Learn best practices for building a real-time streaming data architecture on AWS with Spark Streaming, Amazon Kinesis, and Amazon Elastic MapReduce (EMR). Get a closer look at how to ingest streaming data scalably and durably from data producers like mobile devices, servers, and even web browsers, and design a stream processing application with minimal data duplication and exactly-once processing.
Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services
Customer Guest: Harry Koch, Solutions Architecture, Philips
An overview of Amazon Kinesis Firehose, Amazon Kinesis Analytics, and Amazon Kinesis Streams so you can quickly get started with real-time, streaming data.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story? This session will cover the Royal Bank of Canada’s (RBC) journey of moving away from traditional ETL batch processing with Teradata towards using the Hadoop ecosystem for ingesting data. One of the first systems to leverage this new approach was the Event Standardization Service (ESS). This service provides a centralized “client event” ingestion point for the bank’s internal systems through either a web service or text file daily batch feed. ESS allows down stream reporting applications and end users to query these centralized events.
We discuss the drivers and expected benefits of changing the existing event processing. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speakers
Darryl Sutton, T4G, Principal Consultant
Kenneth Poon, RBC, Director, Data Engineering
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Apache Kafka in the Transportation and LogisticsKai Wähner
Event Streaming with Apache Kafka in the Transportation and Logistics.
Track & Trace, Real-time Locating System, Customer 360, Open API, and more…
Examples include Swiss Post, SBB, Deutsche Bahn, Hermes, Migros, Here Technologies, Otonomo, Lyft, Uber, Free Now, Lufthansa, Air France, Singapore Airlines, Amadeus Group, and more.
An overview of Amazon Kinesis Firehose, Amazon Kinesis Analytics, and Amazon Kinesis Streams so you can quickly get started with real-time, streaming data.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story? This session will cover the Royal Bank of Canada’s (RBC) journey of moving away from traditional ETL batch processing with Teradata towards using the Hadoop ecosystem for ingesting data. One of the first systems to leverage this new approach was the Event Standardization Service (ESS). This service provides a centralized “client event” ingestion point for the bank’s internal systems through either a web service or text file daily batch feed. ESS allows down stream reporting applications and end users to query these centralized events.
We discuss the drivers and expected benefits of changing the existing event processing. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speakers
Darryl Sutton, T4G, Principal Consultant
Kenneth Poon, RBC, Director, Data Engineering
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Apache Kafka in the Transportation and LogisticsKai Wähner
Event Streaming with Apache Kafka in the Transportation and Logistics.
Track & Trace, Real-time Locating System, Customer 360, Open API, and more…
Examples include Swiss Post, SBB, Deutsche Bahn, Hermes, Migros, Here Technologies, Otonomo, Lyft, Uber, Free Now, Lufthansa, Air France, Singapore Airlines, Amadeus Group, and more.
Today’s organisations require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lake is a new and increasingly popular way to store all of your data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
In this webinar, you will discover how AWS gives you fast access to flexible and low-cost IT resources, so you can rapidly scale and build your data lake that can power any kind of analytics such as data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity and variety of data.
Learning Objectives:
• Discover how you can rapidly scale and build your data lake with AWS.
• Explore the key pillars behind a successful data lake implementation.
• Learn how to use the Amazon Simple Storage Service (S3) as the basis for your data lake.
• Learn about the new AWS services recently launched, Amazon Athena and Amazon Redshift Spectrum, that help customers directly query that data lake.
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
An Introduction to Confluent Cloud: Apache Kafka as a Serviceconfluent
Business breakout during Confluent’s streaming event in Munich, presented by Hans Jespersen, VP WW Systems Engineering at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Presented by: Matthew McClean, AWS Partner Solutions Architect, Amazon Web Services
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. Learn the differences between a event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB)Kai Wähner
Learn the differences between an event-driven streaming platform and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
Extract-Transform-Load (ETL) is still a widely-used pattern to move data between different systems via batch processing. Due to its challenges in today’s world where real time is the new standard, an Enterprise Service Bus (ESB) is used in many enterprises as integration backbone between any kind of microservice, legacy application or cloud service to move data via SOAP / REST Web Services or other technologies. Stream Processing is often added as its own component in the enterprise architecture for correlation of different events to implement contextual rules and stateful analytics. Using all these components introduces challenges and complexities in development and operations.
This session discusses how teams in different industries solve these challenges by building a native streaming platform from the ground up instead of using ETL and ESB tools in their architecture. This allows to build and deploy independent, mission-critical streaming real time application and microservices. The architecture leverages distributed processing and fault-tolerance with fast failover, no-downtime rolling deployments and the ability to reprocess events, so you can recalculate output when your code changes. Integration and Stream Processing are still key functionality but can be realized in real time natively instead of using additional ETL, ESB or Stream Processing tools.
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Mainframe Integration, Offloading and Replacement with Apache KafkaKai Wähner
Video recording of this presentation:
https://youtu.be/upWzamacOVQ
Blog post with more details:
https://www.kai-waehner.de/blog/2020/04/24/mainframe-offloading-replacement-apache-kafka-connect-ibm-db2-mq-cdc-cobol/
Mainframes are still hard at work, processing over 70 percent of the world’s most essential computing transactions every day. Very high cost, monolithic architectures, and missing experts are the key challenges for mainframe applications. Time to get more innovative, even with the mainframe!
Mainframe offloading with Apache Kafka and its ecosystem can be used to keep a more modern data store in real-time sync with the mainframe. At the same time, it is persisting the event data on the bus to enable microservices, and deliver the data to other systems such as data warehouses and search indexes.
