This Spark SQL course will help you learn how to use Spark SQL to query, process, and analyze large-scale data using SQL and DataFrame APIs on Apache Spark. By the end of this Spark SQL certification course, you will be able to write efficient SQL queries, perform ETL operations, work with structured data from multiple sources, and apply Spark SQL concepts in real-world big data and data engineering scenarios.
Prerequisites
- SQL fundamentals
- Basic programming concepts
- Python, Scala, or Java familiarity
- Database and data warehousing basics
- Big data and distributed computing fundamentals
- Basic Linux/Unix commands
Course Target Audience
- IT engineers
- Data analysts
- Data scientists
- Big data beginners
- Analytics professionals
- Research professionals
- ETL developers
- Business intelligence professionals
- Reporting professionals
- Students and fresh graduates
What Will You Learn
- Apache Spark architecture fundamentals
- Distributed computing concepts
- Resilient Distributed Datasets (RDDs)
- Spark DataFrames API
- Spark Datasets API
- Running SQL and HiveQL queries in Spark
- Temporary views and catalogs
- Working with structured and semi-structured data
- Data sources: CSV, JSON, Parquet, ORC, Avro
- Hive and JDBC data integration
- SQL joins, aggregations, and subqueries
- Window functions in Spark SQL
- Catalyst optimizer and query execution
- Performance tuning and optimization techniques
- Data caching and partitioning
- ETL pipeline development
- Batch and structured streaming integration
- Spark UI monitoring
- Query plan analysis and debugging
