Learn Apache Spark with Python
- 8 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Get your team access to Udemy's top 3,000+ courses anytime, anywhere.Try Udemy for Business
- Introduction to Pyspark
Install and run Apache Spark on a desktop computer or on a cluster
- Understand how Spark SQL lets you work with structured data
- Understanding Spark with Examples and many more
- Basic Knowledge of Big Data
- Basics of Java and OOPs
- Basic Computer Programming Terminologies
Apache Spark is the hottest Big Data skill today. More and more organizations are adapting Apache Spark for building their big data processing and analytics applications and the demand for Apache Spark professionals is sky rocketing. Learning Apache Spark is a great vehicle to good jobs, better quality of work and the best remuneration packages.
You might already know Apache Spark as a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It’s well-known for its speed, ease of use, generality and the ability to run virtually everywhere. And even though Spark is one of the most asked tools for data engineers, also data scientists can benefit from Spark when doing exploratory data analysis, feature extraction, supervised learning and model evaluation.
The course will cover many more topics of Apache Spark with Python including-
What makes Spark a power tool of Big Data and Data Science?
Learn the fundamentals of Spark including Resilient Distributed Datasets, Spark Actions and Transformations
Explore Spark SQL with CSV, JSON and mySQL (JDBC) data sources
Convenient links to download all source code
- Java Developers who want to upgrade their skills to light weight language python to handle Big data.
- Hadoop developers who want to learn a fast processing engine SPARK
- Python developers who want to upgrade their skills to handle and process Big data using Apache Spark.
- Any professionals or students who want to learn Big data.