Quest Software, Inc. (now part of Dell) announced three significant product releases today aimed at helping customers more quickly adopt Hadoop and exploit their Big Data:

• Kitenga Analytics ? Based on the recent acquisition of Kitenga,
  Quest Software now enables customers to analyze structured,
  semi-structured and unstructured data stored in Hadoop. Available
  immediately, Kitenga Analytics delivers sophisticated capabilities,
  including text search, machine learning, and advanced visualizations,
  all from an easy-to-use interface that does not require understanding
  of complex programming or the Hadoop stack itself. With Kitenga
  Analytics and the Quest Toad^®
  Business Intelligence Suite, an organization has a complete
  self-service analysis environment that empowers business and systems
  analysts across a variety of backgrounds and job roles.

• Toad for Hadoop ? Quest Software expands support for Hadoop in
  the upcoming release of Toad® for Hadoop. With more than two million
  users, and ranked No. 1 in Database Development and Optimization for
  three consecutive years by IDC [1], Toad has been enhanced to help
  database developers and DBAs bridge the gap between what they already
  know about relational database management systems and the new world of
  Hadoop. Toad will provide query and data management functionality for
  Hadoop, as well as an interface to perform data transfers using the
  Quest Hadoop Connector. Like Toad for any other platform, Toad for
  Hadoop makes the lives of developers, DBAs, and analysts easier and
  more productive.

• SharePlex with Hadoop Capabilities ? Quest Software adds Hadoop
  capabilities to the next release of SharePlex^® for Oracle,
  its robust, high-performance Oracle-to-Oracle database replication
  technology. For enterprise mission-critical systems that must always
  be available, the new release will seamlessly create multiple copies
  of Oracle data for movement simultaneously to both another Oracle
  environment and Hadoop, with no downtime. Customers can choose how
  they optimize Oracle and Hadoop environments based on data
  requirements, such as high availability; analytics and reporting;
  image and text processing; and general archiving. The architecture
  allows for scalable data distribution on-premise, in the cloud, and
  across multiple data centers without a single point of failure.