Management by magazine supports big data with the latest Harvard Business Review putting big data on its cover so every CEO can ask the CIO about their big data initiatives. I have experienced firsthand how big data can provide deep business insights and I discussed the business gains at IOD in my “Agile Big Data . . . → Read More: Big Data: IBM Addresses Big Data with the New PureSystems
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Big data continues to be all the rage for government, private and public companies as the new vendors scramble to make their products work and others rebrand last year’s to handle bigger data than their competitors. The problem is that new and old vendors are still missing the scalability or performance mark for making big . . . → Read More: Big Data: Managing by Your Management Expectations There exists a wide variety of performance tuning techniques for optimizing any type of application. Java performance with all of its intricacies presents more challenges. Lies, damn lies, and Java performance statistics truly need to be gathered many different ways to validate your performance. The following three key areas in Java performance can really help . . . → Read More: Java Performance: Three Java Performance Testing Considerations for Architects Since I am president of the local Washington DC DAMA National Capital Region (NCR), I was fortunate to put the agenda together for our annual DAMA Day. Our DAMA Day this year featured the topic of Big Data and I was fortunate to convince Jeff Jonas to keynote at our DAMA Day event. Jeff and . . . → Read More: Big Data: Jeff Jonas Provides More Insights at DAMA-NCR Building a big data warehouse using a Union All View (UAV) table with billions of rows is only the beginning. There has to be a DB2 system that is enabled so the data warehouse can have SQL optimized automatically and queries run efficiently. Hopefully each of the critical DB2 DSNZPARM settings was reviewed during your . . . → Read More: DB2 DSNZPARMS: How to Get DSNZPARM Settings with Data Studio to Improve Big Data Performance Building a big data warehouse with billions of rows always needs an extra bit of attention to get the best performance out of the database processing. Application programming is always interesting as the efficiency of the code and the DB2 data types handled by the application code are critical for data quality and integrity. With . . . → Read More: Big Data Analytics: DB2 Data Types When building a large data warehouse with billions of rows any extra processing is magnified exponentially. Any inefficiency quickly becomes a bigger performance problem as it is multiplied by each of the billions of rows. To squeeze every performance drop out the big data standard, centralized processes or built-in functionality always helps the team focus . . . → Read More: Big Data Analytics: Leveraging DB2 Functions The discussion continues about the building of a petabyte table by putting multiple tables under the definition of a UNION ALL View. This provides unlimited growth potential for the logical big data table to all types of regular and analytical users. Sometime you want to have uniqueness across the entire petabyte table and this can . . . → Read More: Big Data Analytics: Union ALL View — Uniqueness through INSTEAD Triggers Last week’s example built a petabyte table by putting multiple tables under the definition of a UNION ALL View. This tactic provides unlimited growth potential for the logical big data table to all types of regular and analytical users. Unfortunately, as some commented, this UNION ALL view may need help to get performance in DB2. . . . → Read More: Big Data Analytics: Union ALL View Petabyte Performance There are many companies that are realizing business insights through new big data analytics. Many are finally looking at data that they only once dreamed of analyzing because of the continuing decline in the cost of storage and CPU as well as the availability of cloud computing resources. The amount of data and the number . . . → Read More: Big Data Analytics: Union ALL View Building a Petabyte Table |    | ||
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