This document discusses using Apache Apex for real-time decision making within 2 milliseconds. It provides performance benchmarks for Apex, showing average latency of 0.25ms for over 54 million events with 600GB of RAM. It compares Apex favorably to other streaming technologies like Storm and Flink, noting Apex's self-healing capabilities, independence of operators, and ability to meet latency and throughput requirements even during failures. The document recommends Apex for its maturity, fault tolerance, and ability to meet the goals of latency under 16ms, 99.999% availability, and scalability.
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.
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
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.
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 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
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.
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
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.
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 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
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.
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.
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.
Intro to Apache Apex - Next Gen Platform for Ingest and TransformApache Apex
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.
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.
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
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.
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.
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
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.
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.
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.
This talk covers the Vault 8 team's journey at Capital One where we investigated a wide variety of stream processing solutions to build a next generation real-time decisioning platform to power Capital One's infrastructure.
The result of our analysis showed Apache Storm, Apache Flink, and Apache Apex as prime contenders for our use case with Apache Apex ultimately proving to be the solution of choice based on its present readiness for enterprise deployment and its excellent performance.
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.
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.
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.
Intro to Apache Apex - Next Gen Platform for Ingest and TransformApache Apex
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.
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.
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
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.
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.
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
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.
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.
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.
This talk covers the Vault 8 team's journey at Capital One where we investigated a wide variety of stream processing solutions to build a next generation real-time decisioning platform to power Capital One's infrastructure.
The result of our analysis showed Apache Storm, Apache Flink, and Apache Apex as prime contenders for our use case with Apache Apex ultimately proving to be the solution of choice based on its present readiness for enterprise deployment and its excellent performance.
24 Hours of PASS, Summit Preview Session: Virtual SQL Server CPUsDavid Klee
One of the largest points of contention with virtual SQL Servers and the VM administrators is how to configure the CPUs. Experience says more CPUs are better for performance. VM admins say less is better. Third-party vendors say you need all of them (and it doesn’t matter how many your hosts have either). Can over-provisioning virtual machine CPUs speed things up, or does it slow things down? What is the right methodology to determine the correct number of virtual CPUs? How does this configuration align with the physical servers? From sampling and analyzing performance data, to “right-sizing’ your SQL Server virtual machine CPU count, to properly aligning the VM with the physical server NUMA topology, you will gain the understanding of how to properly manage and validate your virtual SQL Server vCPU configuration in this insightful session. Valuable tips and tricks will be shared that you can take back to your virtual SQL Servers and immediately apply to your own environments.
Maria DB Galera Cluster for High AvailabilityOSSCube
Want to understand how to set high availability solutions for MySQL using MariaDB Galera Cluster? Join this webinar, and learn from experts. During this webinar, you will also get guidance on how to implement MariaDB Galera Cluster.
University of Alberta migrated their central Learning Management System from Blackboard Vista on Oracle to Moodle on Postgresql 9.0. We went from a pilot project of 13 courses in January 2011 to running all centrally supported courses (3600+) in Moodle in September 2012. Our central Moodle instance has seen more than 500,000 page loads and 24,000 unique visitors in a single day. Over the last two years we have learned a few hard lessons and overcome a few challenges in running Postgresql in a 24x7 production environment.
Stream Computing (The Engineer's Perspective)Ilya Ganelin
This is a ground zero introduction to stream processing. The focus is on what differentiates them - this turns out not to be performance, but how they solve the challenges scalability, availability, durability, and failure-handling.
We look at Storm, Flink, and Apex as case studies to understand the space.
3450 - Writing and optimising applications for performance in a hybrid messag...Timothy McCormick
Messaging architectures in any environment, from local standalone deployments through to public clouds, must provide the highest reliability yet maximize their performance. This session gives you an insight into IBM MQ and how applications can be made to perform to their absolute best while maintaining the data integrity that IBM MQ is renowned for. We'll see how this can be achieved through a combination of good application design, system tuning and architectural patterns.
Platform Security Summit 18: Xen Security Weather Report 2018The Linux Foundation
The Xen Project is unique in its breadth of adoption and diverse contributions. Many vendors in the ecosystem are not directly competing, enabling collaboration which otherwise would not be possible. While hypervisors were once seen as purely cloud and server technologies, they are now used in many market segments to add compartmentalization and layers of security. This has led to renewed focus on older technologies, such as L4Re/seL4 and new technologies such as zircon, ACRN and others.
Meanwhile, the Xen Project has been trailblazing in adopting virtualization in new market segments and continues to innovate and set the direction for the industry. This has enabled downstream Xen developers to build viable businesses and products in areas such as security and embedded. This talk will cover Xen feature changes that are driven by security needs, and the challenges of safety certification within the context of open source projects and Xen Project in particular.
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.
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.
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.
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.
