Introduction to Apache Apex - The next generation native Hadoop platform. This talk will cover details about how Apache Apex can be used as a powerful and versatile platform for big data processing. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch alerts, real-time actions, threat detection, etc.
Bio:
Pramod Immaneni is Apache Apex PMC member and senior architect at DataTorrent, where he works on Apache Apex and specializes in big data platform and applications. Prior to DataTorrent, he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs.
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
Stream data processing is becoming increasingly important to support business needs for faster time to insight and action with growing volume of information from more sources. Apache Apex (http://apex.apache.org/) is a unified big data in motion processing platform for the Apache Hadoop ecosystem. Apex supports demanding use cases with:
* Architecture for high throughput, low latency and exactly-once processing semantics.
* Comprehensive library of building blocks including connectors for Kafka, Files, Cassandra, HBase and many more
* Java based with unobtrusive API to build real-time and batch applications and implement custom business logic.
* Advanced engine features for auto-scaling, dynamic changes, compute locality.
Apex was developed since 2012 and is used in production in various industries like online advertising, Internet of Things (IoT) and financial services.
Presenter - Siyuan Hua, Apache Apex PMC Member & DataTorrent Engineer
Apache Apex provides a DAG construction API that gives the developers full control over the logical plan. Some use cases don't require all of that flexibility, at least so it may appear initially. Also a large part of the audience may be more familiar with an API that exhibits more functional programming flavor, such as the new Java 8 Stream interfaces and the Apache Flink and Spark-Streaming API. Thus, to make Apex beginners to get simple first app running with familiar API, we are now providing the Stream API on top of the existing DAG API. The Stream API is designed to be easy to use yet flexible to extend and compatible with the native Apex API. This means, developers can construct their application in a way similar to Flink, Spark but also have the power to fine tune the DAG at will. Per our roadmap, the Stream API will closely follow Apache Beam (aka Google Data Flow) model. In the future, you should be able to either easily run Beam applications with the Apex Engine or express an existing application in a more declarative style.
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex
Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.
Ingesting Data from Kafka to JDBC with Transformation and EnrichmentApache Apex
Presenter - Dr Sandeep Deshmukh, Committer Apache Apex, DataTorrent engineer
Abstract:
Ingesting and extracting data from Hadoop can be a frustrating, time consuming activity for many enterprises. Apache Apex Data Ingestion is a standalone big data application that simplifies the collection, aggregation and movement of large amounts of data to and from Hadoop for a more efficient data processing pipeline. Apache Apex Data Ingestion makes configuring and running Hadoop data ingestion and data extraction a point and click process enabling a smooth, easy path to your Hadoop-based big data project.
In this series of talks, we would cover how Hadoop Ingestion is made easy using Apache Apex. The third talk in this series would focus on ingesting unbounded data from Kafka to JDBC with couple of processing operators -Transform and enrichment.
Intro to Apache Apex (next gen Hadoop) & comparison to Spark StreamingApache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
Apache Apex is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data.
Apache Apex is a native Hadoop data-in-motion platform. We will discuss architectural differences between Apache Apex features with Spark Streaming. We will discuss how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion.
We will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. We will also discuss how these features affect time to market and total cost of ownership.
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
Stream data processing is becoming increasingly important to support business needs for faster time to insight and action with growing volume of information from more sources. Apache Apex (http://apex.apache.org/) is a unified big data in motion processing platform for the Apache Hadoop ecosystem. Apex supports demanding use cases with:
* Architecture for high throughput, low latency and exactly-once processing semantics.
* Comprehensive library of building blocks including connectors for Kafka, Files, Cassandra, HBase and many more
* Java based with unobtrusive API to build real-time and batch applications and implement custom business logic.
* Advanced engine features for auto-scaling, dynamic changes, compute locality.
Apex was developed since 2012 and is used in production in various industries like online advertising, Internet of Things (IoT) and financial services.
Presenter - Siyuan Hua, Apache Apex PMC Member & DataTorrent Engineer
Apache Apex provides a DAG construction API that gives the developers full control over the logical plan. Some use cases don't require all of that flexibility, at least so it may appear initially. Also a large part of the audience may be more familiar with an API that exhibits more functional programming flavor, such as the new Java 8 Stream interfaces and the Apache Flink and Spark-Streaming API. Thus, to make Apex beginners to get simple first app running with familiar API, we are now providing the Stream API on top of the existing DAG API. The Stream API is designed to be easy to use yet flexible to extend and compatible with the native Apex API. This means, developers can construct their application in a way similar to Flink, Spark but also have the power to fine tune the DAG at will. Per our roadmap, the Stream API will closely follow Apache Beam (aka Google Data Flow) model. In the future, you should be able to either easily run Beam applications with the Apex Engine or express an existing application in a more declarative style.
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex
Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.
Ingesting Data from Kafka to JDBC with Transformation and EnrichmentApache Apex
Presenter - Dr Sandeep Deshmukh, Committer Apache Apex, DataTorrent engineer
Abstract:
Ingesting and extracting data from Hadoop can be a frustrating, time consuming activity for many enterprises. Apache Apex Data Ingestion is a standalone big data application that simplifies the collection, aggregation and movement of large amounts of data to and from Hadoop for a more efficient data processing pipeline. Apache Apex Data Ingestion makes configuring and running Hadoop data ingestion and data extraction a point and click process enabling a smooth, easy path to your Hadoop-based big data project.
In this series of talks, we would cover how Hadoop Ingestion is made easy using Apache Apex. The third talk in this series would focus on ingesting unbounded data from Kafka to JDBC with couple of processing operators -Transform and enrichment.
