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Cloudera Developer Training for Apache Spark (CDTAS)
New Age Technologies has been delivering Authorized Training since 1996. We offer Cloudera’s full suite of authorized courses including courses pertaining to Apache Spark, Hadoop, HBase, MapReduce, Data Science, Cloudera Data Analyst and more. If you have any questions or can’t seem to find the Cloudera class that you are interested in, contact one of our Cloudera Training Specialists. Invest in your future today with Cloudera training from New Age Technologies.
✉ Cloudera Training Specialists | ☏ 502.909.0819
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Cloudera Developer Training for Apache Spark Overview:
The Cloudera Developer Training for Apache Spark hands-on course enables you to build complete, unified big data applications combining batch, streaming, and interactive analytics on all their data. With Apache Spark, developers can write sophisticated parallel applications to execute faster decisions, better decisions, and real-time actions, applied to a wide variety of use cases, architectures, and industries.
Cloudera’s Hadoop Ecosystem -> Apache Spark:
Apache Spark is the next-generation successor to MapReduce. Apache Spark is a powerful, open source processing engine for data in the Hadoop cluster, optimized for speed, ease of use, and sophisticated analytics. The Spark framework supports streaming data processing and complex, iterative algorithms, enabling applications to run up to 100x faster than traditional Hadoop MapReduce programs.
Cloudera Developer Training for Apache Spark Prerequisites:
Before attending this course, you must have the following:
- Best suited to developers and engineers
- Course examples and exercises are presented in Python and Scala, so knowledge of one of these programming languages is required
- Basic knowledge of Linux is assumed
- Prior knowledge of Hadoop is not required
Cloudera Developer Training for Apache Spark Objectives:
After successfully completing this course, you will be able to:
- Use the Spark shell for interactive data analysis
- Understand the features of Spark’s Resilient Distributed Datasets
- Understand how Spark runs on a cluster
- Use parallel programming with Spark
- Write Spark applications
- Process streaming data with Spark
Cloudera Developer Training for Apache Spark Outline:
Module 1: Why Spark?
- Problems with Traditional Large-Scale Systems
- Introducing Spark
Module 2: Spark Basics
- What is Apache Spark?
- Using the Spark Shell
- Resilient Distributed Datasets (RDDs)
- Functional Programming with Spark
Module 3: Working with RDDs
- RDD Operations
- Key-Value Pair RDDs
- MapReduce and Pair RDD Operations
Module 4: The Hadoop Distributed File System
- Why HDFS?
- HDFS Architecture
- Using HDFS
Module 5: Running Spark on a Cluster
- Overview
- A Spark Standalone Cluster
- The Spark Standalone Web UI
Module 6: Parallel Programming with Spark
- RDD Partitions and HDFS Data Locality
- Working With Partitions
- Executing Parallel Operations
Module 7: Caching and Persistence
- RDD Lineage
- Caching Overview
- Distributed Persistence
Module 8: Writing Spark Applications
- Spark Applications vs. Spark Shell
- Creating the SparkContext
- Configuring Spark Properties
- Building and Running a Spark Application
- Logging
Module 9: Spark, Hadoop, and the Enterprise Data Center
- Overview
- Spark and the Hadoop Ecosystem
- Spark and MapReduce
Module 10: Spark Streaming
- Spark Streaming Overview
- Example: Streaming Word Count
- Other Streaming Operations
- Sliding Window Operations
- Developing Spark Streaming Applications
Module 11: Common Spark Algorithms
- Iterative Algorithms
- Graph Analysis
- Machine Learning
Module 12: Improving Spark Performance
- Shared Variables: Broadcast Variables
- Shared Variables: Accumulators
- Common Performance Issues
