Step 1: Select a Data Storage Format

Consider the data format options and determine how you want to use to store your data.

Keep in mind that a single application can access data from a variety of data formats. The following data formats are available.


MapR-FS is a random read-write distributed file system that allows applications to concurrently read and write directly to files. This data store is great for storing and scanning large data sets of historical data, and for sharing files between various services and applications. Any node with access to the MapR-FS can access files on the file system.

Consider the following examples:
  • Write large amounts of user click-stream data for a web site in a simple directory structure based on the date, and then process that data using tools like Spark, Drill, Hive or another MapReduce application.
  • Store various types of images, audio files, and video files in one shared directory so that web or mobile applications can render the content as required.
  • Share configuration files or internationalized resources among various applications by storing these files in a shared directory.
  • Simplify the deployment of new applications by adding java libraries (.jar files) to a shared directory and then including the directory in the classpath of one or more applications.
  • Store the docker files and images in a shared location which can be accessed by various servers. This provides a single, shared location from which users can launch containers.

When you store large data sets, use a file format in which the data can be consumed efficiently. For example, Parquet, ORC, sequence files are good for storing and scanning. Parquet, in particular, is great for storing data on the MAPR-FS as it stores data in columnar format which can be partitioned. Parquet also works well for use cases where you query the data with Drill or process the data with Spark applications. Note that you can use CSV or JSON formats, but they are less efficient when your intention is to scan the data.

For more information about MapR-FS, see MapR-FS


MapR-DB is an enterprise-grade, high performance, NoSQL database management system that supports both binary and JSON tables. Consider using MapR-DB tables when you want to query and organize large amounts data. It also integrates with Drill, Apache Spark, Hive and other MapReduce tools to provide applications the ability to scan or query large data sets in an efficient, distributed way.

MapR-DB provides the following features:
  • A flexible schema. Each row or document can have its own set of attributes.
  • Efficient random access. Applications can quickly access one or more records using a row key, document ID, or a conditional queries.
  • Easy and efficient data mutation. Applications can insert, update, and delete rows or documents.
MapR-DB Binary Tables
MapR-DB binary tables consist of rows that are identified by primary keys and row data is identified by key/value pairs. MapR-DB tables are similar to HBase tables in that MapR-DB does not determine or store the datatype of each value in the table. But, MapR-DB tables perform operations more efficiently than HBase table. You might want to use binary tables when you want to create or use an existing HBase application. However, on the Converged Data Platform, JSON tables are usually preferred due to their flexibility.
MapR-DB JSON Tables
A MapR-DB JSON tables provide a flexible, powerful schema that you can customize based on the data that you want to represent. Each row in a JSON table corresponds to an JSON document with an unique _id and each JSON document can have a different set of columns. MapR-DB JSON tables determine the datatype of each value based on the type of data written to the document.
The following example lists three JSON documents from a single JSON table. Note that the attributes associated with each document varies.

For more information, see MapR-DB


MapR-ES is a publish/subscribe messaging solution that uses the Apache Kafka API. MapR-ES writes events as messages in a topic and topics are part of a stream. Producer applications can publish events to a stream and consumer applications can read all or a subset of the messages in a stream. By default, messages are stored into a topic for 7 days and after that point they are automatically purged by MapR. However, you can shorten or extend the time-to-live (ttl) for messages in a stream based on your use case.

For more information, see MapR-ES.