Intro to Apache Apex (next gen Hadoop) & comparison to Spark StreamingApache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
Apache Apex is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data.
Apache Apex is a native Hadoop data-in-motion platform. We will discuss architectural differences between Apache Apex features with Spark Streaming. We will discuss how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion.
We will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. We will also discuss how these features affect time to market and total cost of ownership.
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data TransformationsApache Apex
Presenter:
Chaitanya Chebolu, Committer for Apache Apex and Software Engineer at DataTorrent.
In this session we will cover the use-case of ingesting data from Kafka and writing to HDFS with a couple of processing operators - Parser, Dedup, Transform.
Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
Architectual Comparison of Apache Apex and Spark StreamingApache Apex
This presentation discusses architectural differences between Apache Apex features with Spark Streaming. It discusses how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion.
Also, it will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. Further, it will discuss how these features affect time to market and total cost of ownership.
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareApache Apex
The presentation covers how Apache Apex is used to deliver actionable insights in real-time for Ad-tech. It includes a reference architecture to provide dimensional aggregates on TB scale for billions of events per day. The reference architecture covers concepts around Apache Apex, with Kafka as source and dimensional compute. Slides from Devendra Tagare at Apache Big Data North America in Miami 2017.
David Yan offers an overview of Apache Apex, a stream processing engine used in production by several large companies for real-time data analytics.
Apache Apex uses a programming paradigm based on a directed acyclic graph (DAG). Each node in the DAG represents an operator, which can be data input, data output, or data transformation. Each directed edge in the DAG represents a stream, which is the flow of data from one operator to another.
As part of Apex, the Malhar library provides a suite of connector operators so that Apex applications can read from or write to various data sources. It also includes utility operators that are commonly used in streaming applications, such as parsers, deduplicators and join, and generic building blocks that facilitate scalable state management and checkpointing.
In addition to processing based on ingression time and processing time, Apex supports event-time windows and session windows. It also supports windowing, watermarks, allowed lateness, accumulation mode, triggering, and retraction detailed by Apache Beam as well as feedback loops in the DAG for iterative processing and at-least-once and “end-to-end” exactly-once processing guarantees. Apex provides various ways to fine-tune applications, such as operator partitioning, locality, and affinity.
Apex is integrated with several open source projects, including Apache Beam, Apache Samoa (distributed machine learning), and Apache Calcite (SQL-based application specification). Users can choose Apex as the backend engine when running their application model based on these projects.
David explains how to develop fault-tolerant streaming applications with low latency and high throughput using Apex, presenting the programming model with examples and demonstrating how custom business logic can be integrated using both the declarative high-level API and the compositional DAG-level API.
Apache Apex: Stream Processing Architecture and ApplicationsThomas Weise
Slides from http://www.meetup.com/Hadoop-User-Group-Munich/events/230313355/
This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
Apache Big Data 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
Apache Apex is a next gen big data analytics platform. Originally developed at DataTorrent it comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn about the Apex architecture, including its unique features for scalability, fault tolerance and processing guarantees, programming model and use cases.
http://apachebigdata2016.sched.org/event/6M0L/next-gen-big-data-analytics-with-apache-apex-thomas-weise-datatorrent
Ingestion and Dimensions Compute and Enrich using Apache ApexApache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
This talk will be a deep dive into ingesting unbounded file data and streaming data from Kafka into Hadoop. We will also cover data enrichment and dimensional compute. Customer use-case and reference architecture.
Intro to YARN (Hadoop 2.0) & Apex as YARN App (Next Gen Big Data)Apache Apex
Presenter:
Priyanka Gugale, Committer for Apache Apex and Software Engineer at DataTorrent.
In this session we will cover introduction to Yarn, understanding yarn architecture as well as look into Yarn application lifecycle. We will also learn how Apache Apex is one of the Yarn applications in Hadoop.
Smart Partitioning with Apache Apex (Webinar)Apache Apex
Processing big data often requires running the same computations parallelly in multiple processes or threads, called partitions, with each partition handling a subset of the data. This becomes all the more necessary when processing live data streams where maintaining SLA is paramount. Furthermore, multiple different computations make up an application and each of them may have different partitioning needs. Partitioning also needs to adapt to changing data rates, input sources and other application requirements like SLA.
In this talk, we will introduce how Apache Apex, a distributed stream processing platform on Hadoop, handles partitioning. We will look at different partitioning schemes provided by Apex some of which are unique in this space. We will also look at how Apex does dynamic partitioning, a feature unique to and pioneered by Apex to handle varying data needs with examples. We will also talk about the different utilities and libraries that Apex provides for users to be able to affect their own custom partitioning.
Introduction to Apache Apex and writing a big data streaming application Apache Apex
Introduction to Apache Apex - The next generation native Hadoop platform, and writing a native Hadoop big data Apache Apex streaming application.
This talk will cover details about how Apex can be used as a powerful and versatile platform for big data. Apache apex is being used in production by customers for both streaming and batch use cases. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch. alerts, real-time actions, threat detection, etc.
Presenter : <b>Pramod Immaneni</b> Apache Apex PPMC member and senior architect at DataTorrent Inc, where he works on Apex and specializes in big data applications. Prior to DataTorrent he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs. Before that he was a technical co-founder of a mobile startup where he was an architect of a dynamic content rendering engine for mobile devices.