But the final goal and ultimate vision are to replace the mainframe by new applications using modern and less costly technologies. Stand up to the dinosaur, but keep in mind that legacy migration is a journey! Kai will guide you to the next step of your company’s evolution!
You will learn:
- how to not only reduce operational expenses but provide a path for architecture modernization, agility and eventually mainframe replacement
- what steps some of Confluent’s customers already took, leveraging technologies like Change Data Capture (CDC) or MQ for mainframe offloading
- how an event streaming platform enables cost reduction, architecture modernization, and a combination of a mainframe with new technologies
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Deep Dive into Stateful Stream Processing in Structured Streaming with Tathag...Databricks
Stateful processing is one of the most challenging aspects of distributed, fault-tolerant stream processing. The DataFrame APIs in Structured Streaming make it easy for the developer to express their stateful logic, either implicitly (streaming aggregations) or explicitly (mapGroupsWithState). However, there are a number of moving parts under the hood which makes all the magic possible. In this talk, I will dive deep into different stateful operations (streaming aggregations, deduplication and joins) and how they work under the hood in the Structured Streaming engine.
Real-time analytics have traditionally been analyzed using batch processing in DWH/Hadoop environments. Common use cases use data lakes, data science, and machine learning (ML). Creating serverless data-driven architecture and serverless streaming solutions with services like Amazon Kinesis, AWS Lambda, and Amazon Athena can solve real-time ingestion, storage, and analytics challenges, and help you focus on application logic without managing infrastructure. Learn design patterns and best practices for serverless stream processing.
Kafka Connect: Real-time Data Integration at Scale with Apache Kafka, Ewen Ch...confluent
Many companies are adopting Apache Kafka to power their data pipelines, including LinkedIn, Netflix, and Airbnb. Kafka’s ability to handle high throughput real-time data makes it a perfect fit for solving the data integration problem, acting as the common buffer for all your data and bridging the gap between streaming and batch systems.
However, building a data pipeline around Kafka today can be challenging because it requires combining a wide variety of tools to collect data from disparate data systems. One tool streams updates from your database to Kafka, another imports logs, and yet another exports to HDFS. As a result, building a data pipeline can take significant engineering effort and has high operational overhead because all these different tools require ongoing monitoring and maintenance. Additionally, some of the tools are simply a poor fit for the job: the fragmented nature of the data integration tools ecosystem lead to creative but misguided solutions such as misusing stream processing frameworks for data integration purposes.
We describe the design and implementation of Kafka Connect, Kafka’s new tool for scalable, fault-tolerant data import and export. First we’ll discuss some existing tools in the space and why they fall short when applied to data integration at large scale. Next, we will explore Kafka Connect’s design and how it compares to systems with similar goals, discussing key design decisions that trade off between ease of use for connector developers, operational complexity, and reuse of existing connectors. Finally, we’ll discuss how standardizing on Kafka Connect can ultimately lead to simplifying your entire data pipeline, making ETL into your data warehouse and enabling stream processing applications as simple as adding another Kafka connector.
eventbrite_kafka_summit_event_logo_v3-035858-edited.png
Real-time event processing monitors the incoming data stream and initiates action based on detected events like fraud, error or performance degradation. These events are often used to issue alerts and notifications, take responsive action, or to populate a monitoring dashboard. In this session, we will walk through different use cases for event processing and demonstrate how to build a scalable pipeline for tracking IoT device status. AWS services to be covered include: AWS Lambda and the Kinesis Client Library (KCL).
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...Amazon Web Services
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
Today’s organisations require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lake is a new and increasingly popular way to store all of your data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
In this webinar, you will discover how AWS gives you fast access to flexible and low-cost IT resources, so you can rapidly scale and build your data lake that can power any kind of analytics such as data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity and variety of data.
Learning Objectives:
• Discover how you can rapidly scale and build your data lake with AWS.
• Explore the key pillars behind a successful data lake implementation.
• Learn how to use the Amazon Simple Storage Service (S3) as the basis for your data lake.
• Learn about the new AWS services recently launched, Amazon Athena and Amazon Redshift Spectrum, that help customers directly query that data lake.
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
An Introduction to Confluent Cloud: Apache Kafka as a Serviceconfluent
Business breakout during Confluent’s streaming event in Munich, presented by Hans Jespersen, VP WW Systems Engineering at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Presented by: Matthew McClean, AWS Partner Solutions Architect, Amazon Web Services
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. Learn the differences between a event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB)Kai Wähner
Learn the differences between an event-driven streaming platform and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
Extract-Transform-Load (ETL) is still a widely-used pattern to move data between different systems via batch processing. Due to its challenges in today’s world where real time is the new standard, an Enterprise Service Bus (ESB) is used in many enterprises as integration backbone between any kind of microservice, legacy application or cloud service to move data via SOAP / REST Web Services or other technologies. Stream Processing is often added as its own component in the enterprise architecture for correlation of different events to implement contextual rules and stateful analytics. Using all these components introduces challenges and complexities in development and operations.
This session discusses how teams in different industries solve these challenges by building a native streaming platform from the ground up instead of using ETL and ESB tools in their architecture. This allows to build and deploy independent, mission-critical streaming real time application and microservices. The architecture leverages distributed processing and fault-tolerance with fast failover, no-downtime rolling deployments and the ability to reprocess events, so you can recalculate output when your code changes. Integration and Stream Processing are still key functionality but can be realized in real time natively instead of using additional ETL, ESB or Stream Processing tools.
