Apache Spark™ - Unified Engine for large-scale data analytics
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Overview - Spark 3.5.3 Documentation - Apache Spark
Users can also download a “Hadoop free” binary and run Spark with any Hadoop version by augmenting Spark’s classpath. Scala and Java users can include Spark in their projects using its Maven coordinates and Python users can install Spark from PyPI.
Quick Start - Spark 3.5.3 Documentation - Apache Spark
We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python. To follow along with this guide, first, download a packaged release of Spark from the Spark website.
Downloads - Apache Spark
Download Spark: spark-3.5.3-bin-hadoop3.tgz. Verify this release using the 3.5.3 signatures, checksums and project release KEYS by following these procedures. Note that Spark 3 is pre-built with Scala 2.12 in general and Spark 3.2+ provides …
FAQ - Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming ...
Configuration - Spark 3.5.3 Documentation - Apache Spark
Configures the default timestamp type of Spark SQL, including SQL DDL, Cast clause, type literal and the schema inference of data sources. Setting the configuration as TIMESTAMP_NTZ will use TIMESTAMP WITHOUT TIME ZONE as the default type while putting it as TIMESTAMP_LTZ will use TIMESTAMP WITH LOCAL TIME ZONE.
Using Spark's "Hadoop Free" Build - Spark 3.5.3 Documentation
Spark uses Hadoop client libraries for HDFS and YARN. Starting in version Spark 1.4, the project packages “Hadoop free” builds that lets you more easily connect a single Spark binary to any Hadoop version.
Documentation - Apache Spark
Spark 2.0.0 preview. The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Spark Streaming, and GraphX. In addition, this page lists other resources for learning Spark.
Security - Spark 3.5.3 Documentation - Apache Spark
Spark Security: Things You Need To Know. Security features like authentication are not enabled by default. When deploying a cluster that is open to the internet or an untrusted network, it’s important to secure access to the cluster to prevent unauthorized applications from …
Integration with Cloud Infrastructures - Spark 3.5.3 Documentation
Spark can read and write data in object stores through filesystem connectors implemented in Hadoop or provided by the infrastructure suppliers themselves. These connectors make the object stores look almost like file systems, with directories and files and the classic operations on them such as list, delete and rename.