks such as Oracle Event Processing, Apache Storm or Spark Streaming. With Oracle Stream Analytics, analytics on event streams can be put in the hands of the business analyst. It simplifies the implementation of event processing solutions so that every business analyst is able to graphically and decleratively define event stream processing pipelines, without having to write a single line of code or continous query language (CQL). Event Processing is no longer “complex”! This session presents Oracle Stream Analytics directly on some selected demo use cases.
Fast Data: A Customer’s Journey to Delivering a Compelling Real-Time SolutionGuido Schmutz
This is my part of the Open World 2014 presentation on Fast Data and Oracle Event Processing (OEP) 12c.
It contains an architecture discussion with some architecture patterns of where Events are useful. The 2nd part is a demo showcase showing OEP12c and BAM12c in action, analyzing the live OOW2014 twitter feed.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Oracle Stream Explorer - Simplifying Event/Stream ProcessingGuido Schmutz
The announcement of the Oracle StreamXplorer was a major step forward for bringing event processing to the masses. It so much simplyfies the implementation of event processing solutions: any business analyst will be able to graphically and decleratively define event stream processing pipelines, without having to write a single line of code or CQL. Event Processing is no longer “complex”! This session will present what Oracle StreamXplorer is and how it simplifies the development of event processing solutions compared to the Event Processing framework of the Oracle SOA Suite.
Big Data and Fast Data - Lambda Architecture in ActionGuido Schmutz
Big Data (volume) and real-time information processing (velocity) are two important aspects of Big Data systems. At first sight, these two aspects seem to be incompatible. Are traditional software architectures still the right choice? Do we need new, revolutionary architectures to tackle the requirements of Big Data?
This presentation discusses the idea of the so-called lambda architecture for Big Data, which acts on the assumption of a bisection of the data-processing: in a batch-phase a temporally bounded, large dataset is processed either through traditional ETL or MapReduce. In parallel, a real-time, online processing is constantly calculating the values of the new data coming in during the batch phase. The combination of the two results, batch and online processing is giving the constantly up-to-date view.
This talk presents how such an architecture can be implemented using Oracle products such as Oracle NoSQL, Hadoop and Oracle Event Processing as well as some selected products from the Open Source Software community. While this session mostly focuses on the software architecture of BigData and FastData systems, some lessons learned in the implementation of such a system are presented as well.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Blueprints for the analysis of social mediaGuido Schmutz
Presentation about analysis of social media in near real-time using Open Source software such as Kafka, Storm, Cassandra Titan. The architecture presented is a Lambda Architecture, where the speed layer itself is implementing using a unfied log/message architecture with Kafka as the event bus.
Reliable Data Intestion in BigData / IoTGuido Schmutz
Many of the Big Data and IoT use cases are based on combing data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache Flume, Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the event streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and also used to be called Complex Event Processing (CEP). In the last 3 years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Apache Samza as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Event and Stream Processing and present what differences you might find between the more traditional CEP and the more modern Stream Processing solutions and show that a combination of both will bring the most value.
Internet of Things - Are traditional architectures good enough?Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
Real Time Analytics with Apache Cassandra - Cassandra Day BerlinGuido Schmutz
Time series data is everywhere: IoT, sensor data or financial transactions. The industry has moved to databases like Cassandra to handle the high velocity and high volume of data that is now common place. In this talk I will present how we have used Cassandra to store time series data. I will highlight both the Cassandra data model as well as the architecture we put in place for collecting and ingesting data into Cassandra, using Apache Kafka and Apache Storm.
Processing Twitter Events in Real-Time with Oracle Event Processing (OEP) 12cGuido Schmutz
This session will present how to connect to the Twitter Streaming API and process and analyse the tweets in real-time by using Oracle Event Processing. We will see how typical analytics on a such social media data can be done directly on the event stream using Java and/or the Continuous Query Language (CQL). How do we best store the results, so that they can be made available continously to a dashboard for presentation will be shown as well. Additionally the so-called Lambda architecture will be presented, where such an event streaming solution plays a critical part, in combination with a Big Data batch processing layer.
Lambda architecture for real time big dataTrieu Nguyen
Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
Why lambda architecture is correct solution for big data?
Twitter Storm: Ereignisverarbeitung in EchtzeitGuido Schmutz
Hadoop bzw. MapReduce eignet sich sehr gut, um grosse Datenmengen effizient verarbeiten zu können. Eine Verarbeitung in Hadoop ist jedoch immer batch-orientiert, d.h. es braucht eine gewisse Zeit, bis ein Resultat zur Verfügung steht. Für gewisse Anwendungsfälle kann dies ausreichend sein, andere Anwendungsfälle benötigen jedoch Daten in Echtzeit. Für die Lösung solcher Problemstellungen, gibt es seit einigen Jahren sogennante Complex-Event Processing (CEP) Systeme. Diese lassen es zu, direkt auf dem eingehenden Ereignisstrom Abfragen/Berechungen und Verabeitugnen durchzuführen, ohne diese Informationen erst in einer Datenbank abspeichern zu müssen.