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Mainframe Integration, Offloading and Replacement with Apache KafkaKai Wähner
Video recording of this presentation:
https://youtu.be/upWzamacOVQ
Blog post with more details:
https://www.kai-waehner.de/blog/2020/04/24/mainframe-offloading-replacement-apache-kafka-connect-ibm-db2-mq-cdc-cobol/
Mainframes are still hard at work, processing over 70 percent of the world’s most essential computing transactions every day. Very high cost, monolithic architectures, and missing experts are the key challenges for mainframe applications. Time to get more innovative, even with the mainframe!
Mainframe offloading with Apache Kafka and its ecosystem can be used to keep a more modern data store in real-time sync with the mainframe. At the same time, it is persisting the event data on the bus to enable microservices, and deliver the data to other systems such as data warehouses and search indexes.
But the final goal and ultimate vision are to replace the mainframe by new applications using modern and less costly technologies. Stand up to the dinosaur, but keep in mind that legacy migration is a journey! Kai will guide you to the next step of your company’s evolution!
You will learn:
- how to not only reduce operational expenses but provide a path for architecture modernization, agility and eventually mainframe replacement
- what steps some of Confluent’s customers already took, leveraging technologies like Change Data Capture (CDC) or MQ for mainframe offloading
- how an event streaming platform enables cost reduction, architecture modernization, and a combination of a mainframe with new technologies
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Deep Dive into Stateful Stream Processing in Structured Streaming with Tathag...Databricks
Stateful processing is one of the most challenging aspects of distributed, fault-tolerant stream processing. The DataFrame APIs in Structured Streaming make it easy for the developer to express their stateful logic, either implicitly (streaming aggregations) or explicitly (mapGroupsWithState). However, there are a number of moving parts under the hood which makes all the magic possible. In this talk, I will dive deep into different stateful operations (streaming aggregations, deduplication and joins) and how they work under the hood in the Structured Streaming engine.
Real-time analytics have traditionally been analyzed using batch processing in DWH/Hadoop environments. Common use cases use data lakes, data science, and machine learning (ML). Creating serverless data-driven architecture and serverless streaming solutions with services like Amazon Kinesis, AWS Lambda, and Amazon Athena can solve real-time ingestion, storage, and analytics challenges, and help you focus on application logic without managing infrastructure. Learn design patterns and best practices for serverless stream processing.
Kafka Connect: Real-time Data Integration at Scale with Apache Kafka, Ewen Ch...confluent
Many companies are adopting Apache Kafka to power their data pipelines, including LinkedIn, Netflix, and Airbnb. Kafka’s ability to handle high throughput real-time data makes it a perfect fit for solving the data integration problem, acting as the common buffer for all your data and bridging the gap between streaming and batch systems.
However, building a data pipeline around Kafka today can be challenging because it requires combining a wide variety of tools to collect data from disparate data systems. One tool streams updates from your database to Kafka, another imports logs, and yet another exports to HDFS. As a result, building a data pipeline can take significant engineering effort and has high operational overhead because all these different tools require ongoing monitoring and maintenance. Additionally, some of the tools are simply a poor fit for the job: the fragmented nature of the data integration tools ecosystem lead to creative but misguided solutions such as misusing stream processing frameworks for data integration purposes.
We describe the design and implementation of Kafka Connect, Kafka’s new tool for scalable, fault-tolerant data import and export. First we’ll discuss some existing tools in the space and why they fall short when applied to data integration at large scale. Next, we will explore Kafka Connect’s design and how it compares to systems with similar goals, discussing key design decisions that trade off between ease of use for connector developers, operational complexity, and reuse of existing connectors. Finally, we’ll discuss how standardizing on Kafka Connect can ultimately lead to simplifying your entire data pipeline, making ETL into your data warehouse and enabling stream processing applications as simple as adding another Kafka connector.
eventbrite_kafka_summit_event_logo_v3-035858-edited.png
Real-time event processing monitors the incoming data stream and initiates action based on detected events like fraud, error or performance degradation. These events are often used to issue alerts and notifications, take responsive action, or to populate a monitoring dashboard. In this session, we will walk through different use cases for event processing and demonstrate how to build a scalable pipeline for tracking IoT device status. AWS services to be covered include: AWS Lambda and the Kinesis Client Library (KCL).
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...Amazon Web Services
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
(BDT403) Best Practices for Building Real-time Streaming Applications with Am...Amazon Web Services
Amazon Kinesis is a fully managed, cloud-based service for real-time data processing over large, distributed data streams. Customers who use Amazon Kinesis can continuously capture and process real-time data such as website clickstreams, financial transactions, social media feeds, IT logs, location-tracking events, and more. In this session, we first focus on building a scalable, durable streaming data ingest workflow, from data producers like mobile devices, servers, or even a web browser, using the right tool for the right job. Then, we cover code design that minimizes duplicates and achieves exactly-once processing semantics in your elastic stream-processing application, built with the Kinesis Client Library. Attend this session to learn best practices for building a real-time streaming data architecture with Amazon Kinesis, and get answers to technical questions frequently asked by those starting to process streaming events.
AWS re:Invent 2016: Beeswax: Building a Real-Time Streaming Data Platform on ...Amazon Web Services
Amazon Kinesis is a platform of services for building real-time, streaming data applications in the cloud. Customers can use Amazon Kinesis to collect, stream, and process real-time data such as website clickstreams, financial transactions, social media feeds, application logs, location-tracking events, and more. In this session, we first cover best practices for building an end-to-end streaming data applications using Amazon Kinesis. Next, Beeswax, which provides real-time Bidder as a Service for programmatic digital advertising, will talk about how they built a feature-rich, real-time streaming data solution on AWS using Amazon Kinesis, Amazon Redshift, Amazon S3, Amazon EMR, and Apache Spark. Beeswax will discuss key components of their solution including scalable data capture, messaging hub for archival, data warehousing, near real-time analytics, and real-time alerting.
