
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Last Releases
- v3.5.6Preparing Spark release v3.5.6-rc1 Source: https://github.com/apache/spark/releases/tag/v3.5.6
- v4.0.0Preparing Spark release v4.0.0-rc7 Source: https://github.com/apache/spark/releases/tag/v4.0.0
- v3.5.5Preparing Spark release v3.5.5-rc1 Source: https://github.com/apache/spark/releases/tag/v3.5.5
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Common Issues When Installing Apache Spark Locally and How to Resolve Them
Installing Apache Spark locally is essential for data engineers and developers aiming to test configurations, build data pipelines, and experiment with Spark’s rich features on a personal system. However, getting Spark up and running can be tricky due to dependencies, environment configurations, and compatibility issues. This article identifies some common installation problems for Apache Spark…
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How to Install Apache Spark Locally: A Step-by-Step Guide
Apache Spark is a powerful, open-source analytics engine known for its speed and scalability, particularly for big data processing. Spark supports multiple programming languages, making it accessible for developers working in Python, Java, Scala, and R. While Spark is often deployed on clusters, a local installation can be invaluable for testing, development, and offline work.…
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Apache Spark – A Comprehensive Guide to the Powerhouse of Data Processing
Introduction to Apache Spark Apache Spark has become a staple in the world of big data, known for its powerful processing capabilities and ease of use across various data-intensive applications. Originally developed at UC Berkeley’s AMPLab, Spark was introduced as a solution to the limitations of Hadoop MapReduce, offering significant improvements in speed, scalability, and…
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AWS Glue 5.0
AWS Glue 5.0 is generally available, offering improved performance, enhanced security, and support for Amazon Sagemaker Unified Studio and Sagemaker Lakehouse. It upgrades engines to Apache Spark 3.5.2, Python 3.11, and Java 17, and adds support for Apache Hudi, Iceberg, and Delta Lake. Source: https://aws.amazon.com/about-aws/whats-new/2024/12/aws-glue-5-0/