Twitter Storm ist ein Open Source Framework für die Verarbeitung von Datenströmem in Echtzeit. Es wird auch als "Hadoop für Echtzeitverarbeitung" bezeichnet, wobei das Programmiermodell sich doch stark von Hadoop unterscheidet. Storm ist mehrheitlich in Clojure geschrieben und unterstützt Java direkt. Die grundlegenden Bausteine, die Spouts und die Bolts können sowohl in Java wie auch in anderen Programmiersprachen implementiert werden.
Diese Session präsentiert, wie man mit Hilfe von Twitter Storm Applikaitonen implementieren kann und zeigt entsprechende Anwendungsfälle, welche sich mit Twitter Storm lösen lassen. Zudem wird diskutiert, wie sich Storm mit Hadoop und NoSQL sinnvoll kombinieren lässt.
From Events to Networks: Time Series Analysis on ScaleDr. Mirko Kämpf
Event processing, time series aggregation and analysis, and finally analysis of structural patterns between those data snippets can all be done on Hadoop clusters on huge data volumes.
In order to find hidden relations and invisible structures one has to combine three disciplines using a variety of tools. Luckily, the Hadoop ecosystem offers many of such tools. In this session you can see practical examples and a demonstration of the "Hadoop-Oscilloscope". Generic analysis patterns and recommendations towards selection of appropriate algorithms will also provide additional background.
"Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about...Kai Wähner
I discuss a good big data architecture which includes Data Warehouse / Business Intelligence + Apache Hadoop + Real Time / Stream Processing. Several real world example are shown. TIBCO offers some very nice products for realizing these use cases, e.g. Spotfire (Business Intelligence / BI), StreamBase (Stream Processing), BusinessEvents (Complex Event Processing / CEP) and BusinessWorks (Integration / ESB). TIBCO is also ready for Hadoop by offering connectors and plugins for many important Hadoop frameworks / interfaces such as HDFS, Pig, Hive, Impala, Apache Flume and more.
Watch this recorded webcast and listen to Infochimps CSO and Co-Founder, Dhruv Bansal, and Think Big Analytics Principal Architect, Douglas Moore, share successful use cases and recommendations for building real-time predictive analytics in your enterprise.
Migrate and Modernize Hadoop-Based Security Policies for DatabricksDatabricks
Data teams are faced with a variety of tasks when migrating Hadoop-based platforms to Databricks. A common pitfall happens during the migration step where often overlooked access control policies can block adoption. This session will focus on the best practices to migrate and modernize Hadoop-based policies to govern data access (such as those in Apache Ranger or Apache Sentry). Data architects must consider new, fine-grained access control requirements when migrating from Hadoop architectures to Databricks in order to deliver secure access to as many data sets and data consumers as possible. This session will provide guidance across open source, AWS, Azure and partner tools, such as Immuta, on how to scale existing Hadoop-based policies to dynamically support more classes of users, implement fine-grained access control and leverage automation to protect sensitive data while maximizing utility — without manual effort
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the event streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and also used to be called Complex Event Processing (CEP). In the last 3 years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Apache Samza as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Event and Stream Processing and present what differences you might find between the more traditional CEP and the more modern Stream Processing solutions and show that a combination of both will bring the most value.
Internet of Things - Are traditional architectures good enough?Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
Real Time Analytics with Apache Cassandra - Cassandra Day BerlinGuido Schmutz
Time series data is everywhere: IoT, sensor data or financial transactions. The industry has moved to databases like Cassandra to handle the high velocity and high volume of data that is now common place. In this talk I will present how we have used Cassandra to store time series data. I will highlight both the Cassandra data model as well as the architecture we put in place for collecting and ingesting data into Cassandra, using Apache Kafka and Apache Storm.
Processing Twitter Events in Real-Time with Oracle Event Processing (OEP) 12cGuido Schmutz
This session will present how to connect to the Twitter Streaming API and process and analyse the tweets in real-time by using Oracle Event Processing. We will see how typical analytics on a such social media data can be done directly on the event stream using Java and/or the Continuous Query Language (CQL). How do we best store the results, so that they can be made available continously to a dashboard for presentation will be shown as well. Additionally the so-called Lambda architecture will be presented, where such an event streaming solution plays a critical part, in combination with a Big Data batch processing layer.
Lambda architecture for real time big dataTrieu Nguyen
Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
Why lambda architecture is correct solution for big data?
Twitter Storm: Ereignisverarbeitung in EchtzeitGuido Schmutz
Hadoop bzw. MapReduce eignet sich sehr gut, um grosse Datenmengen effizient verarbeiten zu können. Eine Verarbeitung in Hadoop ist jedoch immer batch-orientiert, d.h. es braucht eine gewisse Zeit, bis ein Resultat zur Verfügung steht. Für gewisse Anwendungsfälle kann dies ausreichend sein, andere Anwendungsfälle benötigen jedoch Daten in Echtzeit. Für die Lösung solcher Problemstellungen, gibt es seit einigen Jahren sogennante Complex-Event Processing (CEP) Systeme. Diese lassen es zu, direkt auf dem eingehenden Ereignisstrom Abfragen/Berechungen und Verabeitugnen durchzuführen, ohne diese Informationen erst in einer Datenbank abspeichern zu müssen.