(GAM301) Real-Time Game Analytics with Amazon Kinesis, Amazon Redshift, and A...Amazon Web Services
Success in free-to-play gaming requires knowing what your players love most. The faster you can respond to players' behavior, the better your chances of success. Learn how mobile game company GREE, with over 150 million users worldwide, built a real-time analytics pipeline for their games using Amazon Kinesis, Amazon Redshift, and Amazon DynamoDB. They walk through their analytics architecture, the choices they made, the challenges they overcame, and the benefits they gained. Also hear how GREE migrated to the new system while keeping their games running and collecting metrics.
Serverless architectures can eliminate the need to provision and manage servers required to process files or streaming data in real time. In this session, we will cover the fundamentals of using AWS Lambda to process data from sources such as Amazon DynamoDB Streams, Amazon Kinesis, and Amazon S3. We will walk through sample use cases for real-time data processing and discuss best practices on using these services together. We will then demonstrate run a live demonstration on how to set up a real-time stream processing solution using just Amazon Kinesis and AWS Lambda, all without the need to run or manage servers.
Learning Objectives:
• Learn the fundamentals of using AWS Lambda with various AWS data sources
• Understand best practices of using AWS Lambda with Amazon Kinesis
Who Should Attend:
• Developers
NASA LandSat data can be stored, transformed, navigated, and visualized. In this session we will explore how the LandSat dataset is stored in Amazon Simple Storage Service (S3), one of the recommended cloud storage services in AWS for storage of petabytes of data, and how data stored in S3 can be processed on the server with the Lambda service, visualized for users, and made available to search engines.
Create by: Ben Snively, Senior Solutions Architect
Real-Time Bidding (RTB) is a service offered by advertising networks to agencies. The agencies decide on the value of advertising opportunities in real-time and bid accordingly on behalf of their advertising clients. Typically the window of opportunity for bids to be calculated from provided consumer details (e.g. cookies) and then submitted is 100ms.
The demo shows how to identify potential campaign memebers for a targeted marketing campaign. SFDC stores general information about customers/leads, and information about how they responded to previous campaigns. Those dataset are fed to AWS Machine Learning for prediction
2014년 5월 28일 일본에서 진행된 AWS 기술 웨비나의 발표 자료를 한국의 정윤진 솔루션스 아키텍트가 한글로 번역한 자료입니다. 웨비나 당시와 현재의 내용이 상이한 부분이 있을 수 있으니 자료 열람에 이 점 참고하시기 바라며, 혹 내용에 대한 문의사항이 있으신 경우 info-kr@amazon.com으로 연락 부탁드리겠습니다.
MySQL Cluster is a proven technology that today is successfully servicing the most performance-intensive workloads. MySQL Cluster is deployed across telecom networks and is powering mission-critical web applications.
Without trading off use of commodity hardware, transactional consistency and use of complex queries, MySQL Cluster provides:
- Web Scalability (web-scale performance on both reads and writes)
- Carrier Grade Availability (99.999%)
- Developer Agility (freedom to use SQL or NoSQL access methods)
MySQL Cluster is recommended in the situations where:
- It is crucial to reduce service downtime, because this produces a heavy impact on business
- Sharding the database to scale write performance highly impacts development of application (in MySQL Cluster the sharding is automatic and transparent to the application)
- There are real-time responses needs
- There are unpredictable scalability demands
- It is important to have data-access flexibility (SQL & NoSQL)
From a practical point of view the HOL's steps were:
- Installation of MySQL Cluster
- Start & Monitoring of MySQL Cluster
- Connecting to MySQL Cluster
- Overview of MySQL Cluster’s Admin Commands & Operations
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...PAPIs.io
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. In this talk we'll show how to use an AWS Spark cluster to train a model quickly from a laptop at a very little cost (around 10€).
Vincent Van Steenbergen is a freelance (big) data engineer who's working on a range of international projects, implementing systems able to handle terabytes of data, usually involving Spark, Scala, Kafka, Hadoop and Cassandra. His main interest right now is applying these techniques to solve machine learning problems. Vincent was previously a technical architect at Property. Works, a real estate startup in London and before that an R&D engineer at IDAaaS.
This presentation from the AWS Lab at Cloud Expo Europe 2014 explores large scale data analysis on AWS. The cost of data generation is falling. Storing, analyzing and sharing data using the tools that AWS offers a low cost and easy to use solution for creating value from your data assets.
(MBL310) Workshop: Build iOS Apps Using AWS Mobile Services | AWS re:Invent 2014Amazon Web Services
Learn how to build a powerful iOS app that leverages a variety of AWS services. In this three-hour, demo-heavy workshop, we show how you can build a modern native client app using Apple Swift and the AWS Mobile SDK that uses a number of cross-platform mobile cloud services directly with minimal code on the client. We share best practices for building a highly scalable backend so you can add your own functionality. This is a step-by-step journey where you configure and add components to your architecture, then modify and test your components inside a mobile location-based messaging app. In the end, you will have a mobile app with your own backend consisting of different AWS services including: Amazon Cognito, Amazon Mobile Analytics, Amazon SNS Push Notification, Amazon S3, Amazon CloudFront, Amazon CloudSearch, Amazon DynamoDB, Amazon SQS, and AWS Elastic Beanstalk.
