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Built from the ground up by Oracle, Oracle Big Data Connectors delivers a high-performance Hadoop to Oracle Database integration solution and enables optimized analysis using Oracle's distribution of open source R directly on Hadoop data.
Built from the ground up by Oracle, Oracle Big Data Connectors delivers a high-performance Hadoop to Oracle Database integration solution and enables optimized analysis using Oracle's distribution of open source R directly on Hadoop data. By providing efficient connectivity, Big Data Connectors enables analysis of all data in the enterprise - both structured and unstructured.
These are the connectors:
Enables an Oracle external table to access data stored in Hadoop Distributed File System (HDFS) files or a table in Apache Hive. The data can remain in HDFS or the Hive table, or it can be loaded into an Oracle database.
Provides 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.
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, Apache Solr, or Oracle Database. An additional XML processing capability is through XML Extensions for Hive.
A helper shell that provides a simple-to-use command line interface to Oracle Loader for Hadoop, Oracle SQL Connector for HDFS, and Copy to Hadoop (a feature of Big Data SQL). It has basic shell features such as command line recall, history, inheriting environment variables from the parent process, setting new or existing environment variables, and performing environmental substitution in the command line.
Provides a general computation framework, in which you can use the R language to write your custom logic as mappers or reducers. A collection of R packages provides predictive analytic techniques that run as MapReduce jobs. The code executes in a distributed, parallel manner using the available compute and storage resources on the Hadoop cluster. Oracle R Advanced Analytics for Hadoop includes interfaces to work with Apache Hive tables, the Apache Hadoop compute infrastructure, the local R environment, and Oracle database tables.
Extracts, loads, and transforms data from sources such as files and databases into Hadoop and from Hadoop into Oracle or third-party databases. Oracle Data Integrator provides a graphical user interface to utilize the native Hadoop tools and transformation engines such as Hive, HBase, Sqoop, Oracle Loader for Hadoop, and Oracle SQL Connector for Hadoop Distributed File System.
Provides direct, fast, parallel, secure and consistent access to master data in Oracle Database using Hive SQL, Spark SQL, as well as Hadoop APIs that support SerDes, HCatalog, InputFormat and StorageHandler.