20 docs tagged with "RDD Transformations"

Learn how to write complex SQL queries in PySpark, including joins, subqueries, aggregations, and window functions for advanced analytics in Databricks.

Learn how to create PySpark DataFrames from CSV, JSON, Parquet files, and Hive tables using Databricks and Spark best practices.

Learn how to perform data aggregation in PySpark using groupBy, agg, max, sum, avg, distinct, and sorting operations with real shipment dataset examples.

Learn how to write and run your first PySpark job with a hands-on “Hello World” example, and understand the end-to-end workflow in Spark.

Learn how to handle missing data in PySpark DataFrames using drop, fill, and replace operations with practical Databricks examples.

Learn all techniques for handling missing or null data in PySpark DataFrames including dropping nulls, filling values, conditional replacement, and computing statistics.

Step-by-step guide to install PySpark, set up your development environment, and run your first Spark job for big data processing.

Learn why PySpark is a leading framework for big data processing, its importance in modern data engineering, and how it enables fast, scalable analytics.

Learn how to use key-value RDDs in Spark with reduceByKey, groupByKey, and aggregate operations, complete with real-world Databricks examples and performance tips.

Imagine you’re a data scientist in a high-tech lab, not just a data engineer. Data isn’t sitting quietly in files—it’s streaming, growing, and changing constantly. You want to predict outcomes, classify users, or group behaviors, all at scale.

Understand the performance differences between PySpark DataFrame API and Spark SQL, with tips on when to use each approach for optimal performance in Databricks.

Understand the PySpark architecture, including Driver, Executor, and cluster modes, to efficiently design distributed data processing workflows.

Learn the fundamentals of RDDs in Apache Spark, including how to create them, apply transformations, trigger actions, and understand their importance in distributed data processing.

Learn how Spark RDD caching and persistence work, why they matter for performance, and how to manage memory effectively in distributed data pipelines.

Understand the differences between RDDs, DataFrames, and Datasets in PySpark, and learn when to use each for efficient big data processing.

Learn the core components of PySpark—SparkSession, SparkContext, and configurations—and how they form the foundation of big data processing.

Learn how to create and use PySpark UDFs (User Defined Functions) and UDAFs (User Defined Aggregate Functions) to implement custom logic and aggregations in Spark SQL and DataFrames.

Learn how to use Spark SQL in PySpark by registering temporary views and running SQL queries on DataFrames in Databricks.

Learn how to use PySpark window functions for ranking, running totals, cumulative sums, and time-based analytics in Databricks.

Complete guide to date and timestamp operations in PySpark, including extracting date components, aggregations, ratios, and SQL queries with real examples.