Twitter Storm ist ein Open Source Framework für die Verarbeitung von Datenströmem in Echtzeit. Es wird auch als "Hadoop für Echtzeitverarbeitung" bezeichnet, wobei das Programmiermodell sich doch stark von Hadoop unterscheidet. Storm ist mehrheitlich in Clojure geschrieben und unterstützt Java direkt. Die grundlegenden Bausteine, die Spouts und die Bolts können sowohl in Java wie auch in anderen Programmiersprachen implementiert werden.
Diese Session präsentiert, wie man mit Hilfe von Twitter Storm Applikaitonen implementieren kann und zeigt entsprechende Anwendungsfälle, welche sich mit Twitter Storm lösen lassen. Zudem wird diskutiert, wie sich Storm mit Hadoop und NoSQL sinnvoll kombinieren lässt.
From Events to Networks: Time Series Analysis on ScaleDr. Mirko Kämpf
Event processing, time series aggregation and analysis, and finally analysis of structural patterns between those data snippets can all be done on Hadoop clusters on huge data volumes.
In order to find hidden relations and invisible structures one has to combine three disciplines using a variety of tools. Luckily, the Hadoop ecosystem offers many of such tools. In this session you can see practical examples and a demonstration of the "Hadoop-Oscilloscope". Generic analysis patterns and recommendations towards selection of appropriate algorithms will also provide additional background.
"Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about...Kai Wähner
I discuss a good big data architecture which includes Data Warehouse / Business Intelligence + Apache Hadoop + Real Time / Stream Processing. Several real world example are shown. TIBCO offers some very nice products for realizing these use cases, e.g. Spotfire (Business Intelligence / BI), StreamBase (Stream Processing), BusinessEvents (Complex Event Processing / CEP) and BusinessWorks (Integration / ESB). TIBCO is also ready for Hadoop by offering connectors and plugins for many important Hadoop frameworks / interfaces such as HDFS, Pig, Hive, Impala, Apache Flume and more.
Watch this recorded webcast and listen to Infochimps CSO and Co-Founder, Dhruv Bansal, and Think Big Analytics Principal Architect, Douglas Moore, share successful use cases and recommendations for building real-time predictive analytics in your enterprise.
Migrate and Modernize Hadoop-Based Security Policies for DatabricksDatabricks
Data teams are faced with a variety of tasks when migrating Hadoop-based platforms to Databricks. A common pitfall happens during the migration step where often overlooked access control policies can block adoption. This session will focus on the best practices to migrate and modernize Hadoop-based policies to govern data access (such as those in Apache Ranger or Apache Sentry). Data architects must consider new, fine-grained access control requirements when migrating from Hadoop architectures to Databricks in order to deliver secure access to as many data sets and data consumers as possible. This session will provide guidance across open source, AWS, Azure and partner tools, such as Immuta, on how to scale existing Hadoop-based policies to dynamically support more classes of users, implement fine-grained access control and leverage automation to protect sensitive data while maximizing utility — without manual effort
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
More and more data sources today provide a constant data stream, from Internet of Things devices to Social Media streams. It is one thing to collect these events in the velocity they arrive, without losing any single message. An Event Hub and a data flow engine can help here. It’s another thing to do some (complex) analytics on the data. There is always the option to first store them in a data sink of choice, such as a data lake implemented with HDFS/object store, or in a database such as a NoSQL or even an RDBMS, if the volume of events is not too high. Storing a high-volume event stream is feasible and not such a challenge anymore. But doing it adds to the end-to-end latency and it’s a matter of minutes or hours until you can present some results of your analytics. If you need to react fast, you simply can't afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics directly on the data stream. This is called Stream Processing or Stream Analytics. In this talk I will present the important concepts, a Stream Processing solution should support and then dive into some of the most popular frameworks available on the market and how they compare.
Stream Processing – Concepts and FrameworksGuido Schmutz
More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. It is one thing to collect these events in the velocity they arrive, without losing any single message. An Event Hub and a data flow engine can help here. It’s another thing to do some (complex) analytics on the data. There is always the option to first store in a data sink of choice and later analyze it. Storing even a high-volume event stream is feasible and not a challenge anymore. But this adds to the end-to-end latency and it takes minutes if not hours to present results. If you need to react fast, you simply can’t afford to first store the data. You need to do process it directly on the data stream. This is called Stream Processing or Stream Analytics. In this talk I will present the important concepts, a Stream Processing solution should support and then dive into some of the most popular frameworks available on the market and how they compare.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One was is to first persist the data into a data store and then use a traditional data visualisation solution to present the data.