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.
Presenter: Kenn Knowles, Software Engineer, Google & Apache Beam (incubating) PPMC member
Apache Beam (incubating) is a programming model and library for unified batch & streaming big data processing. This talk will cover the Beam programming model broadly, including its origin story and vision for the future. We will dig into how Beam separates concerns for authors of streaming data processing pipelines, isolating what you want to compute from where your data is distributed in time and when you want to produce output. Time permitting, we might dive deeper into what goes into building a Beam runner, for example atop Apache Apex.
Making sense of Apache Bigtop's role in ODPi and how it matters to Apache ApexApache Apex
Roman Shaposhnik: Director of Open Source, Pivotal; Committer, Apache Hadoop; Founder, Apache Bigtop
Making sense of Apache Bigtop's role in ODPi and how it matters to Apache Apex.
Chinmay Kolhatkar: Engineer, DataTorrent & Committer, Apache Apex
For ease of use and deployment, Apache Apex leverages Apache Bigtop. Apex, being part of bigtop stack, can be easily deployed in both debian and rpm based cluster system and run validation tests for installation. This talk will cover a demo on how to install apex-bigtop and use it. It also covers a test sandbox docker environment, having pre-installed bigtop-hadoop and bigtop-apex, for quickly getting started with apex.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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/
12. 12
Hard Metrics Goal
Latency < 40ms
Ideally < 16ms
Throughput Goal of 2000 events / second
Durability No loss, every message gets exactly one response
Availability 99.5% uptime (downtime of 1.83 days / year);
Ideally 99.999% uptime (downtime of 5.26 minutes / year)
Scalability Can add resources, still meet latency requirements
Integration Transparently connected to existing systems – Hardware, Messaging,
HDFS
Soft Metrics Goal
Open Source All components licensed as open source
Extensibility Rules can be updated, model is regularly refreshed
27. 27
Durability
• Two physically independent pipelines on the same cluster processing
identical data
• For the same tuple, we find the best-case time between two pipelines
– 39 records out of 5.2M exceeded 16ms
– 173 out of 5.2M exceeded 16ms in one pipeline but succeeded in the other
• 99.99925% success rate – “Five Nines”
• Average Latency of 0.0981ms
31. 31
Streaming Technologies Evaluated
• Spark Streaming
• Samza
• Storm
• Feedzai
• Infosphere Streams
• Flink
• Ignite
• VoltDB
• Cassandra
• Apex
• Of all evaluated technologies, Apache Apex is the only technology that is ready to
bring the decision making solution to production based on:
– Maturity
– Fault-tolerance
– Enterprise-readiness
– Performance
• Focus on open source
• Drive Roadmap
• Competitive Advantage for C1
32. 32
Stream Processing – Apache Storm
• An open-source, distributed, real-time computation system
– Logical operators (spouts and bolts) form statically parallelizable topologies
– Very high throughput of messages with very low latency
– Can provide <10ms latency end-end under normal operation
• Basic abstractions provide an at-least-once processing guarantee
Limitations
• Nimbus is a single point of failure
– Rectified by Hortonworks, but not yet available to the public (no timeline for release)
• Upstream bolt/spout failure triggers re-compute on entire tree
– Can only create parallel independent stream by having separate redundant topologies
• Bolts/spouts share JVM Hard to debug
• Failed tuples cannot be replayed quicker than 1s
• No dynamic topologies
• Cannot add or remove applications without service interruption
33. 33
Stream Processing – Apache Flink
• An open-source, distributed, real-time computation system
– Logical operators are compiled into a DAG of tasks executed by Task Managers
– Supports streaming, micro-batch, batch compute
– Supports aggregate operations on streams (reduce, join, groupBy)
– Capable of <10 ms end-end latency with streaming under normal operation
• Can provide exactly-once processing guarantees
Limitations
• Failures trigger reset of ALL operators to last checkpoint
– Depends on upstream message broker to track state
• Operators share JVM
– Failure in one brings down all tasks sharing that JVM
– Hard to debug
• No dynamic topologies
• Young community, young product
34. 34
Stream Processing – Apache Apex
• An open-source, distributed, real-time computation system on YARN
• Apex is the core system powering DataTorrent, released under ASF
• Demonstrated high throughput with low latency running a next-generation
C1 model (avg. 0.25ms, max 2ms, @ 70k records/sec) w/ 600GB RAM
• True YARN application developed on the principles of Hadoop and YARN
at Yahoo!
• Mature product
– Core principles of Apex are derived from a proven solution in Yahoo Finance and
Yahoo hadoop.
– Operability in Apex is first class citizen with focus on Enterprise capabilities
• DataTorrent (Apex) is executing on production clusters at Fortune 100
companies.
