PySpark Training | Pyspark course online
- 1. PySpark SQL: Basics & Queries Query Big Data with the Power of Spark SQL
- 2. Agenda What is PySpark SQL? DataFrames vs SQLContext Basic SQL Queries in PySpark Working with Tables & Views Common Query Examples Use Cases & Best Practices Hands-on Demo / Sample Code +91-96400 01789 contact@accentfuture.com
- 3. What is PySpark SQL? Component of Apache Spark for SQL-based querying Works on top of structured data (DataFrames) Allows querying using SQL or DataFrame APIs Key benefit: Combine SQL familiarity with big data scale +91-96400 01789 contact@accentfuture.com
- 4. Why Use PySpark SQL? Scalable SQL over distributed datasets Integrated with DataFrame APIs Compatible with Hive, Parquet, ORC, etc. Great for ETL, analytics, machine learning pipelines +91-96400 01789 contact@accentfuture.com
- 5. PySpark SQL Architecture Diagram: SparkSession Catalyst Optimizer Query Execution RDD → → → Explain how SQL queries get optimized and converted into execution plans +91-96400 01789 contact@accentfuture.com
- 6. Getting Started with SparkSession SparkSession is the entry point for PySpark SQL Automatically handles SQLContext and HiveContext +91-96400 01789 contact@accentfuture.com
- 7. Creating DataFrames From RDD, CSV, JSON, Parqu Preview data in tabular form +91-96400 01789 contact@accentfuture.com
- 8. Registering Temp Views Use SQL Queries like: Spark SQL treats DataFrame as SQL table +91-96400 01789 contact@accentfuture.com
- 9. Common SQL Queries SELECT, WHERE, GROUP BY, ORDER BY, LIMIT JOIN, UNION, DISTINCT +91-96400 01789 contact@accentfuture.com
- 10. Querying with DataFrame API • Equivalent to SQL but more flexible • Chainable syntax for transformations +91-96400 01789 contact@accentfuture.com
- 11. Saving Results • Write to CSV, JSON, Parquet • Partitioning and overwrite options +91-96400 01789 contact@accentfuture.com
- 12. Best Practices Use .cache() for reused queries Use .explain() to inspect query plans Avoid wide transformations where possible Prefer DataFrame over raw RDDs +91-96400 01789 contact@accentfuture.com
- 13. Real-World Use Case Example: Analyzing sales data with PySpark SQL Show query for total sales by region, top-selling products +91-96400 01789 contact@accentfuture.com
- 14. Contact Details 📧 contact@accentfuture.com 🌐 AccentFuture 📞 +91-96400 01789 PYSPARK ONLINE TRAINING +91-96400 01789 contact@accentfuture.com