Businesses with expansive operations, and that are diverse in nature, generate all kinds of information (structured / unstructured) that is stored in multiple locations and databases.

That means, the availability of precious information i.e. business insights that may be hidden in the huge volumes of data, becomes scarce and untimely (even if available) for the managements to make sense of it, take informed decisions, and act upon it.

However, lack of data readiness i.e. data preparation, cleansing / deduplication, sorting, and scrubbing to make the data ready for analysis, is often the major problem faced by any big-data analytics project.

Getting to that data-ready state for analytics is a cumbersome as well as costly task; entailing good amount of efforts, resources, and time.

But, you need not worry anymore as our Hortonworks Certified and multi-functional data ingestion and analytics solution, HCube, is here to address this burning aspect of analytics.

Its configuration-driven architecture helps it performs the role of a readymade Hadoop connector for several types of databases. Thus, ensuring data-readiness by accelerating data ingestion, and achieving simplified and timely analytics for you.

Differentiators of HCube:

  • Configuration-driven data ingestion and analytics
  • Readymade connector with ~100 data sources (Databases, In-memory grids, messaging solutions, event streams, data warehouses, etc.)
  • OOTB integration with mobile, cloud, and IoT
  • Standards-based solution, OOB templates, and best practices
  • Incorporates the AngularJS UI
  • Deployment Options
    1. Packaged solution for Cloudera, Hortonworks and Vanila Hadoop platforms
    2. Docker Containers with packaged solution for scalability
  • Managed Services (with choice of Hadoop Vendors)
    1. On-Premises
    2. Cloud (Private / Public / Hybrid)

Features of HCube:

  • Seamlessly integrates your business data with
    1. On-premises databases and data warehouses like Oracle, SQL Server, MySQL, and DB2
    2. In-memory data-bases like SAP HANA, etc.
    3. Messaging & event streams like HTTP, JMS, etc.
    4. Relational databases like Teradata and Netezza
    5. NoSQL data-stores like Mongo DB and Cassandra
  • Extracts table data from source database servers to HDFS, Hive, and HBase, and allows conditional transfer
  • Allows batch-wise job execution
  • Built-in job scheduler and change data capture (CDC) features
  • Easy integration with any reporting tool
  • Security capabilities via role based user authentication and authorization


Benefits to your business:

  • Cost efficient Reduced cap-ex and op-ex as data gets easily moved from data sources to Hadoop.
  • Accelerated data ingestion Increased rate of data availability in Hadoop for analytics.
  • Easy cross-platform integration Hassle-free integration of many types of data sources (In-memory, relational, and NoSQL databases) with Hadoop-based systems – HDFS, Hive, and Hive.
  • Higher management visibility (Reports & dashboards)
  • Easy to use for Hadoop and other database users.

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