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 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.
Spark: The Definitive Guide: Big Data Processing Made Simple
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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 to you in this definitive guide. Every chapter has something to fascinate you and build your skillsets for the next level. Click below to acquaint better with this book as well as order one or two from Amazon.
Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale
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 is 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.
Kafka Streams in Action: Real-time apps and microservices with the Kafka Streams API
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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.
Mastering Kafka Streams and ksqlDB: Building Real-Time Data Systems by Example
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5. Building Data Streaming Applications with Apache Kafka
Level: Intermediate to Advanced
This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease.
It first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, the authors take you through more advanced concepts in Apache Kafka such as capacity planning and security.
What You Will Learn once you have this resource
- Learn the basics of Apache Kafka from scratch
- Use the basic building blocks of a streaming application
- Design effective streaming applications with Kafka using Spark, Storm &, and Heron
- Understand the importance of a low -latency , high- throughput, and fault-tolerant messaging system
- Make effective capacity planning while deploying your Kafka Application
- Understand and implement the best security practices
If you want to learn how to use Apache Kafka and the different tools in the Kafka ecosystem in the easiest possible manner, this book is for you. Click the link below, get to Amazon, look out for more information and lastly, order a copy for your personal collection of knowledge and skills.
Building Data Streaming Applications with Apache Kafka: Design, develop and streamline applications using Apache Kafka, Storm, Heron and Spark
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6. Advanced Analytics with Spark
In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.
You will start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance.
If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you will find the book’s patterns useful for working on your own data applications.
Once you buy this book, you will:
- Familiarize yourself with the Spark programming model
- Become comfortable within the Spark ecosystem
- Learn general approaches in data science
- Examine complete implementations that analyze large public data sets
- Discover which machine learning tools make sense for particular problems
- Acquire code that can be adapted to many uses
The styles that the author employs in this resource is warm and welcomes all developers to start mastering Apache Spark and its ecosystem. Ever intermediate reader with and interest in data analytics will find this book invaluable in their journey to mastery. Click below to buy this book and get started immediately.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale
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7. Learning Spark
Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you will be able to:
- Learn Python, SQL, Scala, or Java high-level Structured APIs
- Understand Spark operations and SQL Engine
- Inspect, tune, and debug Spark operations with Spark configurations and Spark UI
- Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka
- Perform analytics on batch and streaming data using Structured Streaming
- Build reliable data pipelines with open source Delta Lake and Spark
- Develop machine learning pipelines with MLlib and productionize models using MLflow
Machine learning continues to mature and gain traction the corporate space as well as in the technology arena where it emanates from. Learning Spark gets deep in this area of expertise by tackling all levels of data analytics so that you can gain the skills and knowledge you seek. It is all displayed in Amazon waiting for you to pick it up and learn. Click below to get started.
Learning Spark: Lightning-Fast Data Analytics
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8. Streaming Systems
Before we set off into the wild jungle of this resource, let us take a little path and look into the background of the authors. Tyler Akidau is a senior staff software engineer at Google, where he is the technical lead for the Data Processing Languages & Systems group, responsible for Google’s Apache Beam efforts, Google Cloud Dataflow, and internal data processing tools like Google Flume, MapReduce, and MillWheel.
Slava Chernyak, a co-author is a senior software engineer at Google Seattle while Reuven Lax, another author, is a senior staff software engineer at Google Seattle, and has spent the past nine years helping to shape Google’s data processing and analysis strategy.
With this practical guide by the three authors, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.
Expanded from Tyler Akidau’s popular blog posts “Streaming 101″ and “Streaming 102“, this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You will also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax presented above.
Once you have this book, you will explore:
- How streaming and batch data processing patterns compare
- The core principles and concepts behind robust out-of-order data processing
- How watermarks track progress and completeness in infinite datasets
- How exactly-once data processing techniques ensure correctness
- How the concepts of streams and tables form the foundations of both batch and streaming data processing
- The practical motivations behind a powerful persistent state mechanism, driven by a real-world example
- How time-varying relations provide a link between stream processing and the world of SQL and relational algebra
The authors will take your hand, take you slowly from a beginner level to a level of understanding that you will find comfortable to start real work in a data related field. Fetch your book from Amazon below and start your career before you know it.
Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing
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9. High Performance Spark
Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. It is a solution for you who have tried to implement this tool but you still feel like the optimizations you expected to happen are still not good enough.
Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you will also learn how to make it sing.
With this book, you will explore:
- How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure
- The choice between data joins in Core Spark and Spark SQL
- Techniques for getting the most out of standard RDD transformations
- How to work around performance issues in Spark’s key/value pair paradigm
- Writing high-performance Spark code without Scala or the JVM
- How to test for functionality and performance when applying suggested improvements
- Using Spark MLlib and Spark ML machine learning libraries
- Spark’s Streaming components and external community packages
It should be noted that this guide is not a beginner’s guide. Background in Scala and some Spark is desirable to get the most out of this book. Holden has done her best in explaining the nuances of writing spark code. Click below to get started buy ordering this from Amazon.
High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
To summarize the entire article, Apache Kafka and Apache Spark are well sought after tools in the data field. Machine learning, data analysis, data streaming and data science are the future currency of knowledge, decision making and much more. has become the future. The books shared above can be taken advantage of by beginners as well as advanced learners to further up their knowledge.
Time for you to explore and search for new skills has arrived. Respond to the beckoning and a few years later, you will never regret having invested to make yourself better. Thank you for reading through as you get motivated to build yourself. We appreciate your continued support and awesome readership.