This is a video of the webcast of an Apache Apex meetup event organized by Guru Virtues at 267 Boston Rd no. 9, North Billerica, MA, on <b>May 7th 2016</b> and broadcasted from San Jose, CA. If you are interested in helping organize i.e., hosting, presenting, community leadership Apache Apex community, please email apex-meetup@datatorrent.com
Low Latency Polyglot Model Scoring using Apache ApexApache Apex
Data science is fast becoming a complementary approach and process to solve business challenges today. The explosion of frameworks to help data scientists build models bears a testimony to this. However when a model needs to be turned into a production version in very low latency and enterprise grade environments, there are a very few choices with each one having their own strengths and weaknesses. Adding to this is the current disconnect between a data scientists world which is all about modelling and an engineers world which is about SLAs and service guarantees. A framework like Apache Apex can complement each of these roles and provide constructs for both these worlds. This would help enterprises to drastically cut down the cost of model deployment to production environments.
Apache Apex (incubating) is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data.
Presented by Pramod Immaneni at Data Riders Meetup hosted by Nexient on Apr 5th, 2016
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data TransformationsApache Apex
Presenter:
Chaitanya Chebolu, Committer for Apache Apex and Software Engineer at DataTorrent.
In this session we will cover the use-case of ingesting data from Kafka and writing to HDFS with a couple of processing operators - Parser, Dedup, Transform.
Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
Architectual Comparison of Apache Apex and Spark StreamingApache Apex
This presentation discusses architectural differences between Apache Apex features with Spark Streaming. It discusses how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion.
Also, it will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. Further, it will discuss how these features affect time to market and total cost of ownership.
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareApache Apex
The presentation covers how Apache Apex is used to deliver actionable insights in real-time for Ad-tech. It includes a reference architecture to provide dimensional aggregates on TB scale for billions of events per day. The reference architecture covers concepts around Apache Apex, with Kafka as source and dimensional compute. Slides from Devendra Tagare at Apache Big Data North America in Miami 2017.
David Yan offers an overview of Apache Apex, a stream processing engine used in production by several large companies for real-time data analytics.
Apache Apex uses a programming paradigm based on a directed acyclic graph (DAG). Each node in the DAG represents an operator, which can be data input, data output, or data transformation. Each directed edge in the DAG represents a stream, which is the flow of data from one operator to another.
As part of Apex, the Malhar library provides a suite of connector operators so that Apex applications can read from or write to various data sources. It also includes utility operators that are commonly used in streaming applications, such as parsers, deduplicators and join, and generic building blocks that facilitate scalable state management and checkpointing.
In addition to processing based on ingression time and processing time, Apex supports event-time windows and session windows. It also supports windowing, watermarks, allowed lateness, accumulation mode, triggering, and retraction detailed by Apache Beam as well as feedback loops in the DAG for iterative processing and at-least-once and “end-to-end” exactly-once processing guarantees. Apex provides various ways to fine-tune applications, such as operator partitioning, locality, and affinity.
Apex is integrated with several open source projects, including Apache Beam, Apache Samoa (distributed machine learning), and Apache Calcite (SQL-based application specification). Users can choose Apex as the backend engine when running their application model based on these projects.
David explains how to develop fault-tolerant streaming applications with low latency and high throughput using Apex, presenting the programming model with examples and demonstrating how custom business logic can be integrated using both the declarative high-level API and the compositional DAG-level API.
Apache Apex: Stream Processing Architecture and ApplicationsThomas Weise
Slides from http://www.meetup.com/Hadoop-User-Group-Munich/events/230313355/
This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
Apache Big Data 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
Apache Apex is a next gen big data analytics platform. Originally developed at DataTorrent it comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn about the Apex architecture, including its unique features for scalability, fault tolerance and processing guarantees, programming model and use cases.
http://apachebigdata2016.sched.org/event/6M0L/next-gen-big-data-analytics-with-apache-apex-thomas-weise-datatorrent
Ingestion and Dimensions Compute and Enrich using Apache ApexApache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
This talk will be a deep dive into ingesting unbounded file data and streaming data from Kafka into Hadoop. We will also cover data enrichment and dimensional compute. Customer use-case and reference architecture.
Intro to YARN (Hadoop 2.0) & Apex as YARN App (Next Gen Big Data)Apache Apex
Presenter:
Priyanka Gugale, Committer for Apache Apex and Software Engineer at DataTorrent.
In this session we will cover introduction to Yarn, understanding yarn architecture as well as look into Yarn application lifecycle. We will also learn how Apache Apex is one of the Yarn applications in Hadoop.
Smart Partitioning with Apache Apex (Webinar)Apache Apex
Processing big data often requires running the same computations parallelly in multiple processes or threads, called partitions, with each partition handling a subset of the data. This becomes all the more necessary when processing live data streams where maintaining SLA is paramount. Furthermore, multiple different computations make up an application and each of them may have different partitioning needs. Partitioning also needs to adapt to changing data rates, input sources and other application requirements like SLA.
In this talk, we will introduce how Apache Apex, a distributed stream processing platform on Hadoop, handles partitioning. We will look at different partitioning schemes provided by Apex some of which are unique in this space. We will also look at how Apex does dynamic partitioning, a feature unique to and pioneered by Apex to handle varying data needs with examples. We will also talk about the different utilities and libraries that Apex provides for users to be able to affect their own custom partitioning.
Introduction to Apache Apex and writing a big data streaming application Apache Apex
Introduction to Apache Apex - The next generation native Hadoop platform, and writing a native Hadoop big data Apache Apex streaming application.
This talk will cover details about how Apex can be used as a powerful and versatile platform for big data. Apache apex is being used in production by customers for both streaming and batch use cases. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch. alerts, real-time actions, threat detection, etc.
Presenter : <b>Pramod Immaneni</b> Apache Apex PPMC member and senior architect at DataTorrent Inc, where he works on Apex and specializes in big data applications. Prior to DataTorrent he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs. Before that he was a technical co-founder of a mobile startup where he was an architect of a dynamic content rendering engine for mobile devices.