If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and highlights some of the products available to implement these blueprints.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are available for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
Big Data LDN 2018: FORTUNE 100 LESSONS ON ARCHITECTING DATA LAKES FOR REAL-TI...Matt Stubbs
Date: 13th November 2018
Location: Fast Data Theatre
Time: 11:10 - 11:40
Speaker: Sunil MIstry
Organisation: Attunity
About: How do you maximise the value from your operational data? There is a growing need to process and analyse data in motion, as your business looks to generate additional value from multiple data sources. Analysis of real-time data streams can deliver competitive business advantage, reduces costs and create new revenue streams.
Come learn how Attunity, through CDC technology helps organisation on this journey from a batch orientated world to the modern streaming architecture on premise and in the cloud. Learn how to bring your most valuable data from Relational OLTP, Mainframes and SAP into this modern data architecture.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
Fundamentals Big Data and AI ArchitectureGuido Schmutz
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Yael Garten
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047
Description:
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
ApacheCon North America 2019
StreamPipes is an open source self-service IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams
https://streampipes.apache.org/
30 Minutes to the Analytics Platform with Infrastructure as CodeGuido Schmutz
Analytical platforms for PoCs and evaluation can be built in the cloud in an hour - with ready-made setup scripts. But if you put the services together freely, it gets more difficult. The open-source platform-in-a-box "Platys" (https://github.com/TrivadisPF/platys) shows that it is easier for test and PoC environments. In addition to possible uses and examples, we explain services and "just briefly" set up a data lake with a database, event broker, stream processing, blob store, SQL access and data science notebook.
Event Broker (Kafka) in a Modern Data ArchitectureGuido Schmutz
Today's modern data architectures and the their implementations contain an Event Broker. What are the benefits of placing an Event Broker in a Modern Data (Analytics) Architecture? What exactly is an Event Broker and what capabilities should it provide? Why is Apache Kafka the most popular realisation of an Event Broker?
These and many other questions will be answered in this session. The talk will start with a vendor-neutral definition of the capabilities of an Event Broker.
Then the session will highlight the different architecture styles which can be supported using an Event Broker (Kafka), such as Streaming Data Integration, Stream Analytics and Decoupled Event-Driven Applications and how can these be combined into a unified architecture, making the Event Broker the central nervous system of an enterprise architecture. We will end with an overview of the Kafka ecosystem and a placement of the various components onto the Modern Data (Analytics) Architecture.
Big Data, Data Lake, Fast Data - Dataserialiation-FormatsGuido Schmutz
The concept of "Data Lake" is in everyone's mind today. The idea of storing all the data that accumulates in a company in a central location and making it available sounds very interesting at first. But Data Lake can quickly turn from a clear, beautiful mountain lake into a huge pond, especially if it is inexpertly entrusted with all the source data formats that are common in today's enterprises, such as XML, JSON, CSV or unstructured text data. Who, after some time, still has an overview of which data, which format and how they have developed over different versions? Anyone who wants to help themselves from the Data Lake must ask themselves the same questions over and over again: what information is provided, what data types do they have and how has the content changed over time?
Data serialization frameworks such as Apache Avro and Google Protocol Buffer (Protobuf), which enable platform-independent data modeling and data storage, can help. This talk will discuss the possibilities of Avro and Protobuf and show how they can be used in the context of a data lake and what advantages can be achieved. The support on Avro and Protobuf by Big Data and Fast Data platforms is also a topic.
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
Kafka as your Data Lake - is it Feasible?Guido Schmutz
For a long time we discuss how much data we can keep in Kafka. Can we store data forever or do we remove data after a while and maybe having the history in a data lake on Object Storage or HDFS? With the advent of Tiered Storage in Confluent Enterprise Platform, storing data much longer in Kafka is much very feasible. So can we replace a traditional data lake with just Kafka? Maybe at least for the raw data? But what about accessing the data, for example using SQL?
KSQL allows for processing data in a streaming fashion using an SQL like dialect. But what about reading all data of a topic? You can reset the offset and still use KSQL. But there is another family of products, so-called query engines for Big Data. They originate from the idea of reading Big Data sources such as HDFS, object storage or HBase, using the SQL language. Presto, Apache Drill and Dremio are the most popular solutions in that space. Lately these query engines also added support for Kafka topics as a source of data. With that you can read a topic as a table and join it with information available in other data sources. The idea of course is not real-time streaming analytics but batch analytics directly on the Kafka topic, without having to store it in a big data storage.
This talk answers, how well these tools support Kafka as a data source. What serialization formats do they support? Is there some form of predicate push-down supported or do we have to always read the complete topic? How performant is a query against a topic, compared to a query against the same data sitting in HDFS or an object store? And finally, will this allow us to replace our data lake or at least part of it by Apache Kafka?
Event Hub (i.e. Kafka) in Modern Data ArchitectureGuido Schmutz
Today's modern data architectures and the their implementations contain an Event Hub. What are the benefits of placing an Event Hub in a Modern Data (Analytics) Architecture? What exactly is an Event Hub and what capabilities should it provide? Why is Apache Kafka the most popular realization of an Event Hub?
