Ever heard of event streaming? Look keenly at the two words and I can be sure you must have stumbled upon it in one way or the other in your internet escapades and wild journeys. May be you have and maybe you have not. What matters now is that all of us get to the same page by getting a basic grasp what it is and why it is important in this discussion.
Well, a simple way to view it is that event streaming enables companies or organizations to analyze data that pertains to an event (a click, an error, a success) in an application and respond to that event in real time. Events as it we have attempted to list can be virtually anything of interest. It may be how many times a success message has been received or errors generated or something else. This is defined and determined by customer needs or use case demands.
The reason why we have Apache Kafka in the title of this article is because right now, the most accepted and popular tool for event streaming is Apache Kafka. It is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Source: Kafka Site. Apache Kafka allows users to send, store and request data when and where they need it.
The second thing you have observed in the title is Apache Spark. We are going to tackle what that is here. Originally developed at the University of California, Berkeley’s AMPLab, Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Source: Wikipedia.
1. Spark The Definitive Guide
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.
You will explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine-learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library.
You should buy this book because you will:
- Get a gentle overview of big data and Spark
- Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples
- Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames
- Understand how Spark runs on a cluster
- Debug, monitor, and tune Spark clusters and applications
- Learn the power of Structured Streaming, Spark’s stream-processing engine
- Learn how you can apply MLlib to a variety of problems, including classification or recommendation.
As a product of the creators of the open-source cluster-computing framework, this is a guide that anyone can pick up and find gems of wisdom and an absolutely clean rendition of Apache Spark. You will get a gentle overview of big data while getting into the crux of knowledge you have always wished to be exposed to. Click below to get your mind elevated and sprinkled with the best Spark content from Amazon.
2. Kafka The Definitive Guide
This book’s updated second edition shows application architects, developers, and production engineers new to the Kafka open-source streaming platform how to handle real-time data feeds. Additional chapters cover Kafka’s AdminClient API, new security features, and tooling changes.
Engineers from Confluent and LinkedIn responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream processing applications with this platform. Through detailed examples, you will learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.
There is simply a lot of stuff about Kafka that you are going to enjoy as you get up-skilled.
The reason you should buy this book is because you will examine:
- How publish-subscribe messaging fits in the big data ecosystem
- Kafka producers and consumers for writing and reading messages
- Patterns and use-case requirements to ensure reliable data delivery
- Best practices for building data pipelines and applications with Kafka
- How to perform monitoring, tuning, and maintenance tasks with Kafka in production
- The most critical metrics among Kafkaâ??s operational measurements
- Kafka’s delivery capabilities for stream processing systems
Receive knowledge that engineers from Confluent and LinkedIn have sat down and shared with you in this definitive guide. Every chapter has something to fascinate you and build your skillsets for the next level. Click below to acquaint yourself better with this book as well as order one or two from Amazon.
3. Kafka Streams in Action
Level: Beginner to Intermediate
Author Bill Bejeck is a Kafka Streams contributor and Confluent engineer with over 15 years of software development experience.
For a beginner to Intermediate reader, Kafka Streams in Action teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you will explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You will even dive into streaming SQL with KSQL! Practical to the very end, it finishes with testing and operational aspects, such as monitoring and debugging.
You should buy this book because you will learn the following
- Using the KStreams API
- Filtering, transforming, and splitting data
- Working with the Processor API
- Integrating with external systems
Bill covers a whole lot more than other authors do in this subject including testing and monitoring among others. Its whole approach makes it a book you should pursue and read. Click on the link below to get started as soon as you order two or three copies from Amazon.
4. Mastering Kafka Streams and ksqlDB
This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real-time.
Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You will learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing.
After buying this book, you get to:
- Learn the basics of Kafka and the pub/sub communication pattern
- Build stateless and stateful stream processing applications using Kafka Streams and ksqlDB
- Perform advanced stateful operations, including windowed joins and aggregations
- Understand how stateful processing works under the hood
- Learn about ksqlDB’s data integration features, powered by Kafka Connect
- Work with different types of collections in ksqlDB and perform push and pull queries
- Deploy your Kafka Streams and ksqlDB applications to production
Click below to appreciate the good work that Mitch has availed for your instruction and learning from Amazon. It is well worth it.
5. Building Data Streaming Applications with Apache Kafka
Level: Intermediate to Advanced
