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43347662
Learning Spark: Lightning-Fast Data Analytics, (Paperback)
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Item #:
43347662
XPF 7212
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What Stands Out
Speed Optimization
Learn how to leverage Spark’s in-memory processing for lightning-fast data analytics, significantly reducing data processing times compared to traditional extraction methods.
Comprehensive Coverage
This guide covers extensive use cases and features of Spark, making it ideal for both beginners and experienced data professionals looking to deepen their analytics skills.
Practical Examples
Includes real-world examples and hands-on exercises that allow readers to apply concepts immediately, enhancing understanding and readiness to tackle data challenges.
Product Details
- Explains why structure and unification in Apache Spark are important for data engineers and data scientists
- Covers simple and complex data analytics and machine learning algorithms
- Provides step-by-step walk-throughs, code snippets, and notebooks
- Teaches Python, SQL, Scala, or Java high-level Structured APIs
- Shows how to connect to various data sources and perform analytics on batch and streaming data using Structured Streaming
- Demonstrates how to build reliable data pipelines, develop machine learning pipelines, and productionize models using open source Delta Lake and Spark
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | August, 2020 |
| Pages | 397 |
| Reading level | Professional & Vocational |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 2nd Edition |
| Publisher | O'Reilly Media |
| Original languages | English |
| Translated from | eng |
| Language | English |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.00 x 0.90 x 9.20 in (17.8 x 2.3 x 23.4 cm) |
| Assembled product weight | 1.4 lb (640 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Data Engineers
Ideal for data engineers looking to improve performance in data processing with Apache Spark techniques and tools.
-
Data Scientists
Great for data scientists wanting to analyze large data sets effectively using Spark's advanced capabilities and features.
-
Students & Learners
Perfect for students studying data analytics, providing foundational knowledge and practical applications of Spark in real-world scenarios.
-
Complete Beginners
Not suitable for individuals without prior programming or data analytics knowledge, as it assumes some foundational understanding.
Product Description
Learning Spark: Lightning-Fast Data Analytics, (Paperback)
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Jules S Damji All Books Editorial Review
Learning Spark: Lightning-Fast Data Analytics is an insightful second edition published by O'Reilly Media in August 2020. This non-fiction paperback spans 397 pages and is tailored for professionals in the computing and data science fields. The book delves deep into data analytics, making complex concepts accessible and practical. As a resource for both novices and seasoned practitioners, it provides extensive coverage of Apache Spark, a vital tool in modern data processing. Readers appreciate its clear explanations and real-world examples that bridge the gap between theory and application, enhancing their understanding of data science methodologies.
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Pros
- Comprehensive insights into Apache Spark
- Accessible for both beginners and experts
- Clear explanations of complex concepts
- Real-world examples help understanding
- ISBN978-1-492-04319-9 for reference
Cons
- Could be challenging for absolute beginners
Product Price History
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Features & Benefits
- Learn Python, SQL, Scala, or Java high-level Structured APIs
- Understand Spark operations and SQL Engine
- Inspect, tune, and debug Spark operations with Spark configurations and Spark UI
- Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka
- Perform analytics on batch and streaming data using Structured Streaming
- Build reliable data pipelines with open source Delta Lake and Spark
- Develop machine learning pipelines with MLlib and productionize models using MLflow
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