These and many other questions will be answered in this session. The talk will start with a vendor-neutral definition of the capabilities of an Event Hub.
Then the session will highlight the different architecture styles which can be supported using an Event Hub (Kafka), such as Streaming Data Integration, Stream Analytics and Decoupled Event-Driven Applications and how can these be combined into a unified architecture, making the Event Hub the central nervous system of an enterprise architecture. We will end with an overview of the Kafka ecosystem and a placement of the various components onto the Modern Data (Analytics) Architecture.
Solutions for bi-directional integration between Oracle RDBMS & Apache KafkaGuido Schmutz
Apache Kafka is a popular distributed streaming data platform and more and more is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. A lot of data necessary in stream processing is stored in traditional systems backed by relational databases. This session will present different approaches for integrating relational databases with Kafka, such as Kafka Connect, Oracle GoldenGate, ORDS APIs and bridging Kafka with Oracle AQ.
Event Hub (i.e. Kafka) in Modern Data (Analytics) ArchitectureGuido Schmutz
Today's modern data architectures and the their implementations contain an Event Hub. What are the benefits of placing an Event Hub in a Modern Data (Analytics) Architecture? What exactly is an Event Hub and what capabilities should it provide? Why is Apache Kafka the most popular realization of an Event Hub? These and many other questions will be answered in this session. The talk will start with a vendor-neutral definition of the capabilities of an Event Hub. Then the session will highlight the different architecture styles which can be supported using an Event Hub (Kafka), such as Streaming Data Integration, Stream Analytics and Decoupled Event-Driven Applications and how can these be combined into a unified architecture, making the Event Hub the central nervous system of an enterprise architecture. We will end with an overview of the Kafka ecosystem and a placement of the various components onto the Modern Data (Analytics) Architecture.
Building Event Driven (Micro)services with Apache KafkaGuido Schmutz
What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will start with quick recap of how we created systems over the past 20 years and how different architectures evolved from it. The talk will show how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so.
Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Location Analytics - Real-Time Geofencing using Apache KafkaGuido Schmutz
An important underlying concept behind location-based applications is called geofencing. Geofencing is a process that allows acting on users and/or devices who enter/exit a specific geographical area, known as a geo-fence. A geo-fence can be dynamically generated—as in a radius around a point location, or a geo-fence can be a predefined set of boundaries (such as secured areas, buildings, boarders of counties, states or countries).
Geofencing lays the foundation for realizing use cases around fleet monitoring, asset tracking, phone tracking across cell sites, connected manufacturing, ride-sharing solutions and many others.
GPS tracking tells constantly and in real time where a device is located and forms the stream of events which needs to be analyzed against the much more static set of geo-fences. Many of the use cases mentioned above require low-latency actions taken place, if either a device enters or leaves a geo-fence or when it is approaching such a geo-fence. That’s where streaming data ingestion and streaming analytics and therefore the Kafka ecosystem comes into play.
This session will present how location analytics applications can be implemented using Kafka and KSQL & Kafka Streams. It highlights the exiting features available out-of-the-box and then shows how easy it is to extend it by custom defined functions (UDFs). The design of such solution so that it can scale with both an increasing amount of position events as well as geo-fences will be discussed as well.
Solutions for bi-directional integration between Oracle RDBMS and Apache KafkaGuido Schmutz
Apache Kafka is a popular distributed streaming data platform. A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Data sources flowing into Kafka are often native data streams such as social media streams, telemetry data, financial transactions and many others. But these data stream only contain part of the information. A lot of data necessary in stream processing is stored in traditional systems backed by relational databases. To implement new and modern, real-time solutions, an up-to-date view of that information is needed. So how do we make sure that information can flow between the RDBMS and Kafka, so that changes are available in Kafka as soon as possible in near-real-time? This session will present different approaches for integrating relational databases with Kafka, such as Kafka Connect, Oracle GoldenGate and bridging Kafka with Oracle Advanced Queuing (AQ).
Solutions for bi-directional integration between Oracle RDBMS & Apache KafkaGuido Schmutz
Apache Kafka is a popular distributed streaming data platform. A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Data sources flowing into Kafka are often native data streams such as social media streams, telemetry data, financial transactions and many others. But these data stream only contain part of the information. A lot of data necessary in stream processing is stored in traditional systems backed by relational databases. To implement new and modern, real-time solutions, an up-to-date view of that information is needed. So how do we make sure that information can flow between the RDBMS and Kafka, so that changes are available in Kafka as soon as possible in near-real-time? This session will present different approaches for integrating relational databases with Kafka, such as Kafka Connect, Oracle GoldenGate and bridging Kafka with Oracle Advanced Queuing (AQ).