AWS April 2016 Webinar Series - Getting Started with Real-Time Data Analytics...Amazon Web Services
It is becoming increasingly important to analyze real time streaming data. It allows organizations to remain competitive by uncovering relevant, actionable insights. AWS makes it easy to capture, store, and analyze real-time streaming data.
In this webinar, we will guide you through some of the proven architectures for processing streaming data, using a combination of tools including Amazon Kinesis Streams, AWS Lambda, and Spark Streaming on Amazon Elastic MapReduce (EMR). We will then talk about common use cases and best practices for real-time data analysis on AWS.
Learning Objectives:
Understand how you can analyze real-time data streams using Amazon Kinesis, AWS Lambda, and Spark running on Amazon EMR
Learn use cases and best practices for streaming data applications on AWS
Deep Dive and Best Practices for Real Time Streaming ApplicationsAmazon Web Services
Get answers to technical questions, frequently asked by those starting to work with streaming data. Learn best practices for building a real-time streaming data architecture on AWS with Amazon Kinesis, Spark Streaming, AWS Lambda, and Amazon EMR. First, we will focus on building a scalable, durable streaming data ingestion workflow from data producers like mobile devices, servers, or even web browsers. We will provide guidelines to minimize duplicates and achieve exactly-once processing semantics in your stream-processing applications. Then, we will show some of the proven architectures for processing streaming data using a combination of tools including Amazon Kinesis Stream, AWS Lambda, and Spark Streaming running on Amazon EMR.
Amazon Kinesis is the AWS service for real-time streaming big data ingestion and processing. This talk gives a detailed exploration of Kinesis stream processing. We'll discuss in detail techniques for building, and scaling Kinesis processing applications, including data filtration and transformation. Finally we'll address tips and techniques to emitting data into S3, DynamoDB, and Redshift.
Amazon Kinesis Platform – The Complete Overview - Pop-up Loft TLV 2017Amazon Web Services
Real-Time Streaming Analytics became popular amongst many verticals and use cases. In AdTech, Gaming, Financial Service and IoT, AWS customers are leveraging Amazon Kinesis platform to ingest billions of events every day and process them in real-time. In this session, we will discuss Amazon Kinesis Streams, Amazon Kinesis Firehose and Amazon Kinesis Analytics. We will show best practice and design patterns in integrating Amazon Kinesis platform with other services like Amazon EMR, Redshift, Amazon Elasticsearch and AWS lambda as well as 3rd party connectors like storm, Spark and more.
Amazon Kinesis provides services for you to work with streaming data on AWS. Learn how to load streaming data continuously and cost-effectively to Amazon S3 and Amazon Redshift using Amazon Kinesis Firehose without writing custom stream processing code. Get an introduction to building custom stream processing applications with Amazon Kinesis Streams for specialized needs.
Amazon Kinesis provides services for you to work with streaming data on AWS. Learn how to load streaming data continuously and cost-effectively to Amazon S3 and Amazon Redshift using Amazon Kinesis Firehose without writing custom stream processing code. Get an introduction to building custom stream processing applications with Amazon Kinesis Streams for specialised needs.
Presented at the AWS Summit in London, here's a deep dive on getting started with Amazon Kinesis and use-case with Jampp, the world's leading mobile app marketing platform.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we’ll review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
This session is recommended for anyone interested in understanding how to use AWS big data services to develop real-time analytics applications. In this session, you will get an overview of a number of Amazon's big data and analytics services that enable you to build highly scaleable cloud applications that immediately and continuously analyze large sets of distributed data. We'll explain how services like Amazon Kinesis, EMR and Redshift can be used for data ingestion, processing and storage to enable real-time insights and analysis into customer, operational and machine generated data and log files. We'll explore system requirements, design considerations, and walk through a specific customer use case to illustrate the power of real-time insights on their business.
Using real time big data analytics for competitive advantageAmazon Web Services
Many organisations find it challenging to successfully perform real-time data analytics using their own on premise IT infrastructure. Building a system that can adapt and scale rapidly to handle dramatic increases in transaction loads can potentially be quite a costly and time consuming exercise.
Most of the time, infrastructure is under-utilised and it’s near impossible for organisations to forecast the amount of computing power they will need in the future to serve their customers and suppliers.
To overcome these challenges, organisations can instead utilise the cloud to support their real-time data analytics activities. Scalable, agile and secure, cloud-based infrastructure enables organisations to quickly spin up infrastructure to support their data analytics projects exactly when it is needed. Importantly, they can ‘switch off’ infrastructure when it is not.
BluePi Consulting and Amazon Web Services (AWS) are giving you the opportunity to discover how organisations are using real time data analytics to gain new insights from their information to improve the customer experience and drive competitive advantage.
Introducing Amazon Kinesis: Real-time Processing of Streaming Big Data (BDT10...Amazon Web Services
"This presentation will introduce Kinesis, the new AWS service for real-time streaming big data ingestion and processing.
We’ll provide an overview of the key scenarios and business use cases suitable for real-time processing, and discuss how AWS designed Amazon Kinesis to help customers shift from a traditional batch-oriented processing of data to a continual real-time processing model. We’ll provide an overview of the key concepts, attributes, APIs and features of the service, and discuss building a Kinesis-enabled application for real-time processing. We’ll also contrast with other approaches for streaming data ingestion and processing. Finally, we’ll also discuss how Kinesis fits as part of a larger big data infrastructure on AWS, including S3, DynamoDB, EMR, and Redshift."
