# Big data resources The following resources may help in understanding big data architecture and components: * [Pentaho Big Data Analytics Center](http://www.pentahobigdata.com/resources) * [Apache Hadoop project](http://hadoop.apache.org/) : A project that contains libraries that allows for the distributed processing of large data sets across clusters of computers using simple programming models. There are several modules, including the [Hadoop Distributed File System (HDFS)](http://wiki.apache.org/hadoop/HDFS): A distributed file system that provides high-throughput access to application data and [Hadoop MapReduce](http://wiki.apache.org/hadoop/MapReduce), which is a key algorithm to distribute work around a cluster. * [Avro](http://avro.apache.org/): A data serialization system * [Cassandra](http://cassandra.apache.org/): A scalable multi-master database with no single points of failure * [HBase](http://hbase.apache.org/): A scalable, distributed database that supports structured data storage for large tables * [Hive](http://hive.apache.org/): A data warehouse infrastructure that provides data summarization and on-demand querying * [Pig](http://pig.apache.org/): A high-level, data-flow language and execution framework for parallel computation * [ZooKeeper](http://zookeeper.apache.org/): A high-performance coordination service for distributed applications * [MongoDB](http://www.mongodb.org/): A NoSQL open source document-oriented database system developed and supported by 10gen * [Splunk](http://www.splunk.com/): A data collection, visualization and indexing engine for operational intelligence that is developed by Splunk, Inc. * [CouchDB](http://couchdb.apache.org/): A NoSQL open source document-oriented database system developed and supported by Apache * [Sqoop](http://sqoop.apache.org/): Software for transferring data between relational databases and Hadoop * [Oozie](http://oozie.apache.org/): A workflow scheduler system to manage Hadoop jobs --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.pentaho.com/install/9.3-install/use-hadoop-with-pentaho/advanced-topics/big-data-resources.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.