This is a video of the webcast of an Apache Apex meetup event organized by Guru Virtues at 267 Boston Rd no. 9, North Billerica, MA, on <b>May 7th 2016</b> and broadcasted from San Jose, CA. If you are interested in helping organize i.e., hosting, presenting, community leadership Apache Apex community, please email apex-meetup@datatorrent.com
Low Latency Polyglot Model Scoring using Apache ApexApache Apex
Data science is fast becoming a complementary approach and process to solve business challenges today. The explosion of frameworks to help data scientists build models bears a testimony to this. However when a model needs to be turned into a production version in very low latency and enterprise grade environments, there are a very few choices with each one having their own strengths and weaknesses. Adding to this is the current disconnect between a data scientists world which is all about modelling and an engineers world which is about SLAs and service guarantees. A framework like Apache Apex can complement each of these roles and provide constructs for both these worlds. This would help enterprises to drastically cut down the cost of model deployment to production environments.
Apache Apex (incubating) is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data.
Presented by Pramod Immaneni at Data Riders Meetup hosted by Nexient on Apr 5th, 2016
Building Your First Apache Apex (Next Gen Big Data/Hadoop) ApplicationApache Apex
This webinar will be a hands-on demonstration of how to clone and build the Apache Apex source code repositories, how to run the maven archetype to create a new Apex project, how to enhance it to build a word counting application and finally, how to run it and view results. We will also do a brief code walkthrough.
Bio:
Dr. Munagala V. Ramanath is a Committer for Apache Apex and a Software Engineer at DataTorrent. He has many years experience working for a variety of companies in California and a Ph.D. in Computer Science from the University of Wisconsin, Madison.
Apache Apex Fault Tolerance and Processing SemanticsApache Apex
Components of an Apex application running on YARN, how they are made fault tolerant, how checkpointing works, recovery from failures, incremental recovery, processing guarantees.
In this session, we will talk about two of the most promising incubating open source Projects, Apache Apex & Apache Geode and how together they attempt to solve shortcomings of existing big data analytics platforms.
Project Apex is an enterprise grade native YARN big data-in-motion platform that unifies stream processing as well as batch processing. Apex processes big data-in-motion in a highly scalable, highly performant, fault-tolerant, stateful, secure, distributed, and an easily operable way.
Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing.
We will also look at some use cases where how these two projects can be used together to form distributed, fault tolerant, reliable In memory data processing layer.
Capital One's Next Generation Decision in less than 2 msApache Apex
Slide deck for Capital One's talk on using Apache Apex for their next generation decisioning platform, achieving an ultra-low latency of under 2 ms for decision making, and handling 2,000 events burst at a net rate of 70,000 events/sec.
Some examples and motivation for creating data structures from nothing but functions - Church Encoding! There's particular detail on how it can make free monads more efficient.
Recovery: Job Growth and Education Requirements Through 2020CEW Georgetown
Recovery: Job Growth and Education Requirements Through 2020: Projections of jobs and education requirements through 2020. This report shows where the jobs will be by education level, occupation and industry. Recovery 2020 is an update to our Help Wanted: Projections of Jobs and Education Requirements Through 2018.
3 hard facts shaping higher education thinking and behaviorGrant Thornton LLP
Expansion in tuition, enrollment, faculty, buildings, and everything else ― is fast becoming a thing of the past. Institutions will have to carefully pick initiatives, making clear choices about what to do and, most significantly, what not to do. Download 2016 State of higher education >> http://gt-us.co/1UbUF56
African Americans: College Majors and Earnings CEW Georgetown
While college access has increased among African Americans, they are overrepresented in majors that lead to low-paying jobs. In our new report, African Americans: College Majors and Earnings shows that African Americans are underrepresented in the number of college majors associated with the fastest growing, highest-paying occupations. Read the full report: http://bit.ly/20M28d1
The Online College Labor Market: Where the Jobs Are More than 80 percent of job openings for workers with a bachelor’s degree or higher are posted online. This report analyzes the demand for college talent in the job market by examining online job advertisements for college degree-holders by education, occupations, and industries.
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder, DataTorrent - ...Dataconomy Media
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder of DataTorrent presented "Streaming Analytics with Apache Apex" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.
Apache Apex: Stream Processing Architecture and Applications Comsysto Reply GmbH
• Architecture highlights: high throughput, low-latency, operability with stateful fault tolerance, strong processing guarantees, auto-scaling etc
• Application development model, unified approach for real-time and batch use cases
• Tools for ease of use, ease of operability and ease of management
• How customers use Apache Apex in production
BigDataSpain 2016: Introduction to Apache ApexThomas Weise
Apache Apex is an open source stream processing platform, built for large scale, high-throughput, low-latency, high availability and operability. With a unified architecture it can be used for real-time and batch processing. Apex is Java based and runs natively on Apache Hadoop YARN and HDFS.
We will discuss the key features of Apache Apex and architectural differences from similar platforms and how these differences affect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, low latency SLA, high throughput and large scale ingestion.
Apex APIs and libraries of operators and examples focus on developer productivity. We will present the programming model with examples and how custom business logic can be easily integrated based on the Apex operator API.
