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Item #:
56528764
In-Memory Analytics with Apache Arrow: Perform fast and efficient data analytics on both flat and hierarchical structured data
85% of respondents would recommend this to a friend
Item #:
56528764
XCD 242
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What Stands Out
High Performance
Apache Arrow enables lightning-fast data processing by utilizing in-memory analytics, allowing users to handle large datasets efficiently and reduce query execution times significantly.
Flexible Structure
Supports both flat and hierarchical data structures, making it adaptable for a wide range of data analytics tasks, enhancing usability for diverse industries and data types.
Open-Source Framework
As an open-source platform, Apache Arrow is constantly improved by a thriving community, ensuring that users benefit from continuous updates, enhancements, and a rich ecosystem of tools.
Product Details
- Process tabular data and build high-performance query engines using Apache Arrow
- Learn about Apache Arrow's data types and interoperability with pandas and Parquet
- Work with Apache Arrow Flight RPC, Compute, and Dataset APIs for tabular data
- Reviewed, contributed, and supported by Dremio, the co-creator of Apache Arrow
- Access and handle data files locally and in the cloud using Apache Arrow libraries
- Suitable for developers, data analysts, and data scientists working with tabular data
| Publisher | Packt Publishing |
| Publication date | June 24, 2022 |
| Language | English |
| Print length | 392 pages |
| ISBN-10 | 1801071039 |
| ISBN-13 | 978-1801071031 |
| Item Weight | 1.48 pounds (670 grams) |
| Dimensions | 7.5 x 0.89 x 9.25 inches (19.1 x 2.3 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists needing efficient data processing and analysis with large datasets using modern tools.
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Business Analysts
Business analysts benefit from quick data insights and real-time analytics for making informed strategic decisions.
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Developers
Developers can leverage the flexibility of Apache Arrow for building high-performance data applications and services.
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Casual Users
Not suitable for casual users who require simple data analytics without advanced capabilities or technical skills.
Product Description
In-Memory Analytics with Apache Arrow: Perform fast and efficient data analytics on both flat and hierarchical structured data
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AI & Machine Learning Editorial Review
In-Memory Analytics with Apache Arrow is a well-written and informative book that is developer-centric. It covers the entire ecosystem of in-memory analytics, and the author writes in a language that is easy to understand. The book is interactive, and readers can play around with the technology while reading. It uses diagrams, color-coding, and various programming languages to illustrate its points. The book explains complex concepts such as memory mapping, SIMD, and column-oriented data buffer in simple terms. The book contains very real-world examples that prove how applicable the technology can be. The book has a detailed walkthrough on how to contribute back to Arrow. The book needs improvement in the binding as several pages have slid out of the bindings within a few weeks of purchase.
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Pros
- Well-written and informative
- Interactive and easy to understand
- Covers the entire ecosystem of in-memory analytics
- Uses diagrams, color-coding, and various programming languages to illustrate its points
- Contains very real-world examples
- Detailed walkthrough on how to contribute back to Arrow
Cons
- Binding needs improvement as several pages have slid out of the bindings within a few weeks of purchase.
Product Price History
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XCD 242
Order now and get it around Wednesday, October 14
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Features & Benefits
- Perform fast and efficient data analytics
- Process tabular data and build high-performance query engines
- Learn about Apache Arrow's interoperability with pandas and Parquet
- Work with Arrow Flight RPC, Compute, and Dataset APIs
- Reviewed, contributed, and supported by Dremio, the co-creator of Apache Arrow
- Create useful, efficient, and powerful analytical services and utilities with Apache Arrow
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