Big Iron for Analytics

- Data: Is growing exponentially, with newer data types
- Data Generators: There are newer streams through which data is being generated like smart devices, GPS location of users, logs etc.
- Nearly half of total sales is influenced by Digital organizations
- Nearly 20% of organizations that go digital tend to have an upper hand over competitors
- 75% of CIOs will recognize the limitations of traditional IT and embrace a leadership approach that embodies a virtuous cycle of innovation
- 40% of IT projects will create new digital services and revenue streams that monetize data
- zAnalytics is available from model Z13 and upwards of machines
- For real time analytics, additional IBM products for quick Db2 table access are needed
- Will have to find a way to store newer data types in Mainframe
Is the Big Iron capable of addressing these trends and associated requirements?
The answer is "partially yes". Depending on the specific use case, a few will be suitable for, and addressed through zAanalytics.
But first, what is zAnalytics?
It is an offering from IBM, that enables organizations to undertake analytics and machine learning on the mainframe.
Advantages of zAnalytics
- Data In-place: There is no need to move data out of mainframes as zAnalytics
- Avoids data duplication and latency
- Security: Users continue to benefit from the highest level of security that Mainframes offer
- Engine: It uses the Apache Spark engine to divide and process the data in-memory
- On-line and batch: Both online and batch workloads are supported for analytics
- Specialty engine offloading: Few of the workloads can be offloaded to the zIIP engine, which further reduces the cost of execution
ยท What are the constraints of zAnalytics?
- zAnalytics is available from model Z13 and upwards of machines
- For real time analytics, additional IBM products for quick Db2 table access are needed
- Will have to find a way to store newer data types in Mainframe
To evaluate zAnalytics, we have written a simple application
with a simple use case.
Let's explore the use
case we are looking at:
It's a simple application as follows
- Customer File: This file consists of 1 Million records consisting of information below
- Transaction file: A file containing 10MM transactions of the customer
- Use case:
- Load both data file into data frames
- Join Both data frames on Unique customer ID
- Sum the transaction amount at customer ID level
- Display top 20 users who spent the maximum
The hardware used for this test was a zTrail machine
provided by IBM. However, it is not meant for benchmarking.
zAnalytics was able
to perform this workload at a rapid pace, in close to three minutes and thirty
seconds (3:30).
Conclusion:
Rewrite your workloads in zAnalytics and witness significant
reduction in execution time and cost.
For more details contact: เว็บพนันบอล m88modernization@infosys.com
References:
[1] http://www.gartner.com/imagesrv/symposium/barcelona/docs/SYM_2012_Barcelona_BIIM_Summary.pdf
[2] https://www.idc.com/getdoc.jsp?containerId=US42256117
[3] https://www.idc.com/getdoc.jsp?containerId=US42256117
Blog authored by
Ramanath Nayak, Lead Consultant, Legacy Modernization