Oracle Big Data Connectors facilitate data access between data stored in a Hadoop cluster and Oracle Database. They can be licensed for use on either Oracle Big Data Appliance or a Hadoop cluster running on commodity hardware.

These are the 5 connectors currently available:

Oracle Loader for Hadoop:
Oracle Loader for Hadoop is an efficient and high-performance loader for fast movement of data from a Hadoop cluster into a table in an Oracle database. Oracle Loader for Hadoop prepartitions the data if necessary and transforms it into a database-ready format. It optionally sorts records by primary key or user-defined columns before loading the data or creating output files. Oracle Loader for Hadoop is a MapReduce application that is invoked as a command-line utility. It accepts the generic command-line options that are supported by the org.apache.hadoop.util.Tool interface.

Oracle SQL Connector for Hadoop Distributed File System (formerly known as Oracle Direct Connector for HDFS):
Oracle SQL Connector for Hadoop Distributed File System enables Oracle Database to access data stored in Hadoop Distributed File System (HDFS) files or a Hive table. The data can remain in HDFS or the Hive table, or it can be loaded into an Oracle database. Oracle SQL Connector for HDFS is a command-line utility that accepts generic command line arguments supported by the org.apache.hadoop.util.Tool interface. It also provides a preprocessor for Oracle external tables.

Oracle R Connector for Hadoop:
Oracle R Connector for Hadoop is an R package that provides an interface between a local R environment, Oracle Database, and Hadoop, allowing speed-of-thought, interactive analysis on all three platforms. Oracle R Connector for Hadoop is designed to work independently, but if the enterprise data for your analysis is also stored in Oracle Database, then the full power of this connector is achieved when it is used with Oracle R Enterprise.

Oracle Data Integrator Application Adapter for Hadoop:
Oracle Data Integrator (ODI) extracts, transforms, and loads data into Oracle Database from a wide range of sources. In Oracle Data Integrator, a knowledge module (KM) is a code template dedicated to a specific task in the data integration process. You use ODI Studio to load, select, and configure the KMs for your particular application. More than 150 KMs are available to help you acquire data from a wide range of third-party databases and other data repositories. You only need to load a few KMs for any particular job. Oracle Data Integrator Application Adapter for Hadoop contains the KMs specifically for use with big data. They stage the data in Hive, a data warehouse built on Hadoop, for the best performance.

Oracle XQuery for Hadoop:
Runs transformations expressed in the XQuery language by translating them into a series of MapReduce jobs, which are executed in parallel on the Hadoop cluster. The input data can be located in a file system accessible through the Hadoop File System API, such as the Hadoop Distributed File System (HDFS), or stored in Oracle NoSQL Database. Oracle XQuery for Hadoop can write the transformation results to HDFS, Oracle NoSQL Database, or Oracle Database.

Individual connectors may require that software components be installed in Oracle Database and either the Hadoop cluster or an external system set up as a Hadoop client for the cluster. Users may also need additional access privileges in Oracle Database.