Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
In-Memory Analytics with Apache Arrow

You're reading from   In-Memory Analytics with Apache Arrow Perform fast and efficient data analytics on both flat and hierarchical structured data

Arrow left icon
Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801071031
Length 392 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Matthew Topol Matthew Topol
Author Profile Icon Matthew Topol
Matthew Topol
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Overview of What Arrow Is, its Capabilities, Benefits, and Goals
2. Chapter 1: Getting Started with Apache Arrow FREE CHAPTER 3. Chapter 2: Working with Key Arrow Specifications 4. Chapter 3: Data Science with Apache Arrow 5. Section 2: Interoperability with Arrow: pandas, Parquet, Flight, and Datasets
6. Chapter 4: Format and Memory Handling 7. Chapter 5: Crossing the Language Barrier with the Arrow C Data API 8. Chapter 6: Leveraging the Arrow Compute APIs 9. Chapter 7: Using the Arrow Datasets API 10. Chapter 8: Exploring Apache Arrow Flight RPC 11. Section 3: Real-World Examples, Use Cases, and Future Development
12. Chapter 9: Powered by Apache Arrow 13. Chapter 10: How to Leave Your Mark on Arrow 14. Chapter 11: Future Development and Plans 15. Other Books You May Enjoy

Lost in translation

Even when systems speak the same standard protocol, there might be a whole bunch of translations and copies happening under the hood. ODBC, for all its benefits, was still designed during a time when it was much more common to be requesting wide tables with large numbers of columns and fewer rows as compared to modern data analysis. While it enabled connectivity between different disparate systems, there's still a lot of translating and copying that has to happen in the ODBC drivers for everything to work correctly. Figure 3.1 shows a comparison between a standard data science workflow using typical ODBC or JDBC and using the Arrow-JDBC adapter.

cLook first at the left side of Figure 3.1, the typical case when using JDBC. There are three points where data has to be translated between formats, as follows:

  1. First, data is translated inside the JDBC/ODBC driver from whatever format the database speaks natively into the JDBC/ODBC standards...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime