Book Image

In-Memory Analytics with Apache Arrow

By : Matthew Topol
Book Image

In-Memory Analytics with Apache Arrow

By: Matthew Topol

Overview of this book

Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily. In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow’s versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio’s usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve. By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow.
Table of Contents (16 chapters)
1
Section 1: Overview of What Arrow Is, its Capabilities, Benefits, and Goals
5
Section 2: Interoperability with Arrow: pandas, Parquet, Flight, and Datasets
11
Section 3: Real-World Examples, Use Cases, and Future Development

ODBC takes an Arrow to the knee

Open Database Connectivity (ODBC) is a standardized Application Programming Interface (API) for accessing databases originally designed and built in the early 1990s. The development of ODBC intended to enable applications to be independent of their underlying database by having a standardized API to use that would be implemented by database-specific drivers. This allowed a developer to write their application and potentially easily migrate to a different database by simply specifying a different driver. In 1997, the Java Database Connectivity (JDBC) API was developed to provide a common API for Java programs to manage multiple drivers and connect either by bridging to an ODBC connection or by other types of connections, which all have different pros and cons. Almost 30 years later, these technologies are still the de facto standard way to communicate with Structured Query Language (SQL) databases.

That all being said, computing, and data in particular...