Location Analytics Real-Time Geofencing using KafkaGuido Schmutz
An important underlying concept behind location-based applications is called geofencing. Geofencing is a process that allows acting on users and/or devices who enter/exit a specific geographical area, known as a geo-fence. A geo-fence can be dynamically generated—as in a radius around a point location, or a geo-fence can be a predefined set of boundaries (such as secured areas, buildings, boarders of counties, states or countries).
Geofencing lays the foundation for realizing use cases around fleet monitoring, asset tracking, phone tracking across cell sites, connected manufacturing, ride-sharing solutions and many others.
GPS tracking tells constantly and in real time where a device is located and forms the stream of events which needs to be analyzed against the much more static set of geo-fences. Many of the use cases mentioned above require low-latency actions taken place, if either a device enters or leaves a geo-fence or when it is approaching such a geo-fence. That’s where streaming data ingestion and streaming analytics and therefore the Kafka ecosystem comes into play.
This session will present how location analytics applications can be implemented using Kafka and KSQL & Kafka Streams. It highlights the exiting features available out-of-the-box and then shows how easy it is to extend it by custom defined functions (UDFs). The design of such solution so that it can scale with both an increasing amount of position events as well as geo-fences will be discussed as well.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solutions and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
Kafka as an event store - is it good enough?Guido Schmutz
Event Sourcing and CQRS are two popular patterns for implementing a Microservices architectures. With Event Sourcing we do not store the state of an object, but instead store all the events impacting its state. Then to retrieve an object state, we have to read the different events related to a certain object and apply them one by one. CQRS (Command Query Responsibility Segregation) on the other hand is a way to dissociate writes (Command) and reads (Query). Event Sourcing and CQRS are frequently grouped and used together to form something bigger. While it is possible to implement CQRS without Event Sourcing, the opposite is not necessarily correct. In order to implement Event Sourcing, an efficient Event Store is needed. But is that also true when combining Event Sourcing and CQRS? And what is an event store in the first place and what features should it implement?
This presentation will first discuss what functionalities an event store should offer and then present how Apache Kafka can be used to implement an event store. But is Kafka good enough or do specific event store solutions such as AxonDB or Event Store provide a better solution?
Solutions for bi-directional Integration between Oracle RDMBS & Apache KafkaGuido Schmutz
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today’s enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It’s important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Location Analytics - Real-Time Geofencing using Kafka Guido Schmutz
An important underlying concept behind location-based applications is called geofencing. Geofencing is a process that allows acting on users and/or devices who enter/exit a specific geographical area, known as a geo-fence. A geo-fence can be dynamically generated—as in a radius around a point location, or a geo-fence can be a predefined set of boundaries (such as secured areas, buildings, boarders of counties, states or countries). Geofencing lays the foundation for realising use cases around fleet monitoring, asset tracking, phone tracking across cell sites, connected manufacturing, ride-sharing solutions and many others. Many of the use cases mentioned above require low-latency actions taken place, if either a device enters or leaves a geo-fence or when it is approaching such a geo-fence. That’s where streaming data ingestion and streaming analytics and therefore the Kafka ecosystem comes into play. This session will present how location analytics applications can be implemented using Kafka and KSQL & Kafka Streams. It highlights the exiting features available out-of-the-box and then shows how easy it is to extend it by custom defined functions (UDFs).
Most data visualization solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualization capabilities. One option is to first persist the data into a data store and then use a traditional data visualization solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualization tools might already integrate with the specific data store. An other option is to use a Streaming Visualization solution. This talk presents different architecture blueprints for integrating data visualization into a fast data solutions.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
Architektur von Big Data Lösungen
1. BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF
HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH
Architektur von Big Data
Lösungen
Guido Schmutz (guido.schmutz@trivadis.com)
@gschmutz
2. Guido Schmutz
Working for Trivadis for more than 20 years
Oracle ACE Director for Fusion Middleware and SOA
Co-Author of different books
Consultant, Trainer, Software Architect for Java, SOA & Big Data / Fast Data
Member of Trivadis Architecture Board
Technology Manager @ Trivadis
More than 30 years of software development experience
Contact: guido.schmutz@trivadis.com
Blog: http://guidoschmutz.wordpress.com
Slideshare: http://www.slideshare.net/gschmutz
Twitter: gschmutz
2 Architektur von Big Data Lösungen
3. Agenda
1. Introduction
2. Big Data Reference Architectures
• Traditional Big Data
• Event / Stream-Processing
• Lambda Architecture
• Kappa Architecture
• Unified Architecture
• Microservices Architecture
3. Big Data Ecosystem – many choices sorted!
3 Architektur von Big Data Lösungen
5. Big Data Definition (4 Vs)
+ Time to action ? – Big Data + Real-Time = Stream Processing
Characteristics of Big Data: Its Volume, Velocity
and Variety in combination
Reliable Data Ingestion in Big Data/IoT
6. How to do Big Data? Why is a structuring / architecture
important?
