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- Data
Big Data with PySpark
5 Courses
4 Labs
11 Hours
Skill IQ
The Big Data with PySpark learning path equips learners with the skills to process, transform, and analyze large datasets efficiently. This path covers data ingestion, ETL workflows, query optimization, and distributed machine learning using PySpark DataFrames, SQL, MLlib, and Structured Streaming. By mastering performance tuning and real-time processing, learners can build scalable data pipelines for analytics, machine learning, and big data applications.
Content in this path
Big Data with PySpark
Watch the following courses to start your big data with PySpark learning journey.
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What You'll Learn
- How to perform big data analytics with PySpark
- How to build ETL pipelines with PySpark
- How to perform scalable machine learning with PySpark
- How to perform real-time stream processing with PySpark
- How to build recommendation systems with Pyspark
Prerequisites
- Learners interested in this path should have a solid understanding of Python programming, SQL, and basic data manipulation using pandas. Familiarity with distributed computing concepts and cloud or big data tools (e.g., Hadoop, Spark, or databases like PostgreSQL) is helpful but not required.
Related topics
- Apache Spark
- PySpark
- SQL
- MLlib
- ETL
- Big Data
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