With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Experts share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

Unlock the secrets of Big Data and AI projects. We've always had teams for managing databases, but with the data management landscape so rapidly evolving – you need to take your data teams to a whole new level.

If you're pursuing a career in data engineering or looking for ways to adapt your enterprise to the world of big data, this report shares the knowledge you need to find your way forward.

This is a practical and comprehensive guide. You will learn the basics of data engineering. Then you will learn the technologies and frameworks required to build data pipelines to work with large datasets.

This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines. It leverages Spark's amazing speed, scalability, simplicity, and versatility to build practical Big Data solutions.

With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Experts share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, it demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.

Concepts and Patterns for Streaming Services with Apache Kafka: this book explains how service-based architectures and stream processing tools such as Apache Kafka can help you build business-critical systems.

This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. It explains how these projects can help you reorient your database architecture around streams and materialized views.

Learn about the Apache Spark framework and develop Spark programs for use cases in big-data analysis. It covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.

This book takes you through the basic concepts behind Hadoop, MapReduce, Pig, and Spark. Then, through multiple examples and use cases, you'll learn how to work with these technologies by applying various Python tools.

This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala.

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, etc..

This book is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.