35. 35
Stream Processing – Apache Apex
Maturity
• Designed to process and manage global data for Yahoo! Finance
– Primary focus is on stability, fault-tolerance and data management
– Only OSS streaming technology considered designed explicitly for the financial world
• Data or computation could never be lost or replicated
• Architecture had to never go down
• Goal was to make it rock-solid and enterprise-ready before worrying about performance
• Data flow across countries – perfect for use-case that requires cross-
cluster interaction
Enterprise Readiness
• Advanced support for:
– Encryption, authentication, compression, administration, and monitoring
– Deployment at scale in the cloud and on-prem – AWS, Google Cloud, Azure
• Integrates with huge set of existing tools:
– HDFS, Kafka, Cassandra, MongoDB, Redis, ElasticSearch, CouchDB, Splunk, etc.
36. 36
Apex Platform – Summary
• Apex Architecture
– Networks of physically independent, parallelizable operators that scale dynamically
– Dynamic topology modification and deployment
– Self-healing, fault tolerant, & recoverable
• Durable messaging queues between operators, check-pointed in memory and on disk
• Resource manager is a replicated YARN process, monitors and restarts downed operators
– No single point of failure, highly modular design
– Can specify locality of execution (avoids network and inter-process latency)
• Guarantees at-least-once, at-most-once, or exactly-once processing
Directed Acyclic Graph (DAG)
Output
Stream
Tuple Tuple
er
Operator
er
Operator
er
Operator
er
Operator
42. 42
Apex Platform – Failure Recovery
• Physical independence of partitions is critical
• Redundant STRAMs
• Configurable window size and heartbeat for low-latency recovery
• Downstream failures do not affect upstream components
– Snapshotting only depends on previous operator, not all previous operators
– Can deploy parallel DAGs with same point of origin (simpler from a hardware and
deployment perspective)
43. 43
Apex Platform – Windowing
• Sliding window and
tumbling window
• Window based on
checkpoint
• No artificial latency
• Used for stats
measurement
44. 44
• Apex
– Great UI to monitor, debug, and control system performance
– Fault-tolerance and recovery out of the box - no additional setup, or improvement
needed
• YARN is still a single point of failure, a name node failure can still impact the system
– Built-in support for dynamic and automatic scaling to handle larger throughputs
– Native integration with Hadoop, YARN, and Kafka – next-gen standard at C1
– Mature product
• Principles derived from years at Yahoo Finance and Yahoo Hadoop
• Built and planned by deep Hadoop and streaming experts
– Proven performance in production at Fortune 100 companies
Enterprise Readiness
45. 45
Enterprise Readiness
• Storm
– Widely used but abandoned by creators at Twitter for Heron in production
• Storm debug-ability - topology components are bundled in one process
• Resource demands
– Need dedicated hardware
– Can’t scale on demand or share usage
• Topology creation/tear-down is expensive, topologies can’t share cluster resources
– Have to manually isolate & de-commission machines
– Performance in failure scenarios is insufficient for this use-case
• Flink
– Operational performance has not been proven
• Only one company (ResearchGate) officially uses Flink in production
– Architecture shares fundamental limitations of Storm with regards to
dynamically scaling operators & topologies and debugability
– Performance in failure scenarios is insufficient for this use-case
46. 46
Performance
• Storm
– Meets latency and throughput requirements only when no failures occur.
– Resilience to failures only possible by running fully independent clusters
– Difficult to debug and operationalize complex systems (due to shared JVM and poor
resource management)
• Flink
– Broader toolset than Storm or Apex – ML, batch processing, and SQL-like queries
– Meets latency and throughput requirements only when no failures occur.
– Failures reset ALL operators back to the source – resilience only possible across
clusters
– Difficult to debug and operationalize complex systems (due to shared JVM)
• Apex
– Supports redundant parallel pipelines within the same cluster
– Outstanding latency and throughput even in failure scenarios
– Self-healing independent operators (simple to isolate failures)
– Only framework to provide fine-grained control over data and compute locality
47. 47
Roadmap – Storm
• Commercial support from from Hortonworks but limited code
contributions
• Twitter - Storm’s largest user - has completely abandoned Storm for Heron
• Business Continuity
– Enhance Storm’s enterprise readiness with high availability (HA) and failover to standby
clusters
– Eliminate Nimbus as a single point of failure
• Operations
– Apache Ambari support for Nimbus HA node setup
– Elastic topologies via YARN and Apache Slider.
– Incremental improvements to Storm UI to easily deploy, manage and monitor
topologies.
