List of Pages in Category Spark (51 pages)
Spark
Apache Spark is a general framework for distributed computing that offers high performance for both batch and interactive processing. It exposes APIs for Java, Python, and Scala and consists of Spark core and several related projects.
- Spark SQL - Module for working with structured data. Allows you to seamlessly mix SQL queries with Spark programs.
- Spark Streaming - API that allows you to build scalable fault-tolerant streaming applications.
- MLlib - API that implements common machine learning algorithms.
- GraphX - API for graphs and graph-parallel computation.
Cloudera supports Spark core, Spark SQL (including DataFrames), Spark Streaming, and MLlib. Cloudera does not currently offer commercial support for GraphX or SparkR.
*
A
- Accessing Avro Data Files From Spark SQL Applications
- Accessing Data Stored in Amazon S3 through Spark
- Accessing Data Stored in Azure Data Lake Store (ADLS) through Spark
- Accessing External Storage from Spark
- Accessing Parquet Files From Spark SQL Applications
- Apache Spark Overview
)B
- Batch Indexing Using Cloudera Search
- Building and Running a Crunch Application with Spark
- Building Spark Applications
C
- Configuring ADLS Gen1 Connectivity
- Configuring ADLS Gen2 Connectivity
- Configuring Google Cloud Storage Connectivity
- Configuring Spark Applications
D
F
H
I
J
M
- Managing Spark
- Managing Spark Using Cloudera Manager
- Managing the Spark History Server
- Monitoring Spark Applications
N
P
R
- Running Apache Hive on Spark in CDH
- Running Spark Applications
- Running Spark Applications on YARN
- Running Spark Python Applications
- Running Your First Spark Application
S
- Setting up an End-to-End Data Streaming Pipeline
- Spark (Standalone) Metrics
- Spark and Hadoop Integration
- Spark and IPython and Jupyter Notebooks
- Spark Application Overview
- Spark Authentication
- Spark Encryption
- Spark Guide
- Spark Indexing
- Spark Metrics
T
- Troubleshooting for Spark
- Tuning Apache Hive on Spark in CDH
- Tuning Apache Spark Applications
- Tuning Apache Spark Applications
U