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...Amazon Web Services
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we will review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
Getting Started with Amazon Kinesis | AWS Public Sector Summit 2016Amazon Web Services
Amazon Kinesis provides services for you to work with streaming data on AWS. Learn how to load streaming data continuously and cost-effectively to Amazon S3 and Amazon Redshift using Amazon Kinesis Firehose without writing custom stream processing code. Get an introduction to building custom stream processing applications with Amazon Kinesis Streams for specialized needs.
Businesses are generating more data than ever before.
Doing real time data analytics requires IT infrastructure that often needs to be scaled up quickly and running an on-premise environment in this setting has its limitations.
Organisations often require a massive amount of IT resources to analyse their data and the upfront capital cost can deter them from embarking on these projects.
What’s needed is scalable, agile and secure cloud-based infrastructure at the lowest possible cost so they can spin up servers that support their data analysis projects exactly when they are required. This infrastructure must enable them to create proof-of-concepts quickly and cheaply – to fail fast and move on.
Amazon Kinesis is a fully managed service for real-time processing of streaming data at massive scale. Amazon Kinesis can collect and process hundreds of terabytes of data per hour from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
This introductory webinar, presented by Adi Krishnan, Senior Product Manager for Amazon Kinesis, will provide you with an overview of the service, sample use cases, and some examples of customer experiences with the service so you can better understand its capabilities and see how it might be integrated into your own applications.
Day 5 - Real-time Data Processing/Internet of Things (IoT) with Amazon KinesisAmazon Web Services
Amazon Kinesis is a fully managed service for real-time processing of streaming data at massive scale. Amazon Kinesis can collect and process hundreds of terabytes of data per hour from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
Reasons to attend:
- This session, will provide you with an overview of Amazon Kinesis.
- Learn about sample use cases and real life case studies.
- Learn how Amazon Kinesis can be integrated into your own applications.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
Learn how you can leverage AWS Platform to tackle all Business Intelligence challenges (Real-time, Data Warehousing performance and Business Intelligence democratisation)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
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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.
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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
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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/
3. Batch Processing
Hourly server logs
Weekly or monthly bills
Daily web-site clickstream
Daily fraud reports
Stream Processing
Real-time metrics
Real-time spending alerts/caps
Real-time clickstream analysis
Real-time detection
It’s All About the Pace
4. Streaming Data Scenarios Across Verticals
Scenarios/
Verticals
Accelerated Ingest-
Transform-Load
Continuous Metrics
Generation
Responsive Data Analysis
Digital Ad
Tech/Marketing
Publisher, bidder data
aggregation
Advertising metrics like
coverage, yield, and
conversion
User engagement with
ads, optimized bid/buy
engines
IoT Sensor, device telemetry
data ingestion
Operational metrics and
dashboards
Device operational
intelligence and alerts
Gaming Online data aggregation,
e.g., top 10 players
Massively multiplayer
online game (MMOG) live
dashboard
Leader board generation,
player-skill match
Consumer
Online
Clickstream analytics Metrics like impressions
and page views
Recommendation engines,
proactive care
5. Customer Use Cases
Sonos runs near real-time streaming
analytics on device data logs from
their connected hi-fi audio equipment.
Analyzing 30TB+ clickstream
data enabling real-time insights for
Publishers.
Glu Mobile collects billions of
gaming events data points from
millions of user devices in
real-time every single day.
Nordstorm recommendation team built
online stylist using Amazon Kinesis
Streams and AWS Lambda.
6. Metering Record Common Log Entry
MQTT RecordSyslog Entry
{
"payerId": "Joe",
"productCode": "AmazonS3",
"clientProductCode": "AmazonS3",
"usageType": "Bandwidth",
"operation": "PUT",
"value": "22490",
"timestamp": "1216674828"
}
{
127.0.0.1 user-
identifier frank
[10/Oct/2000:13:5
5:36 -0700] "GET
/apache_pb.gif
HTTP/1.0" 200
2326
}
{
“SeattlePublicWa
ter/Kinesis/123/
Realtime” –
412309129140
}
{
<165>1 2003-10-11T22:14:15.003Z
mymachine.example.com evntslog -
ID47 [exampleSDID@32473 iut="3"
eventSource="Application"
eventID="1011"][examplePriority@
32473 class="high"]
}
Streaming Data Challenges: Variety & Velocity
• Streaming data comes in
different types and
formats
− Metering records,
logs and sensor data
− JSON, CSV, TSV
• Can vary in size from a
few bytes to kilobytes or
megabytes
• High velocity and
continuous
7. Two Main Processing Patterns
Stream processing (real time)
• Real-time response to events in data streams
Examples:
• Proactively detect hardware errors in device logs
• Notify when inventory drops below a threshold
• Fraud detection
Micro-batching (near real time)
• Near real-time operations on small batches of events in data streams
Examples:
• Aggregate and archive events
• Monitor performance SLAs
9. Amazon Kinesis
Streams
• For Technical Developers
• Build your own custom
applications that process
or analyze streaming
data
Amazon Kinesis
Firehose
• For all developers, data
scientists
• Easily load massive
volumes of streaming data
into S3, Amazon Redshift
and Amazon
ElasticSearch
Amazon Kinesis
Analytics
• For all developers, data
scientists
• Easily analyze data
streams using standard
SQL queries
• Coming soon
Amazon Kinesis: Streaming Data Made Easy
Services make it easy to capture, deliver and process streams on AWS
10. Amazon Kinesis Firehose
Load massive volumes of streaming data into Amazon S3, Amazon
Redshift and Amazon Elasticsearch
Zero administration: Capture and deliver streaming data into Amazon S3, Amazon Redshift,
and other destinations without writing an application or managing infrastructure.
