By Ajay N.R.
In today's digital world, organizations are heavily investing funds on the research of Social Media Analytics. This could be due to social media applications easily generate 10+ terabytes of data every day. The outcome of this data analysis plays a vital role in taking key business decisions. To summarize - any organization that can bring out intelligence out of raw data gets the edge over other competitors.
Interesting Points to Ponder
Although not authenticated, talk of the town is Mainframe servers that hold world's 70%-80% of the data. This may be because Mainframes are extensively used in Retail, Insurance and Banking & Finance domains. Further, CICS handles 30 billion transactions per day.
(Source: CICS Transaction Server Application Architecture, IBM Redbook)
Clearly, when it comes to data, Mainframe is one of the key players. But, why is Analytics an unexplored area when it comes to Mainframes? This blog explores the possible areas for applying business analytics for Mainframe Applications.
Use of Analytics for Mainframes
Most of the data on Mainframe is stored in a structured format – rational database and file system. Organizations would have already invested in building intelligence out of this data. These could be applications engineered in-house or commercial products licensed from several vendors. Having said this, Mainframes also generate vast amount of unstructured data in the following areas:
Core Insights
Players in the Industry
zDoop – The commercial version of Hadoop is specifically designed to be deployed on Mainframe. This means that data on Mainframe will continue to remain on a Legacy system, but allows us to perform various BI activities. This is very critical because data security is one of the key features of Mainframe and any compromise in this area will not accepted by the Mainframe application owner. Another feature provided by zDoop is vStorm Connect. This provides a graphical representation of the source and target location wherein users can drag-and-drop the data from zOS to Hadoop. The tool also takes care of the character conversion during the data movement (Example: EBCIDIC conversion) and handles the data movement of:
Conclusion
In today's digital world, organizations are heavily investing funds on the research of Social Media Analytics. This could be due to social media applications easily generate 10+ terabytes of data every day. The outcome of this data analysis plays a vital role in taking key business decisions. To summarize - any organization that can bring out intelligence out of raw data gets the edge over other competitors.
Interesting Points to Ponder
Although not authenticated, talk of the town is Mainframe servers that hold world's 70%-80% of the data. This may be because Mainframes are extensively used in Retail, Insurance and Banking & Finance domains. Further, CICS handles 30 billion transactions per day.
(Source: CICS Transaction Server Application Architecture, IBM Redbook)
Clearly, when it comes to data, Mainframe is one of the key players. But, why is Analytics an unexplored area when it comes to Mainframes? This blog explores the possible areas for applying business analytics for Mainframe Applications.
Use of Analytics for Mainframes
Most of the data on Mainframe is stored in a structured format – rational database and file system. Organizations would have already invested in building intelligence out of this data. These could be applications engineered in-house or commercial products licensed from several vendors. Having said this, Mainframes also generate vast amount of unstructured data in the following areas:
- Application Log
- System Log
- User Log
This data may contain valuable information that can provide insights into application behavior.
Core Insights
Players in the Industry
zDoop – The commercial version of Hadoop is specifically designed to be deployed on Mainframe. This means that data on Mainframe will continue to remain on a Legacy system, but allows us to perform various BI activities. This is very critical because data security is one of the key features of Mainframe and any compromise in this area will not accepted by the Mainframe application owner. Another feature provided by zDoop is vStorm Connect. This provides a graphical representation of the source and target location wherein users can drag-and-drop the data from zOS to Hadoop. The tool also takes care of the character conversion during the data movement (Example: EBCIDIC conversion) and handles the data movement of:
- DB2
- VSAM
- QSAM
- SMF, RMF
- Log files
Architecture for zDoop
Conclusion
By analyzing, the log files can be very beneficial to business. This technique may not help in growing the business, but will certainly help in identifying and isolating the problems in the current application. In Mainframes, where run-time costs are critical, this technique can help in the saving costs due to application failures. The equation "$ saved = $ earned" certainly holds good in this situation.
No comments:
Post a Comment