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we wanted to present the most comprehensive book on Apache Spark, covering all of the fundamental use cases with easy-to-run examples. Second, we especially wanted to explore the higher-level “structured” APIs that were finalized in Apache Spark 2.0—namely DataFrames, Datasets, Spark SQL, and Structured Streaming—which older books on ...
Nov 1, 2019 · According to Shaikh et al. (2019), Apache Spark is a sophisticated Big data processing tool that uses a hybrid framework.
- Spark CORE
- Spark Apis
- Spark SQL and Dataframes and Datasets
- Spark Streaming
- Spark Graphx
Spark Core is the bedrock on top of which in-memory computing, fault tolerance, and parallel computing are developed. The Core also provides data abstraction via RDDs and together with the cluster manager data arrangement over the different nodes of the cluster. The high-level libraries (Spark SQL, Streaming, MLlib for machine learning, and GraphX ...
Spark incorporates a series of application programming interfaces (APIs) for different programming languages (SQL, Scala, Java, Python, and R), paving the way for the adoption of Spark by a great variety of professionals with different development, data science, and data engineering backgrounds. For example, Spark SQL permits the interaction with R...
Apache Spark provides a data programming abstraction called DataFrames integrated into the Spark SQL module. If you have experience working with Python and/or R dataframes, Spark DataFrames could look familiar to you; however, the latter are distributable across multiple cluster workers, hence not constrained to the capacity of a single computer. S...
Spark Structured Streaming is a high-level library on top of the core Spark SQL engine. Structured Streaming enables Spark’s fault-tolerant and real-time processing of unbounded data streams without users having to think about how the streaming takes place. Spark Structured Streaming provides fault-tolerant, fast, end-to-end, exactly-once, at-scale...
GraphX is a new high-level Spark library for graphs and graph-parallel computation designed to solve graph problems. GraphX extends the Spark RDD capabilities by introducing this new graph abstraction to support graph computation and includes a collection of graph algorithms and builders to optimize graph analytics. The Apache Spark ecosystem descr...
Oct 13, 2016 · Apache Spark provides easy to use APIs for operating on large data sets across different programming languages (Scala, Java, Python and R) and with different levels of data abstraction. This makes it easier for data engineers and scientists to build data algorithms and workflows with less development efforts.
- Salman Salloum, Ruslan Dautov, Xiaojun Chen, Patrick Xiaogang Peng, Joshua Zhexue Huang
- 2016
01: Getting Started. Installation. hands-on lab: 20 min. Let’s get started using Apache Spark, in just four easy steps... spark.apache.org/docs/latest/ (for class, please copy from the USB sticks) oracle.com/technetwork/java/javase/downloads/ jdk7-downloads-1880260.html. follow the license agreement instructions.
Apache Spark was developed in 2009 at the University of California Berkeley’s AMP Lab and later open sourced as an Apache project in 2010. Apache Spark is written in Scala and provides high-level application programming interfaces (APIs) in Java, Scala, Python, and R. Note Apache Spark 1.x is written in Scala 2.10 and Apache Spark 2.x is written
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Book description. Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.