We will cover integration with connectors to sources/destinations (including Kafka, JMS, SQL, NoSQL, files etc.), scalability with advanced partitioning, fault tolerance and processing guarantees, computation and scheduling model, state management, windowing and dynamic changes. Attendees will also learn how these features affect time to market and total cost of ownership and how they are important in existing Apex production deployments.
https://www.bigdataspain.org/
IoT Ingestion & Analytics using Apache Apex - A Native Hadoop PlatformApache Apex
Internet of Things (IoT) devices are becoming more ubiquitous in consumer, business and industrial landscapes. They are being widely used in applications ranging from home automation to the industrial internet. They pose a unique challenge in terms of the volume of data they produce, and the velocity with which they produce it, and the variety of sources they need to handle. The challenge is to ingest and process this data at the speed at which it is being produced in a real-time and fault tolerant fashion. Apache Apex is an industrial grade, scalable and fault tolerant big data processing platform that runs natively on Hadoop. In this deck, you will see how Apex is being used in IoT applications and also see how the enterprise features such as dimensional analytics, real-time dashboards and monitoring play a key role.
Presented by Pramod Immaneni, Principal Architect at DataTorrent and PPMC member Apache Apex, on BrightTALK webinar on Apr 6th, 2016
Stream data from Apache Kafka for processing with Apache ApexApache Apex
Meetup presentation: How Apache Apex consumes from Kafka topics for real-time time processing and analytics. Learn about features of the Apex Kafka Connector, which is one of the most popular operators in the Apex Malhar operator library, and powers several production use cases. We explain the advanced features this operator provides for high throughput, low latency ingest and how it enables fault tolerant topologies with exactly once processing semantics.
GE IOT Predix Time Series & Data Ingestion Service using Apache Apex (Hadoop)Apache Apex
This presentation will introduce usage of Apache Apex for Time Series & Data Ingestion Service by General Electric Internet of things Predix platform. Apache Apex is a native Hadoop data in motion platform that is being used by customers for both streaming as well as batch processing. Common use cases include ingestion into Hadoop, streaming analytics, ETL, database off-loads, alerts and monitoring, machine model scoring, etc.
Abstract: Predix is an General Electric platform for Internet of Things. It helps users develop applications that connect industrial machines with people through data and analytics for better business outcomes. Predix offers a catalog of services that provide core capabilities required by industrial internet applications. We will deep dive into Predix Time Series and Data Ingestion services leveraging fast, scalable, highly performant, and fault tolerant capabilities of Apache Apex.
Speakers:
- Venkatesh Sivasubramanian, Sr Staff Software Engineer, GE Predix & Committer of Apache Apex
- Pramod Immaneni, PPMC member of Apache Apex, and DataTorrent Architect
February 2017 HUG: Exactly-once end-to-end processing with Apache ApexYahoo Developer Network
Apache Apex (http://apex.apache.org/) is a stream processing platform that helps organizations to build processing pipelines with fault tolerance and strong processing guarantees. It was built to support low processing latency, high throughput, scalability, interoperability, high availability and security. The platform comes with Malhar library - an extensive collection of processing operators and a wide range of input and output connectors for out-of-the-box integration with an existing infrastructure. In the talk I am going to describe how connectors together with the distributed checkpointing (a mechanism used by the Apex to support fault tolerance and high availability) provide exactly-once end-to-end processing guarantees.
Speakers:
Vlad Rozov is Apache Apex PMC member and back-end engineer at DataTorrent where he focuses on the buffer server, Apex platform network layer, benchmarks and optimizing the core components for low latency and high throughput. Prior to DataTorrent Vlad worked on distributed BI platform at Huawei and on multi-dimensional database (OLAP) at Hyperion Solutions and Oracle.
Data Stream Processing with Apache FlinkFabian Hueske
This talk is an introduction into Stream Processing with Apache Flink. I gave this talk at the Madrid Apache Flink Meetup at February 25th, 2016.
The talk discusses Flink's features, shows it's DataStream API and explains the benefits of Event-time stream processing. It gives an outlook on some features that will be added after the 1.0 release.
Real Time Insights for Advertising TechApache Apex
A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several Data centers across the world. In batch data processing, data is collected at different geographic locations and processed at regular intervals. This system brings delay of at least 1 hour before an event is accounted for.
The goal of having real time streaming was to provide publishers, Demand Side Platforms (DSP's) and agencies actionable insights in a few minutes from the time of event generation.
This Ad Tech company uses DataTorrent RTS powered by Apex for:
• Real time reporting
• Resource monitoring
• Real time learning
• Allocation engine
Tushar Gosavi from DataTorrent will take the audience through the architecture, custom operators developed, use cases for real time and the challenges involved in implementing streaming systems at scale where multiple data centers are in play.
Tushar is a Senior Engineer at DataTorrent and has worked in distributed systems and storage domains.
From Batch to Streaming ET(L) with Apache Apex at Berlin Buzzwords 2017Thomas Weise
https://berlinbuzzwords.de/17/session/batch-streaming-etl-apache-apex
Stream data processing is increasingly required to support business needs for faster actionable insight with growing volume of information from more sources. Apache Apex is a true stream processing framework for low-latency, high-throughput and reliable processing of complex analytics pipelines on clusters. Apex is designed for quick time-to-production, and is used in production by large companies for real-time and batch processing at scale.
This session will use an Apex production use case to walk through the incremental transition from a batch pipeline with hours of latency to an end-to-end streaming architecture with billions of events per day which are processed to deliver real-time analytical reports. The example is representative for many similar extract-transform-load (ETL) use cases with other data sets that can use a common library of building blocks. The transform (or analytics) piece of such pipelines varies in complexity and often involves business logic specific, custom components.
Topics include:
Pipeline functionality from event source through queryable state for real-time insights.
API for application development and development process.
Library of building blocks including connectors for sources and sinks such as Kafka, JMS, Cassandra, HBase, JDBC and how they enable end-to-end exactly-once results.
Stateful processing with event time windowing.
