Tedious Batch Processes to extract data out of your legacy environments
Reports that show out-dated data
Integration of data across various data source
Or wondering how big data can add value while data going into the big data environment is not current and relevant?
The value of enterprise data is undermined when it is isolated by technology platform, location or format. Organizations that rely on mainframe systems understand this problem, and typically use integration methods that extract, transform and load (ETL) mainframe data into a data warehouse to make it easier to access by applications, such as BI or analytics.
Moving data used to be the best option for integrating mainframe data, particularly when it is in a non-relational data, but this requires additional hardware, software and complex ETL scripting. The result is a copy of the original data, which is subject to change, and information that is no longer current. A more viable option is needed that provides simple, universal access to real-time data, and ideally, with less cost.
Our Data virtualization server brings together heterogeneous data without physical movement, hiding the complexity of back-end data structures behind a standard information interface. Rocket® Data Virtualization Server (DVS) is the industry’s only mainframe-resident data virtualization solution for real-time, universal access to data regardless of location or format.
The need for real time information requires a high performance data architecture that can handle the extreme volumes and unique requirements of mainframe data. Rocket DVS includes several query optimisation features such as parallel I/O and MapReduce. Multiple parallel threads handle input requests and can continually stream and buffer data to the client. Our mainframe MapReduce technology greatly reduces the elapsed time of the query by accessing the database with multiple threads that read the file in parallel.
For a business to exploit the full value of enterprise data it requires seamless, real-time access regardless of platform, location or interface. In terms of data providers, Rocket DVS supports a broad range of data stores such as:
Mainframe: Relational and non-relational databases and file structures—Ababas, DB2, IMS, VSAM and Physical Sequential. Mainframe applications and screens—CICS, IDMS, IMS, Natural.
Distributed: Databases running on Linux, Unix, Windows platforms—DB2, Oracle, Informix, Derby, and SQL Server.
Cloud and Big Data: Cloud-based relational and non-relational data, as well as No SQL databases like Mongo DB.
Rocket DVS has a wide range of connectivity options for data consumers, including ANSI 92-SQL (JDBC/ODBC), NoSQL (JSON), Services (SOAP/REST), Event Streams (XML/CBE) and HTML. Rocket DVS can support asynchronous request/reply to enable requesting applications such as mainframe Batch or programs to request data, but make the result set available to another application or federation server.
Rocket DVS expands the utilization of DB2, allowing for real time data joins on and off mainframe. Our software includes a unique mainframe-based implementation of the Distributed Relational Database Architecture (DRDA), which enables DB2 for z/OS to be joined with DB2 in other mainframe logical partitions (LPARS), or with non-relational mainframe data such as Adabas, IMS DB, VSAM, and Physical Sequential files; as well as DB2 for Linux, and Windows (LUW). This capability makes Rocket DVS a valuable addition to the IBM DB2 Analytics Accelerator providing expanded access to a wider range of data for analytics.
For many large organizations, the mainframe is still a primary platform for system of record data. A comprehensive understanding of your business, customer or market requires universal access to all data, regardless of where it is located or how it is formatted.
Rocket DVS eliminates data movement to effectively bring the analytics closer to the data, with lower costs, more agility and faster time to insight.
This solution provides your organization with ability to move away from batch processing an embrace the value of real time transaction data. We enable you to talk about real time data as input into you big data strategies that evolves a concept of “Real Time Big Data”.
Does this sound too good to be true; call us for a pilot and we will prove this is achievable.