Depending on your use case, move existing data onto the platform or write data directly to the platform.
You can write batch data or streaming data to the MapR Converged Data Platform. Batch data refers to data that is already in a data-store while streaming data refers to the continuous flow of real-time messages that have yet to be written to a data-store. Streaming data is generally processed as it is received while batch data is processed after a set of data is written to the datastore. There are many ways to write batch and streaming data to the platform, the following sections provide a few examples.
You can use an NFS client, hadoop command, or ecosystem components to write batch data to
filesystem. Basic POSIX filesystem operations can be used to move
data to filesystem. For
example, you can use NFS clients, POSIX clients, or applications that utilize libraries such
as java.io to access the filesystem. Hadoop commands and hdfs APIs can be used to add or
update files on the filesystem. For example, you can use the
hadoop distcp command to copy data from HDFS to filesystem. Hadoop Ecosystem components, such as Apache Flume,
can also be used to push log files to filesystem.
You can also write, update, or delete batch data to HPE Ezmeral Data Fabric Database tables. Applications can use the OJAI API to write to JSON tables or the HBase API to write to binary tables.
Write streaming event data as messages in MapR Stream topics using Kafka API or a REST client application. C, Java, or Python applications can produce messages to one or more topics in a MapR Stream. Additionally, applications written in any language can use the REST Proxy to produce messages to one or more topics in a MapR Stream. For example, a financial service application, written in Java, could produce messages about stock market activity to a MapR Stream topic.