Dive into efficient data handling with 'In-Memory Analytics with Apache Arrow.' This book explores Apache Arrow, a powerful open-source project that revolutionizes how tabular and hierarchical data are processed. You'll learn to streamline data pipelines, accelerate analysis, and utilize high-performance tools for data exchange. What this Book will help me do Understand and utilize the Apache Arrow in-memory data format for your data analysis needs. Implement efficient and high-speed data pipelines using Arrow subprojects like Flight SQL and Acero. Enhance integration and performance in analysis workflows by using tools like Parquet and Snowflake with Arrow. Master chaining and reusing computations across languages and environments with Arrow's cross-language support. Apply in real-world scenarios by integrating Apache Arrow with analytics systems like Dremio and DuckDB. Author(s) Matthew Topol, the author of this book, brings 15 years of technical expertise in the realm of data processing and analysis. Having worked across various environments and languages, Matthew offers insights into optimizing workflows using Apache Arrow. His approachable writing style ensures that complex topics are comprehensible. Who is it for? This book is tailored for developers, data engineers, and data scientists eager to enhance their analytic toolset. Whether you're a beginner or have experience in data analysis, you'll find the concepts actionable and transformative. If you are curious about improving the performance and capabilities of your analytic pipelines or tools, this book is for you.

ABOUT THE TALK: In this talk, we'll dive into one of the newest Apache Arrow subprojects, Arrow Database Connectivity (ADBC), an API specification for Arrow-based database access.

Over the course of this session, you’ll get a crash course in ADBC and learn how it communicates with different data APIs (like Arrow Flight SQL and Postgres) using Arrow-native in-memory data. By the end, you’ll understand the use cases it can conquer and know where to access the resources you need to get started.

This talk will cover goals, use-cases, and examples of using ADBC to communicate with different Data APIs (such as Flight SQL or postgres) with Arrow Native in-memory data.

ABOUT THE SPEAKER: Matthew Topol is a committer for the Apache Arrow project, frequently enhancing the Golang Arrow and Parquet libraries among other enhancements and helping to grow the Arrow Community. Recently, Matt has joined Voltron Data in order to work on the Apache Arrow libraries full time and grow the Arrow Golang community. In June 2022, Matt's first book was published, which is the first (and currently only) book on Apache Arrow titled "In-Memory Analytics with Apache Arrow".

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Discover the power of in-memory data analytics with "In-Memory Analytics with Apache Arrow." This book delves into Apache Arrow's unique capabilities, enabling you to handle vast amounts of data efficiently and effectively. Learn how Arrow improves performance, offers seamless integration, and simplifies data analysis in diverse computing environments. What this Book will help me do Gain proficiency with the datastore facilities and data types defined by Apache Arrow. Master the Arrow Flight APIs to efficiently transfer data between systems. Learn to leverage in-memory processing advantages offered by Arrow for state-of-the-art analytics. Understand how Arrow interoperates with popular tools like Pandas, Parquet, and Spark. Develop and deploy high-performance data analysis pipelines with Apache Arrow. Author(s) Matthew Topol, the author of the book, is an experienced practitioner in data analytics and Apache Arrow technology. Having contributed to the development and implementation of Arrow-powered systems, he brings a wealth of knowledge to readers. His ability to delve deep into technical concepts while keeping explanations practical makes this book an excellent guide for learners of the subject. Who is it for? This book is ideal for professionals in the data domain including developers, data analysts, and data scientists aiming to enhance their data manipulation capabilities. Beginners with some familiarity with data analysis concepts will find it beneficial, as well as engineers designing analytics utilities. Programming examples accommodate users of C, Go, and Python, making it broadly accessible.