Title
Learning Spark: Lightningfast Data Analysis
Delivery time: 8-12 business days (International)
Data In All Domains Is Getting Bigger. How Can You Work With It Efficiently? Recently Updated For Spark 1.3, 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. This Edition Includes New Information On Spark Sql, Spark Streaming, Setup, And Maven Coordinates.Written By The Developers Of Spark, This Book Will Have Data Scientists And Engineers Up And Running In No Time. Youll Learn How To Express Parallel Jobs With Just A Few Lines Of Code, And Cover Applications From Simple Batch Jobs To Stream Processing And Machine Learning. Quickly Dive Into Spark Capabilities Such As Distributed Datasets, Inmemory Caching, And The Interactive Shell Leverage Sparks Powerful Builtin Libraries, Including Spark Sql, Spark Streaming, And Mllib Use One Programming Paradigm Instead Of Mixing And Matching Tools Like Hive, Hadoop, Mahout, And Storm Learn How To Deploy Interactive, Batch, And Streaming Applications Connect To Data Sources Including Hdfs, Hive, Json, And S3 Master Advanced Topics Like Data Partitioning And Shared Variables
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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We provide a 2-year limited warranty, from the date of purchase for all our products.
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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Learning Spark'? A: The book focuses on using Apache Spark for efficient big data analysis, covering its capabilities in handling large datasets through simple APIs in Python, Java, and Scala.
- Q: Who are the authors of 'Learning Spark'? A: The book is written by Holden Karau, one of the developers of Apache Spark, ensuring insights from experienced professionals in the field.
- Q: What edition of 'Learning Spark' is available? A: The available edition is the first edition, published on March 24, 2015.
- Q: How many pages does 'Learning Spark' contain? A: The book contains a total of 274 pages.
- Q: Is 'Learning Spark' suitable for beginners? A: Yes, 'Learning Spark' is designed to help data scientists and engineers, including beginners, get started with Apache Spark quickly and efficiently.
- Q: What programming languages are covered in 'Learning Spark'? A: The book covers programming in Python, Java, and Scala, providing a comprehensive understanding of using Spark with these languages.
- Q: Does 'Learning Spark' include information on Spark SQL and Spark Streaming? A: Yes, the book includes updated information on Spark SQL and Spark Streaming, along with setup instructions and Maven coordinates.
- Q: What are some key features of Apache Spark discussed in the book? A: Key features discussed include distributed datasets, in-memory caching, and the interactive shell, along with Spark's built-in libraries like Spark SQL and MLlib.
- Q: Can 'Learning Spark' help with machine learning applications? A: Yes, the book covers applications in machine learning, along with batch and stream processing, making it a valuable resource for those interested in these areas.
- Q: What are the data sources that can be connected to using Spark as described in the book? A: The book discusses connecting to various data sources, including HDFS, Hive, JSON, and S3, allowing for diverse data integration scenarios.