Fault tolerance with exactly-once result semantics, checkpointing, incremental recovery
Scalability and low-latency, high-throughput processing with advanced engine features for auto-scaling, dynamic changes, compute locality.
Recent project development and roadmap.
Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
Strata Singapore: GearpumpReal time DAG-Processing with Akka at ScaleSean Zhong
Gearpump is a Akka based realtime streaming engine, it use Actor to model everything. It has super performance and flexibility. It has performance of 18000000 messages/second and latency of 8ms on a cluster of 4 machines.
Stream data processing is increasingly required to support business needs for faster actionable insight with growing volume of information from more sources. Apache Apex is a true stream processing framework for low-latency, high-throughput and reliable processing of complex analytics pipelines on clusters. Apex is designed for quick time-to-production, and is used in production by large companies for real-time and batch processing at scale.
This session will use an Apex production use case to walk through the incremental transition from a batch pipeline with hours of latency to an end-to-end streaming architecture with billions of events per day which are processed to deliver real-time analytical reports. The example is representative for many similar extract-transform-load (ETL) use cases with other data sets that can use a common library of building blocks. The transform (or analytics) piece of such pipelines varies in complexity and often involves business logic specific, custom components.
Topics include:
* Pipeline functionality from event source through queryable state for real-time insights.
* API for application development and development process.
* Library of building blocks including connectors for sources and sinks such as Kafka, JMS, Cassandra, HBase, JDBC and how they enable end-to-end exactly-once results.
* Stateful processing with event time windowing.
* Fault tolerance with exactly-once result semantics, checkpointing, incremental recovery
* Scalability and low-latency, high-throughput processing with advanced engine features for auto-scaling, dynamic changes, compute locality.
* Who is using Apex in production, and roadmap.
Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
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Venez le découvrir lors de cette session ignite
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Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
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Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
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Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
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However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
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(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
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Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
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Top Features to Include in Your Winzo Clone App for Business Growth (4).pptx
Intro to Apache Apex - Next Gen Platform for Ingest and Transform
1. Intro to Apache Apex
Next Gen Hadoop Platform for Ingest and
Transform
Pramod Immaneni
Sep 10th 2016
2. Next Gen Stream Data Processing
• Data from variety of sources (IoT, Kafka, files, social media etc.)
• Unbounded, continuous data streams
ᵒ Batch can be processed as stream (but a stream is not a batch)
• (In-memory) Processing with temporal boundaries (windows)
• Stateful operations: Aggregation, Rules, … -> Analytics
• Results stored to variety of sinks or destinations
ᵒ Streaming application can also serve data with very low latency
2
Browser
Web Server
Kafka Input
(logs)
Decompress,
Parse, Filter
Dimensions
Aggregate Kafka
Logs
Kafka
3. Apache Apex
3
• In-memory, distributed stream processing
• Application logic broken into components called operators that run in a distributed fashion
across your cluster
• Natural programming model
• Unobtrusive Java API to express (custom) logic
• Maintain state and metrics in your member variables
• Scalable, high throughput, low latency
• Operators can be scaled up or down at runtime according to the load and SLA
• Dynamic scaling (elasticity), compute locality
• Fault tolerance & correctness
• Automatically recover from node outages without having to reprocess from beginning
• State is preserved, checkpointing, incremental recovery
• End-to-end exactly-once
• Operability
• System and application metrics, record/visualize data
• Dynamic changes
6. Application Development Model
6
A Stream is a sequence of data tuples
A typical Operator takes one or more input streams, performs computations & emits one or more output streams
• Each Operator is YOUR custom business logic in java, or built-in operator from our open source library
• Operator has many instances that run in parallel and each instance is single-threaded
Directed Acyclic Graph (DAG) is made up of operators and streams
Directed Acyclic Graph (DAG)
Output
Stream
Tupl
e
Tupl
e
er
Operator
er
Operator
er
Operator
er
Operator
er
Operator
er
Operator
9. Scalability
9
NxM PartitionsUnifier
0 1 2 3
Logical DAG
0 1 2
1
1 Unifier
1
20
Logical Diagram
Physical Diagram with operator 1 with 3 partitions
0
Unifier
1a
1b
1c