6 Architektur von Big Data Lösungen
7. Why talk about Big Data Architectures?
Choosing the right architecture is key for any (big data) project
Big Data is still quite a rather young field and therefore a “moving target”
no standard architectures available which have been used for years
In the past years, some architectures and best practices have evolved
Know your use cases before choosing your architecture / technologies
To have a reference architecture in place helps in choosing the
right/matching technologies
7 Architektur von Big Data Lösungen
8. Important Properties for choosing (Big) Data Architecture
Latency
Keep raw and un-interpreted data “forever” ?
Volume, Velocity, Variety, Veracity
Ad-Hoc Query Capabilities needed ?
Robustness & Fault Tolerance
Scalability
…
8 Architektur von Big Data Lösungen
9. Big Data Reference Architectures -
Traditional Big Data
9 Architektur von Big Data Lösungen
10. “Traditional Architecture” for Big Data
Data
Ingestion
(Analytical) Data Processing
Data
Sources
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
Batch
compute
Pushing
Ingestion Result Store
Query
Engine
Computed
Information
Raw Data
(Reservoir)
= Data in Motion = Data at Rest
Pulling
Ingestion
Channel
10 Architektur von Big Data Lösungen
11. “Traditional Architecture” for Big Data – Hadoop
Technology Mapping
Data
Ingestion
(Analytical) Data Processing
Data
Sources
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
Batch
compute
Pushing
Ingestion Result Store
Query
Engine
Computed
Information
Raw Data
(Reservoir)
= Data in Motion = Data at Rest
Pulling
Ingestion
Channel
11 Architektur von Big Data Lösungen
12. “Traditional Architecture” for Big Data – Spark
Technology Mapping
Data
Ingestion
(Analytical) Data Processing
Data
Sources
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
Batch
compute
Pushing
Ingestion Result Store
Query
Engine
Computed
Information
Raw Data
(Reservoir)
= Data in Motion = Data at Rest
Pulling
Ingestion
Channel
12 Architektur von Big Data Lösungen
13. “Traditional Architecture” for Big Data – Feeding in High-
Volume Event Streams
Data
Ingestion
(Analytical) Data Processing
Data
Sources
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
Batch
compute
Pushing
Ingestion Result Store
Query
Engine
Computed
Information
Raw Data
(Reservoir)
= Data in Motion = Data at Rest
Pulling
Ingestion
Channel
?
?
13 Architektur von Big Data Lösungen
14. Traditional Architecture for Big Data
• Batch Processing - “Data at Rest”
• Not for low latency use cases
• Responses are delivered “after the fact”
• Maximum value of the identified situation is lost
• Decision are made on old and stale data
• Spark Core is a faster alternative to Hadoop Map
Reduce, but still Batch Processing
• Spark Ecosystems offers a lot of additional
advanced analytic capabilities (machine learning,
graph processing, …)
14 Architektur von Big Data Lösungen
15. Big Data Reference Architectures –
Event/Stream Processing
15 Architektur von Big Data Lösungen
16. Event / Stream Processing – “Data in Motion”
“Data in motion”
Events are analyzed and processed in real-
time as the arrive
Decisions are timely, contextual and based
on fresh data
Decision latency is eliminated
16 Architektur von Big Data Lösungen
17. Event / Stream Processing Architecture
Data
Ingestion
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
Logfiles
Social
RDBMS
ERP
Sensor
Machine
(Analytical) Real-Time Data Processing
Stream/Event Processing
Messaging
Result Store
= Data in Motion = Data at Rest
17 Architektur von Big Data Lösungen
18. Challenges for Ingesting Data
Multitude of sensors
Real-Time Streaming
Multiple Firmware versions
Bad Data from damaged sensors
Regulatory Constraints
Data Quality
18 Architektur von Big Data Lösungen
19. Continuous Data Ingestion
DB Source
Big Data
Log
Stream
Processing
IoT Sensor
Event Hub
Topic
Topic
REST
Topic
IoT GW
CDC GW
Connect
CDC
DB Source
Log CDC
Native
IoT Sensor
IoT Sensor
19
Dataflow GW
Topic
Topic
Queue
Message GW
Topic
Dataflow GW
Dataflow
TopicREST
19
File Source
Log
Log
Log
Social
Native
Topic
Topic
19 Architektur von Big Data Lösungen
20. Continuous Data Ingestion
DB Source
Big Data
Log
Stream
Processing
IoT Sensor
Event Hub
Topic
Topic
REST
Topic
IoT GW
CDC GW
Connect
CDC
DB Source
Log CDC
Native
IoT Sensor
IoT Sensor
20
Dataflow GW
Topic
Topic
Queue
Message GW
Topic
Dataflow GW
Dataflow
TopicREST
20
File Source
Log
Log
Log
Social
Native
Topic
Topic
20 Architektur von Big Data Lösungen
21. Data
Ingestion
(Analytical) Real-Time Data Processing
Event / Stream Processing Architecture – Open Source
Technology Mapping
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
