Companies
have been making business decisions for decades based on
transactional data stored in relational databases. Beyond that
critical data, is a potential treasure trove of less structured data:
weblogs, social media, email, sensors, and photographs that can be
mined for useful information.
Oracle
offers a broad integrated portfolio of products to help you acquire
and organize these diverse data sources and analyze them alongside
your existing data to find new insights and capitalize on
hidden relationships.
Oracle
Big Data Connectors Downloads here, includes:
Oracle
SQL Connector for Hadoop Distributed File System Release 2.1.0
Oracle
Loader for Hadoop Release 2.1.0
Oracle
Data Integrator Companion 11g
Oracle
R Connector for Hadoop v 2.1
Oracle
Big Data Documentation
The
Oracle Big Data solution offers an integrated portfolio of products
to help you organize and analyze your diverse data sources alongside
your existing data to find new insights and capitalize on hidden
relationships.
Oracle
Big Data, Release 2.2.0 - E41604_01 zip (27.4 MB)
Integrated
Software and Big Data Connectors User's Guide HTML PDF
Oracle
Data Integrator (ODI) Application Adapter for Hadoop
Apache
Hadoop is designed to handle and process data that is typically from
data sources that are non-relational and data volumes that are beyond
what is handled by relational databases. Typical processing in Hadoop
includes data validation and transformations that are programmed as
MapReduce jobs.
Designing
and implementing a MapReduce job usually requires expert programming
knowledge. However, when you use Oracle Data Integrator with the
Application Adapter for Hadoop, you do not need to write MapReduce
jobs. Oracle Data Integrator uses Hive and the Hive Query Language
(HiveQL), a SQL-like language for implementing MapReduce jobs.
Employing familiar and easy-to-use tools and pre-configured knowledge
modules (KMs), the application adapter provides the following
capabilities:
Loading
data into Hadoop from the local file system and HDFS
Performing
validation and transformation of data within Hadoop
Loading
processed data from Hadoop to an Oracle database for further
processing and generating reports
Oracle
Database 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. It pre-partitions the data if necessary and transforms it
into a database-ready format. Oracle Loader for Hadoop is a Java
MapReduce application that balances the data across reducers to help
maximize performance.
Oracle
R Connector for Hadoop
Oracle
R Connector for Hadoop is a collection of R packages that provide:
Interfaces
to work with Hive tables, the Apache Hadoop compute infrastructure,
the local R environment, and Oracle database tables
Predictive
analytic techniques, written in R or Java as Hadoop MapReduce jobs,
that can be applied to data in HDFS files
You
install and load this package as you would any other R package.
Using simple R functions, you can perform tasks such as:
Access
and transform HDFS data using a Hive-enabled transparency layer
Use
the R language for writing mappers and reducers
Copy
data between R memory, the local file system, HDFS, Hive, and
Oracle databases
Schedule
R programs to execute as Hadoop MapReduce jobs and return the
results to any of those locations
Oracle
SQL Connector for Hadoop Distributed File System
Using
Oracle SQL Connector for HDFS, you can use an Oracle Database to
access and analyze data residing in Hadoop in these formats:
Data
Pump files in HDFS
Delimited
text files in HDFS
Hive
tables
For
other file formats, such as JSON files, you can stage the input in
Hive tables before using Oracle SQL Connector for HDFS. Oracle SQL
Connector for HDFS uses external tables to provide Oracle Database
with read access to Hive tables, and to delimited text files and Data
Pump files in HDFS.
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Cloudera's
Distribution Including Apache Hadoop Library HTML
Oracle
R Enterprise HTML
Oracle
NoSQL Database HTML
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