Learn Apache Spark From Scratch To In-Depth
From the instructor of successful Data Engineering courses on “Big Data Hadoop and Spark with Scala” and “Scala Programming In-Depth”
- From Simple program on word count to Batch Processing to Spark Structure Streaming.
- From Developing and Deploying Spark application to debugging.
- From Performance tuning, Optimization to Troubleshooting
Contents all you need for in-depth study of Apache Spark and to clear Spark interviews.
Taught in very simple English language so any one can follow the course very easily.
No Prerequisites, Good to know basics about Hadoop and Scala
Perfect place to start learning Apache Spark
Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Speed
Run workloads 100x faster.
Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine.
Ease of Use
Write applications quickly in Java, Scala, Python, R, and SQL.
Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells.
Generality
Combine SQL, streaming, and complex analytics.
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.
Runs Everywhere
Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.
