Apache Kafka vs RabbitMQ: Fit For Purpose / Decision Tree

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Kafka as a streaming data platform is becoming the successor to traditional messaging systems such as RabbitMQ. Nevertheless, there are still some use cases where they could be a good fit. This one single slide tries to answer in a concise and unbiased way where to use Apache Kafka and where to use RabbitMQ. Your comments and feedback are much appreciated.

Data & Analytics

Apache Kafka vs RabbitMQ: Fit For Purpose/Decision Tree
Feature Kafka RabbitMQ
Need a durable message store and message replay capability Y N
Need ordered storage and delivery Y* N
Need multiple different consumer of same data Y N
Need to handle throughput of all my data well even at web scale and not a smaller set of messages Y N
Need a high throughput with low latency Y N
Need to decouple producers and consumers from a performance perspective to avoid the ’slow
consumer problem’
Y N
Need good integration with the Hadoop ecosystem and modern stream processing frameworks Y N
Need a good buffer for batch systems to scale well to large backlogs Y N
Need same tool for building data pipelines and streaming data applications without the help of
additional software.
Y N
Need support for multiple protocols: AMQP, STOMP, JMS, MQTT, HTTP, JSON-RPC, … N Y
Need message priority: producers can specify the priority of messages to consumers N Y
Need explicit delivery processing acknowledgements of messages from consumers N Y
Need flexible routing: producers direct messages to appropriate consumers N Y
Need transaction support: provide commit and rollback functionality for local transactions N Y
Need native tracing support to let me find out what's going on if things are misbehaving. N Y
Need a browser-based UI for management and monitoring of my message brokers. N Y
Need a self-sufficient message broker without additional tool such as Zookeeper N Y
*At partition level. By @SlimBaltagi from Advanced Analytics LLC

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Apache Flink is a community-driven open source and memory-centric Big Data analytics framework. It provides the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases. Flink uses a mixture of Scala and Java internally, has very good Scala APIs and some of its libraries are basically pure Scala (FlinkML and Table). At its core, it is a streaming dataflow execution engine and it also provides several APIs for batch processing (DataSet API), real-time streaming (DataStream API) and relational queries (Table API) and also domain-specific libraries for machine learning (FlinkML) and graph processing (Gelly). In this talk, you will learn in more details about: What is Apache Flink, how it fits into the Big Data ecosystem and why it is the 4G (4th Generation) of Big Data Analytics frameworks? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? What are the benchmarking results between Apache Flink and those other Big Data analytics frameworks?

Why apache Flink is the 4G of Big Data Analytics Frameworks

Slim Baltagi

In this hands-on Apache Flink presentation, you will learn in a step-by-step tutorial style about: • How to setup and configure your Apache Flink environment: Local/VM image (on a single machine), cluster (standalone), YARN, cloud (Google Compute Engine, Amazon EMR, ... )? • How to get familiar with Flink tools (Command-Line Interface, Web Client, JobManager Web Interface, Interactive Scala Shell, Zeppelin notebook)? • How to run some Apache Flink example programs? • How to get familiar with Flink's APIs and libraries? • How to write your Apache Flink code in the IDE (IntelliJ IDEA or Eclipse)? • How to test and debug your Apache Flink code? • How to deploy your Apache Flink code in local, in a cluster or in the cloud? • How to tune your Apache Flink application (CPU, Memory, I/O)?

Apache Flink Crash Course by Slim Baltagi and Srini Palthepu

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These are the slides of my talk on June 30, 2015 at the first event of the Chicago Apache Flink meetup. Although most of the current buzz is about Apache Spark, the talk shows how Apache Flink offers the only hybrid open source (Real-Time Streaming + Batch) distributed data processing engine supporting many use cases: Real-Time stream processing, machine learning at scale, graph analytics and batch processing. In these slides, you will find answers to the following questions: What is Apache Flink stack and how it fits into the Big Data ecosystem? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? What is the architecture of Apache Flink? What are the different execution modes of Apache Flink? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? Who is using Apache Flink? Where to learn more about Apache Flink?

Overview of Apache Flink: Next-Gen Big Data Analytics Framework

Slim Baltagi

Hadoop or Spark: is it an either-or proposition? An exodus away from Hadoop to Spark is picking up steam in the news headlines and talks! Away from marketing fluff and politics, this talk analyzes such news and claims from a technical perspective. In practical ways, while referring to components and tools from both Hadoop and Spark ecosystems, this talk will show that the relationship between Hadoop and Spark is not of an either-or type but can take different forms such as: evolution, transition, integration, alternation and complementarity.

Hadoop or Spark: is it an either-or proposition? By Slim Baltagi

Slim Baltagi

Big Data at CME Group: Challenges and Opportunities

Slim Baltagi

Building a Modern Data Architecture with Enterprise Hadoop

Slim Baltagi

Transitioning Compute Models: Hadoop MapReduce to Spark

Slim Baltagi

Slim Baltagi & Rick Fath. Closing Keynote: Big Data Executive Summit. Chicago 11/28/2012. PART I – Hadoop at CME: Our Practical Experience 1. What’s CME Group Inc.? 2. Big Data & CME Group: a natural fit! 3. Drivers for Hadoop adoption at CME Group 4. Key Big Data projects at CME Group 5. Key Learning’s PART II - Bringing Hadoop to the Enterprise: Challenges & Opportunities PART II - Bringing Hadoop to the Enterprise 1. What is Hadoop, what it isn’t and what it can help you do? 2. What are the operational concerns and risks? 3. What organizational changes to expect? 4. What are the observed Hadoop trends?

A Big Data Journey: Bringing Open Source to Finance

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Apache Flink Crash Course by Slim Baltagi and Srini Palthepu

Overview of Apache Flink: Next-Gen Big Data Analytics Framework

Hadoop or Spark: is it an either-or proposition? By Slim Baltagi

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