• Enterprise readiness
– Declarative writing of spouts, bolts, and data-sources into topologies
48. 48
Roadmap – Flink
• Fine-grained fault tolerance (avoid rollback to data source) – Q2 2015
• SQL on Flink – Q3/Q4 2015
• Integrate with distributed memory storage – No ECD
• Use off-heap memory – Q1 2015
• Integration with Samoa, Tez, Mahout DSL – No ECD
49. 49
Roadmap – Apex
• Roadmap for next 6 months
• Support creation of reusable pluggable modules (topologies)
• Add additional operators to connect to existing technology
– Databases
– Messaging
– Modeling systems
• Add additional SQL-like operations
– Join
– Filter
– GroupBy
– Caching
• Add ability to create cycles in graph
– Allows re-use of data for ML algorithms (similar to Spark’s caching)
50. 50
Road Map Comparison
• Storm
– Roadmap is intended to bring Storm to enterprise readiness Storm is not enterprise
ready today according to Hortonworks
• Flink
– Roadmap brings Flink up to par with Spark and Apex, does not create new capabilities
relative to either
– Spark is more mature for batch-processing and micro-batch and Apex is more mature
from a streaming standpoint.
• Apex
– No need to improve core architecture, focus is instead on adding functionality
• Better support for ML
• Better support for wide variety of business use cases
• Better integration with existing tools
– Stated commitment to letting the community dictate direction. From incubator proposal:
• “DataTorrent plans to develop new functionality in an open, community-driven way”
51. 51
Community
• Vendor and community involvement drive roadmap and project growth
• Storm
– Limited improvements to core components of Storm in recent months
– Limited focused and active committers
– Actively promoted and supported in public by Hortonworks
• Flink
– Some adoption in Europe, growing response in U.S.
– 11 active committers, 10 are from Data Artisans (company behind Flink)
– Community is very young, but there is substantial interest
• Apex
– Wide support network around Apex due to its evolution alongside Hadoop and YARN
– Young but actively growing community: http://incubator.apache.org/projects/apex.html
– Opportunity for C1 to drive growth and define the direction of this product
52. 52
Streaming Solutions Comparison
• Apex
– Ideal for this use case, meets all performance requirements and is ready for out-of-the-
box enterprise deployment
– Committer status from C1 allows us to collaboratively drive roadmap and product
evolution to fit our business need.
• Storm
– Great for many streaming use cases but not the right fit for this effort
– Performance in failure scenarios does not meet our requirements
– Community involvement is waning and there is a limited road map for substantial
product growth
• Flink
– Poised to compete with Spark in the future based on community activity and roadmap
– Not ready for enterprise deployment:
• Technical limitations around fault-tolerance and failure recovery
• Lack of broad community involvement
• Roadmap only brings it up to par with existing frameworks
53. 53
New Capabilities Provided by Proposed Architecture
• Millisecond Level Streaming Solution
• Fault Tolerant & Highly Available
• Parallel Model Scoring for Arbitrary Number of Models
• Quick Model Generation & Execution
• Dynamic Scalability based on Latency or Throughput
• Live Model Refresh
• A/B Testing of Models in Production
• System is Self Healing upon failure of components (**)
54. 54
Decisioning System Architecture - Strengths
• Internal
– Capital One software, running on Capital One hardware, designed by Capital One
• Open source
– Internally maintainable code
• Living Model
– Can be re-trained on current data & updated in minutes, not years
– Offline models can expanded and re-developed and deployed to production at will
• Extensible
– Modular architecture with swappable components
• A/B Model Testing in Production
• Dynamic Deployment / Refresh of Models
55. 55
Hardware
MDC Hardware Specifications
• Server Quantity – 15
• Server Model – Supermicro
• CPU – Intel Xeon E5-2695v2 2.4Ghz
12Cores
• Memory – 256GB
• HDD – (5) 4TB Seagate SATA
• Network Switch – Cisco Nexus 6001
10GB
• NIC – 2port SFP+ 10GbE
MDC Software Specifications
• Hadoop – v2.6.0
• Yarn – v2.6.0
• Apache Apex – v3.0
• Linux OS – RHEL v6.7
• Linux OS Kernel - 2.6.32-
573.7.1.el6.x86_64
Spark streaming is missing – nonstarter due to microbatch, lack of dynamic dag reconfiguration
Fast
Easy to use
Mature
***********
Failures are not independent, nimbus, no dynamic topologies, 1 sec ack, resource usage
Community stagnating, only Horton
Roadmap still to bring it to enterprise (Integration with YARN, elastic topologies, high availability)
Easy to Use
Fast
Support for SQL-like queries
----- Meeting Notes (10/27/15 10:14) -----
Reset to upstream data source, Shared JVM, No dynamic topologies
Young community
Roadmap – fine grained fault tolerance, in-memory store integration, off-heap memory, full SQL
Veterans from Yahoo! Finance and Hadoop
Built for Enterprise stability and durability before performance
*****************
Dynamic topologies
Downstream components do not affect upstream
Fine grained control of locality
No single point of failure
Operators are independent
Independence of partitions
Auto-scaling (throughput and latency)