Logfiles
Social
RDBMS
ERP
Sensor
Machine
Stream/Event Processing
Messaging
Result Store
= Data in Motion = Data at Rest
22 Architektur von Big Data Lösungen
22. Data
Ingestion
(Analytical) Real-Time Data Processing
Event / Stream Processing Architecture – Oracle
Technology Mapping
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
Logfiles
Social
RDBMS
ERP
Sensor
Machine
Stream/Event Processing
Messaging
Result Store
= Data in Motion = Data at Rest
23 Architektur von Big Data Lösungen
23. Event / Stream Processing Architecture
The solution for low latency use cases
Process each event separately => low latency
Process events in micro-batches => increases latency but offers better
reliability
Previously known as “Complex Event Processing”
Keep the data moving / Data in Motion instead of Data at Rest => raw events
were not stored
24 Architektur von Big Data Lösungen
24. Event / Stream Processing Architecture - Keep raw
event data
Data
Ingestion
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
Logfiles
Social
RDBMS
ERP
Sensor
Machine
(Analytical) Real-Time Data Processing
Stream/Event Processing
Messaging
Result Store
(Analytical) Batch Data Processing
Raw Data
(Reservoir)
= Data in Motion = Data at Rest
25 Architektur von Big Data Lösungen
25. Big Data Reference Architectures -
Lambda Architecture for Big Data
26 Architektur von Big Data Lösungen
26. “Lambda Architecture” for Big Data
Data
Ingestion
(Analytical) Batch Data Processing
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
(Analytical) Real-Time Data Processing
Stream/Event Processing
Batch
compute
Messaging
Result Store
Query
Engine
Result Store
Computed
Information
Raw Data
(Reservoir)
= Data in Motion = Data at Rest
Pulling
Ingestion
27 Architektur von Big Data Lösungen
27. Lambda Architecture for Big Data
Combines (Big) Data at Rest with (Fast) Data in Motion
Closes the gap from high-latency batch processing
Keeps the raw information forever
Makes it possible to rerun analytics operations on whole data set if necessary
=> because the old run had an error or
=> because we have found a better algorithm we want to apply
Have to implement functionality twice
• Once for batch
• Once for real-time streaming
29 Architektur von Big Data Lösungen
28. Big Data Reference Architectures -
„Kappa“ Architecture
30 Architektur von Big Data Lösungen
29. “Kappa Architecture” for Big Data
Data
Ingestion
“Raw Data Reservoir”
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
(Analytical) Real-Time Data Processing
Stream/Event Processing
Messaging
Result Store
Raw Data
(Reservoir)
Computed
Information
= Data in Motion = Data at Rest
31 Architektur von Big Data Lösungen
Queryable State
30. Organizing NoSQL Data Stores – Different Types
Key Value Store
Wide-column store
Document store
Graph store
Key Value
K1 V1
K2 V2
K3 V3
Document
{
k1: v1,
k2: v2,
k3: [v1, v2, v3]
}
Rowkey
CK1
RK1
V1
CK2
V2
CK3
V3
CK4
V4
…
…
CK1
RK2
V1
CK4
V4
CK6
V6
…
…
…
…
…
…
CK3
V3
32 Architektur von Big Data Lösungen
31. Organizing NoSQL Data Stores – and the Products
Key Value Store
Wide-column store
Document store
Graph store
33 Architektur von Big Data Lösungen
32. Big Data Reference Architectures -
„Unified“ Architecture
34 Architektur von Big Data Lösungen
33. “Unified Architecture” for Big Data
Data
Ingestion
(Analytical) Batch Data Processing (Calculate Models of incoming data)
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
(Analytical) Real-Time Data Processing
Stream/Event Processing
Batch
compute
Messaging
Result Store
Result Store
Computed
Information
Raw Data
(Reservoir)
= Data in Motion = Data at Rest
Prediction
Models
35 Architektur von Big Data Lösungen
Queryable State
35. MicroserviceMicroservice
MicroserviceMicroservice
Event-Driven (Micro-) Services Architecture
Data
Ingestion
“Raw Data Reservoir”
Batch
compute
Data
Sources
Channel
Data
Consumer
Reports
Service
Analytic
Tools
Alerting
Tools
Content
RDBMS
Social
ERP
Logfiles
Sensor
Machine
Microservice 2
Service
Raw Data
(Reservoir)
Computed
Information
= Data in Motion = Data at Rest
37 Architektur von Big Data Lösungen
State
Batch
compute
Microservice 1
Service State
API
Result Store
36. Big Data Ecosystem – many
choices sorted!
38 Architektur von Big Data Lösungen
37. Building Blocks for (Big) Data Processing
Data
Acquisition
Format
File System
Stream Processing
Batch SQL
Graph DBMS
Document
DBMS
Relational
DBMS
Visualization
IoT
Messaging
Analytics
OLAP DBMS
Query
Federation
Table-Style
DBMS
Key Value
DBMS
Batch Processing
In-Memory
39 Architektur von Big Data Lösungen
38. Big Data Ecosystem – many choices sorted!
40 Architektur von Big Data Lösungen