Direct-to-data store integration: Batch, compress, and encrypt streaming data for
delivery into data destinations in as little as 60 secs using simple configurations.
Seamless elasticity: Seamlessly scales to match data throughput w/o intervention
Capture and submit
streaming data to Firehose
Analyze streamingdata using your
favorite BI tools
Firehose loads streaming data
continuously into S3, Amazon Redshift
and Amazon Elasticsearch
12. Amazon Kinesis Streams
Managed ability to capture and store data
Hash Key Range Shards
0000
FFFF
5333
A666
1D – 7D
1000 Events / Second
Put_Record 8888 XXX
Put_Records 8888 XXX,
3333 YYY
Get_Records
258484ZZZ
1MB / Second
5 Reads / Second
2MB / Second
276777XXX
339393XXX
218484ZZZ
228111XXX
247222XXX
98484ZZZ
148888XXX
168888XXX
TRIM HORIZON
18484ZZZ
38111XXX
87222XXX
RECENT
SEQUENCE ID
Partial (Shard) Order
Put_Record 8888 XXX >33
1213232ZZZ
73434YYY
TIMESTAMP
Get_Shard_Iterator
13. Amazon Kinesis Streams
Managed ability to capture and store data
• Streams are made of shards
• Each shard ingests up to 1MB/sec, and
1000 records/sec
• Each shard emits up to 2 MB/sec
• All data is stored for 24 hours by
default; storage can be extended for
up to 7 days
• Scale Kinesis streams using scaling util
• Replay data inside of 24-hour window
1-7 Days
1000 Puts / Sec
1MB/S
5 Gets/ Sec
2MB/S
14. Amazon Kinesis Firehose vs. Amazon Kinesis
Streams
Amazon Kinesis Streams is for use cases that require custom
processing, per incoming record, with sub-1 second processing
latency, and a choice of stream processing frameworks.
Amazon Kinesis Firehose is for use cases that require zero
administration, ability to use existing analytics tools based on
Amazon S3, Amazon Redshift and Amazon Elasticsearch, and a
data latency of 60 seconds or higher.
16. Putting Data into Amazon Kinesis Streams
Determine your partition key strategy
• Managed buffer or streaming MapReduce job
• Ensure high cardinality for your shards
Provision adequate shards
• For ingress needs
• Egress needs for all consuming applications: if more
than two simultaneous applications
• Include headroom for catching up with data in stream
17. Putting Data into Amazon Kinesis
Amazon Kinesis Agent – (supports pre-processing)
• http://docs.aws.amazon.com/firehose/latest/dev/writing-with-agents.html
Pre-batch before Puts for better efficiency
• Consider Flume, Fluentd as collectors/agents
• See https://github.com/awslabs/aws-fluent-plugin-kinesis
Make a tweak to your existing logging
• log4j appender option
• See https://github.com/awslabs/kinesis-log4j-appender
19. Record Order and Multiple Shards
Unordered processing
• Randomize partition key to distribute events over
many shards and use multiple workers
Exact order processing
• Control partition key to ensure events are
grouped into the same shard and read by the
same worker
Need both? Use global sequence number
Producer
Get Global
Sequence
Unordered
Stream
Campaign Centric
Stream
Fraud Inspection
Stream
Get Event
Metadata
20. Sample Code for Scaling Shards
java -cp
KinesisScalingUtils.jar-complete.jar
-Dstream-name=MyStream
-Dscaling-action=scaleUp
-Dcount=10
-Dregion=eu-west-1 ScalingClient
Options:
• stream-name - The name of the stream to be scaled
• scaling-action - The action to be taken to scale. Must be one of "scaleUp”, "scaleDown"
or “resize”
• count - Number of shards by which to absolutely scale up or down, or resize
See https://github.com/awslabs/amazon-kinesis-scaling-utils
22. What?
Case for change product marketing content management
Faster
publication
Step up in
capabilities
Simplification
§ Daily content
refresh
§ Reliable and
predictable
§ Approval
workflows
§ Harmonized
process
§ Improve marketing
effectiveness
§ True localization
§ Easily share all
product information
§ In-context preview
§ One tool to
manage product
information
§ Improved
connectivity
between tooling
§ Less operational
issues
Time to market Marketing
effectiveness
And why?
23. Team
Philips – Netherlands
• Product owner
Dashsoft – Denmark
• Infrastructure Architecture & DevOps
Xplore group (Cronos NV) – Belgium
• Software Architecture & Development
24. Meet the
human PIM
• Maintaining 15.000 Excel sheets!
