Apache Spark In-Depth (Spark with Scala)

Apache Spark In-Depth (Spark with Scala)

What you'll learn:
Apache Spark from scratch to in-depth, starting from simple word count program to Batch Processing to Spark Structure Streaming, Performance Tuning, Optimization, Application Development and Deployment.
Completing this course will also make you ready for most interview questions
Includes Optional Project and path to success

Requirements:
No Pre-requisite required. Curiosity to learn new technology.
Good to know: Hadoop Basics and Scala Basics.
Excellent if you have completed my below 2 data engineering courses: "Big Data Hadoop and Spark with Scala" and "Scala Programming In-Depth"

Description:
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.

Who this course is for:
People looking to advance their career in Data Engineering, Big Data, Hadoop, Spark
Already working on Big Data Hadoop/ Spark and want to clear the concepts

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