2a
2b
Unifier 3
Physical DAG with (1a, 1b, 1c) and (2a, 2b): No bottleneck
Unifier
Unifier0
1a
1b
1c
2a
2b
Unifier 3
Physical DAG with (1a, 1b, 1c) and (2a, 2b): Bottleneck on intermediate Unifier
10. Advanced Partitioning
10
0
1a
1b
2 3 4Unifier
Physical DAG
0 4
3a2a1a
1b 2b 3b
Unifier
Physical DAG with Parallel Partition
Parallel Partition
Container
uopr
uopr1
uopr2
uopr3
uopr4
uopr1
uopr2
uopr3
uopr4
dopr
dopr
doprunifier
unifier
unifier
unifier
Container
Container
NICNIC
NICNIC
NIC
Container
NIC
Logical Plan
Execution Plan, for N = 4; M = 1
Execution Plan, for N = 4; M = 1, K = 2 with cascading unifiers
Cascading Unifiers
0 1 2 3 4
Logical DAG
11. Dynamic Partitioning
11
• Partitioning change while application is running
ᵒ Change number of partitions at runtime based on stats
ᵒ Determine initial number of partitions dynamically
• Kafka operators scale according to number of kafka partitions
ᵒ Supports re-distribution of state when number of partitions change
ᵒ API for custom scaler or partitioner
2b
2c
3
2a
2d
1b
1a1a 2a
1b 2b
3
1a 2b
1b 2c 3b
2a
2d
3a
Unifiers not shown
12. Fault Tolerance
12
• Operator state is checkpointed to persistent store
ᵒ Automatically performed by engine, no additional coding needed
ᵒ Asynchronous and distributed
ᵒ In case of failure operators are restarted from checkpoint state
• Automatic detection and recovery of failed containers
ᵒ Heartbeat mechanism
ᵒ YARN process status notification
• Buffering to enable replay of data from recovered point
ᵒ Fast, incremental recovery, spike handling
• Application master state checkpointed
ᵒ Snapshot of physical (and logical) plan
ᵒ Execution layer change log
13. • In-memory PubSub
• Stores results emitted by operator until committed
• Handles backpressure / spillover to local disk
• Ordering, idempotency
Operator
1
Container 1
Buffer
Server
Node 1
Operator
2
Container 2
Node 2
Buffer Server
13
14. End-to-End Exactly Once
14
• Important when writing to external systems
• Data should not be duplicated or lost in the external system in case of
application failures
• Common external systems
ᵒ Databases
ᵒ Files
ᵒ Message queues
• Exactly-once = at-least-once + idempotency + consistent state
• Data duplication must be avoided when data is replayed from checkpoint
ᵒ Operators implement the logic dependent on the external system
ᵒ Platform provides checkpointing and repeatable windowing
15. Exactly Once - Files
15
File Data
Offset
• Operator saves file offset during
checkpoint
• File contents are flushed before
checkpoint to ensure there is no
pending data in buffer
• On recovery platform restores the file
offset value from checkpoint
• Operator truncates the file to the
offset
• Starts writing data again
• Ensures no data is duplicated or lost
Chk
16. Exactly Once - Databases
16
d11 d12 d13
d21 d22 d23
lwn1 lwn2 lwn3
op-id wn
chk wn wn+1
Lwn+11 Lwn+12 Lwn+13
op-id wn+1
Data Table
Meta Table
• Data in a window is written out in a single
transaction
• Window id is also written to a meta table
as part of the same transaction
• Operator reads the window id from meta
table on recovery
• Ignores data for windows less than the
recovered window id and writes new data
• Partial window data before failure will not
appear in data table as transaction was not
committed
• Assumes idempotency for replay
17. Ingestion Solution
17
• Application package with operators ready to use for ingestion
• Input and output connectors
• Kafka – Dynamically scalable with Kafka scale
• HDFS Block and file
• S3
• Databases with JDBC
- Postgres, Mysql
• Processing
• Deduper
• Parsing, Filtering & Transform
• Comes with pre-built pipelines
• Kafka to HDFS with Deduper
• HDFS sync between two clusters or S3 to HDFS
• Currently in beta
• If interested please contact info@datatorrent.com
25. Maximize Revenue w/ real-time insights
25
PubMatic is the leading marketing automation software company for publishers. Through real-time analytics,
yield management, and workflow automation, PubMatic enables publishers to make smarter inventory
decisions and improve revenue performance
Business Need Apex based Solution Client Outcome
• Ingest and analyze high volume clicks &
views in real-time to help customers
improve revenue
- 200K events/second data
flow
• Report critical metrics for campaign
monetization from auction and client
logs
- 22 TB/day data generated
• Handle ever increasing traffic with
efficient resource utilization
• Always-on ad network
• DataTorrent Enterprise platform,
powered by Apache Apex
• In-memory stream processing
• Comprehensive library of pre-built
operators including connectors
• Built-in fault tolerance
• Dynamically scalable
• Management UI & Data Visualization
console
• Helps PubMatic deliver ad performance
insights to publishers and advertisers in
real-time instead of 5+ hours
• Helps Publishers visualize campaign
performance and adjust ad inventory in
real-time to maximize their revenue
• Enables PubMatic reduce OPEX with
efficient compute resource utilization
• Built-in fault tolerance ensures
customers can always access ad
network
26. Industrial IoT applications
26
GE is dedicated to providing advanced IoT analytics solutions to thousands of customers who are using their
devices and sensors across different verticals. GE has built a sophisticated analytics platform, Predix, to help its
customers develop and execute Industrial IoT applications and gain real-time insights as well as actions.
