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Potential use cases for Spark extend far beyond detection of earthquakes of course. Here’s a quick (but certainly nowhere near exhaustive!) sampling of other use cases that require dealing with the velocity, variety and volume of Big Data, for which Spark is so well suited:
- Radek Ostrowski
Apache Spark use cases with code examples 1. Data Processing and ETL. Data processing and ETL (extract, transform, load) are critical components in data engineering workflows. Organizations need to extract data from various sources, transform it into a suitable format, and load it into a data warehouse or data lake for analysis. How Spark can help:
Apr 11, 2024 · Top Apache Spark use cases show how companies are using Apache Spark for fast data processing and for solving complex data problem in real time.
Option 1: Using Only PySpark Built-in Test Utility Functions ¶. For simple ad-hoc validation cases, PySpark testing utils like assertDataFrameEqual and assertSchemaEqual can be used in a standalone context. You could easily test PySpark code in a notebook session.
Mar 29, 2022 · You can easily create a test Spark Dataset/DataFrame using Scala Case Classes that match the required data structure (we call them “test data classes”). For example, if a Spark...
- Sergey Kotlov
Unit testing with Spark. To test your code in Spark, you must divide your code in at least 2 parts: domain/computation, and input/output. When your Spark transformation doesn’t have code that writes or reads to/from an outer world, it’s really easy to write unit tests:
Jun 29, 2019 · Deequ is built on top of Apache Spark hence it is naturally scalable for the huge amount of data. The best part is, you don’t need to know Spark in detail to use this library. Deequ provides features like — Constraint Suggestions — What to test. Sometimes it might be difficult to find what to test for in a particular object.