• Uploads to SDL
(1 day per country)
• Import excels to SP for production
(2 day per country)
• Global only localize requests (exclude products)
(5-10 days – per 1 country request)
• Customized PDPs and CRPs
(3 days per 1 country request)
• Aligning with BGs to maintain Central Repository
(8 hours per week)
• Notification to markets with new/updated content
(4 hours per week)
• Maintaining central repository excel
(4 hours per week)
• Translation queries
(8 hours per week)
The business challenge
25. The architectural challenge
• From Relational à Self-Describing
productinformation publishing
• Consumers of productinformation
(channels) normally require only
productinformation which is relevant
for them. What’s relevantfor a
channel differs per channel
• (Event-based) delta and full product
information delivery
Asset
relations
Marketing
Text
Specification
sFeatures
Navigation Filter keys
.com Leaflets otherSyndication Case study other
• Variety of formats and structures for
productinformation delivery
• Increasing need of retrieving product
information instead of receiving
• Performing,scalable and flexible
productinformation delivery
• Low impact on the PIM system
• In the cloud
26. Basics of AWS multi-channel publishing
STAGE 4: Isolated channels
•Relevant product information for Y only
•Product information structure according to Y
•File format delivery according to YSerializer Y
•Relevant product information for Z only
•Product information structure according to Z
•File format delivery according to ZSerializer Z
STAGE 1:
Relational Product
Information (PIM
system mirror)
• Includes complete set
of product information
• Based on PIM system
naming convention
and system structure
STAGE 2:
Relational Product
Information
(Philips)
• Includes complete set
of product information
• Based on Philips
naming convention
and structure
STAGE 3: Self-
describing product
information
• Includes complete set
of product information
(products, families
and hierarchies)
• Based on Philips
naming convention
and structure
27.
28. Basics of AWS multi-channel publishing
Technology
DynamoD
B input
Canonical
Conversion
Lambda
Canonical
Model
Link
Propagating
Lambda
Denormalize
d Canonical
Model
Denormalize
r Lambda
Output
Serializer
Lambda
Output
Model
Output
Serializer
Lambda
Output
Changelist
Origin Canonical Serializer
EC2
Containe
r Service
Poll
Changeli
st
Generate
Output
Output
Queue
29. DynamoDB Streams and Kinesis
Delta output from DynamoDB
Reliable and blocking stream
Process updates from
DynamoDB
Kinesis Interface is comparable to
DynamoDB Streams when used
from AWS Lambda.
31. Why
Kinesis?
DynamoDB Streams parallelism is correlated on database provisioned
throughput.
Router Kinesis
Denormaliz
er Lambda
Canonica
l Product
Denormalized
Canonical
Model
Use Kinesis to provide performance based on compute
requirements.
32. Cloudwatch
Centralized monitoring and logging
Used for Scaling in/out DynamoDB, Kinesis and ECS
https://github.com/awslabs/amazon-kinesis-scaling-
utils
33. Autoscaling
Single update:Afew events passing through the system, multiplied for each output channel
Burst: Millions of events are processed each hour when a full data load is performed
Router Kinesis
Denormaliz
er Lambda
Router Kinesis
Denormaliz
er Lambda
34. • Improve time to market
• Unleash new capabilities like real time
publication
• Improve marketing effectiveness
• Reduced total cost of ownership
• Enable workflows to guide content creation
process and safeguard content quality
• Improve reliability / predictability
• Simplify management of product information
• Q&R approved platform to manage our
medical devices
Recap| Business benefits
35. What’s next?
Enhance the API capabilities
Roll out to more channels
Leverage our distributed architecture
• To meet compliance / privacy regulations (e.g. China)
38. Amazon Kinesis Client Library
• Build Kinesis Applications with Kinesis Client Library (KCL)
• Open source client library available for Java, Ruby, Python,
Node.JS dev
• Deploy on your EC2 instances
• KCL Application includes three components:
1. Record Processor Factory – Creates the record processor
2. Record Processor – Processor unit that processes data from a
shard in Amazon Kinesis Streams
3. Worker – Processing unit that maps to each application instance
39. State Management with Kinesis Client Library
• One record processor maps to one shard and processes data records from
that shard
• One worker maps to one or more record processors
• Balances shard-worker associations when worker / instance counts change
• Balances shard-worker associations when shards split or merge
40. Other Options
• Third-party connectors(for example, Splunk)
• AWS IoT platform
• AWS Lambda
• Amazon EMR with Apache Spark, Pig or Hive
41. Apache Spark and Amazon Kinesis Streams
Apache Spark is an in-memory analytics cluster using
RDD for fast processing
Spark Streaming can read directly from an Amazon
Kinesis stream
Amazon software license linking – Add ASL dependency
to SBT/MAVEN project, artifactId = spark-
streaming-kinesis-asl_2.10
KinesisUtils.createStream(‘twitter-stream’)
.filter(_.getText.contains(”Open-Source"))
.countByWindow(Seconds(5))
Example: Counting tweets on a sliding window
43. Using Spark Streaming with Amazon Kinesis
Streams
1. Use Spark 1.6+ with EMRFS consistent view option – if you
use Amazon S3 as storage for Spark checkpoint
2. Amazon DynamoDB table name – make sure there is only one
instance of the application running with Spark Streaming
3. Enable Spark-based checkpoints
4. Number of Amazon Kinesis receivers is multiple of executors so
they are load-balanced
5. Total processing time is less than the batch interval
6. Number of executors is the same as number of cores per
executor
7. Spark Streaming uses default of 1 sec with KCL
47. Conclusion
• Amazon Kinesis offers: managed service to build applications, streaming
data ingestion, and continuous processing
• Ingest aggregate data using Amazon Producer Library
• Process data using Amazon Connector Library and open source connectors
• Determine your partition key strategy
• Try out Amazon Kinesis at http://aws.amazon.com/kinesis/
48. • Technical documentations
• Amazon Kinesis Agent
• Amazon Kinesis Streams and Spark Streaming
• Amazon Kinesis Producer Library Best Practice
• Amazon Kinesis Firehose and AWS Lambda
• Building Near Real-Time Discovery Platform with Amazon Kinesis
• Public case studies
• Glu mobile – Real-Time Analytics
• Hearst Publishing – Clickstream Analytics
• How Sonos Leverages Amazon Kinesis
• Nordstorm Online Stylist
Reference