Apache Spark Training
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The Apache Spark: It is an open source processing engine that builds around the speed, the ease of use, and the analytics. This has better efficiency than MapReduce program because it can process large amounts of data, which is required to lessen the latency processing, which is quite common in the MapReduce.
Following benefits that a candidate can learn by attending the Apache Spark course:
- Know how the Spark performs at the speeds of up to 100 times faster than Map Reduce for iterative algorithms or interactive data mining.
- Know how the Spark provides the in-memory cluster computing for lightning speed and supports Java, Python, R, and Scala APIs for ease of development.
- Know how it can tackle the wide range of data processing scenarios by combining SQL, streaming and complex analytics together seamlessly in the same application.
- Know how the Spark can run on the top of the technologies like: Hadoop, Mesos, standalone, or in the cloud. Moreover, It can access various data sources likewise: HDFS, Cassandra, HBase, or S3.
- The aspirants with software development background, who want to gain acquaintance in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint.
- The software developers, who is responsible for processing the large amounts of data
- The aspirants want to learn something for a new career in data science or big data, Spark is the important part of it.
The candidates should have awareness about the fundamentals of Hadoop.
- An Introduction to Spark
- About Resilient Distributed Dataset and DataFrames
- The Spark application programming
- An Introduction to Spark libraries
- About Spark configuration, monitoring and tuning
1. An Introduction to Spark
- What is Spark and what is its purpose?
- Components of the Spark unified stack
- Resilient Distributed Dataset (RDD)
- Downloading and installing Spark standalone
- Scala and Python overview
- Launching and using Spark’s Scala and Python shell ©
2. About Resilient Distributed Dataset and DataFrames
- Understand how to create parallelized collections and external datasets
- Work with Resilient Distributed Dataset (RDD) operations
- Utilize shared variables and key-value pairs
3. The Spark application programming
- Understand the purpose and usage of the SparkContext
- Initialize Spark with the various programming languages
- Describe and run some Spark examples
- Pass functions to Spark
- Create and run a Spark standalone application
- Submit applications to the cluster
4. An Introduction to Spark libraries
- Understand and use the various Spark libraries
5. About Spark configuration, monitoring and tuning
- Understand components of the Spark cluster
- Configure Spark to modify the Spark properties, environmental variables, or logging properties
- Monitor Spark using the web UIs, metrics, and external instrumentation
- Understand performance tuning considerations