Business Need Apex based Solution Client Outcome
• Ingest and analyze high-volume, high speed
data from thousands of devices, sensors
per customer in real-time without data loss
• Predictive analytics to reduce costly
maintenance and improve customer
service
• Unified monitoring of all connected sensors
and devices to minimize disruptions
• Fast application development cycle
• High scalability to meet changing business
and application workloads
• Ingestion application using DataTorrent
Enterprise platform
• Powered by Apache Apex
• In-memory stream processing
• Built-in fault tolerance
• Dynamic scalability
• Comprehensive library of pre-built
operators
• Management UI console
• Helps GE improve performance and lower
cost by enabling real-time Big Data
analytics
• Helps GE detect possible failures and
minimize unplanned downtimes with
centralized management & monitoring of
devices
• Enables faster innovation with short
application development cycle
• No data loss and 24x7 availability of
applications
• Helps GE adjust to scalability needs with
auto-scaling
27. Smart energy applications
27
Silver Spring Networks helps global utilities and cities connect, optimize, and manage smart energy and smart city
infrastructure. Silver Spring Networks receives data from over 22 million connected devices, conducts 2 million
remote operations per year
Business Need Apex based Solution Client Outcome
• Ingest high-volume, high speed data from
millions of devices & sensors in real-time
without data loss
• Make data accessible to applications
without delay to improve customer service
• Capture & analyze historical data to
understand & improve grid operations
• Reduce the cost, time, and pain of
integrating with 3rd party apps
• Centralized management of software &
operations
• DataTorrent Enterprise platform, powered
by Apache Apex
• In-memory stream processing
• Pre-built operator
• Built-in fault tolerance
• Dynamically scalable
• Management UI console
• Helps Silver Spring Networks ingest &
analyze data in real-time for effective load
management & customer service
• Helps Silver Spring Networks detect
possible failures and reduce outages with
centralized management & monitoring of
devices
• Enables fast application development for
faster time to market
• Helps Silver Spring Networks scale with
easy to partition operators
• Automatic recovery from failures
28. Resources for the use cases
28
• Pubmatic
• https://www.youtube.com/watch?v=JSXpgfQFcU8
• GE
• https://www.youtube.com/watch?v=hmaSkXhHNu0
• http://www.slideshare.net/ApacheApex/ge-iot-predix-time-series-data-ingestion-service-using-
apache-apex-hadoop
• SilverSpring Networks
• https://www.youtube.com/watch?v=8VORISKeSjI
• http://www.slideshare.net/ApacheApex/iot-big-data-ingestion-and-processing-in-hadoop-by-
silver-spring-networks
Partitioning & Scaling built-in
Operators can be dynamically scaled
Throughput, latency or any custom logic
Streams can be split in flexible ways
Tuple hashcode, tuple field or custom logic
Parallel partitioning for parallel pipelines
MxN partitioning for generic pipelines
Unifier concept for merging results from partitions
Helps in handling skew imbalance
Advanced Windowing support
Application window configurable per operator
Sliding window and tumbling window support
Checkpoint window control for fault recovery
Windowing does not introduce artificial latency
Stateful fault tolerance out of the box
Operators recover automatically from a precise point before failure
At least once
At most once
Exactly once at window boundaries
In-memory stream processing platform
Developed since 2012, ASF TLP since 04/2016
Unobtrusive Java API to express (custom) logic
Scale out, distributed, parallel
High throughput & low latency processing
Windowing (temporal boundary)
Reliability, fault tolerance, operability
Hadoop native
Compute locality, affinity
Dynamic updates, elasticity
Large amount of data to process, arrival at high velocity
Pipelining and partitioning
Backpressure
Partitioning
Run same logic in multiple processes or threads
Each partition processes a subset of the data
Apex supports partitioning out of the box
Different partitioning schemes
Unification
Static & Dynamic Partitioning
Separation of processing logic from scaling decisions
Partitioning decision (yes/no) by trigger (StatsListener)
Pluggable component, can use any system or custom metric
Externally driven partitioning example: KafkaInputOperator
Stateful!
Uses checkpointed state
Ability to transfer state from old to new partitions (partitioner, customizable)
Steps:
Call partitioner
Modify physical plan, rewrite checkpoints as needed
Undeploy old partitions from execution layer
Release/request container resources
Deploy new partitions (from rewritten checkpoint)
No loss of data (buffered)
Incremental operation, partitions that don’t change continue processing
API: Partitioner interface
Creating an application in Java provides a much greater flexibility as you can create and use the operators in the same place, two ways
Compositional API is low level API where you specify the individual operator implementations and connect them up in a DAG or a graph. Talk about example above.
Declarative API where you specify operations on data at a high level and the ApexStream API takes care of converting it into a DAG underneath. Talk about example above.
For next version we are working on support for Apache Beam and the different even time windowing options in the specification
This is the previous example modified with windowing support.
There is one global window as we are going to continue to accumulate the counts
We want the results every second and we want to keep the counts in state
With Apex you have the flexibility to create your own custom operators and be able to use it with both low level and high level API.
Here are two examples
A snaphot of our apex malhar library listing the commonly needed operators.
Talk about robust support for kafka, dynamic partitioning, 0.8 and 0.9 API support with offset management and idempotent recovery.
Operators for streaming and batch
When your application is running you would want to know what is going on with your application. That is where monitoring console comes in. Touch upon a few salient features
Gives you a detailed look into your application including all operators and their partitions.
Gives you performance statistics, resource usage and container information for each of the partitions
Helps development and operations by providing access to the individual logs right in the console and allowing users to search the logs or change log levels on the fly.
Also lets you record data in a stream live at any stage.
Talk about locality here quickly
By default operators are deployed in containers (processes) on different nodes across the Hadoop cluster
Locality options for streams
RACK_LOCAL: Data does not traverse network switches
NODE_LOCAL: Data transfer via loopback interface, frees up network bandwidth
CONTAINER_LOCAL: Data transfer via in memory queues between operators, does not require serialization
THREAD_LOCAL: Data passed through call stack, operators share thread
Host Locality
Operators can be deployed on specific hosts
New in 3.4.0: (Anti-)Affinity (APEXCORE-10)
Ability to express relative deployment without specifying a host
Use the built-in real-time dashboards and widgets in your application or connect to your own. This picture shows the variety of widgets we have.
Wanted to talk about some instances where Apache Apex and DataTorrent are being used in production today. These cases mentioned here, customers have talked about using Apache Apex openly and have presented them in meetups. If you want to know more in depth we have provided links to the those meetup resources at the end.
Pubmatic is in advertising space and provides real time analytics around ad impressions and clicks for publishers. They are using dimensional computation operators provided by datatorrent to slice and dice the data in different ways and provide the results of those analytics through real-time dashboards.
GE is the leader in Industrial IoT and has built a cloud platform called Predix to store and analyze machine data from all over the world. There are using datatorrent and apache apex for high speed ingestion and processing in a fault tolerant way.
Silver spring networks also in the IoT space provides technology to collect and analyze data from smart energy meters for utilities. They perform ingestion and analytics with datatorrent to detect failures in the systems in advance and reduce outages.