There are many new enhancements in DB2 11 for z/OS, and some of the most important ones are the ones that help data management handle the schema chaos within application development. Since the colleges are not truly teaching data management, modeling, or standard design normalization practices anymore, development constantly changes the data elements within the . . . → Read More: 3 Ways DB2 11 Helps You Handle Schema Chaos
This year DB2 celebrates 30 years of helping clients. The IDUG conferences are always great venues to connect with colleagues from around the world. The stories about companies, projects, and tools are very valuable to understand the truth, the hype, what is working, and how it is being done. The following three topics seem to . . . → Read More: IDUG Europe 2013 – 4 Topics on Everyone’s Mind
The race is on among all the vendors to lock your new Big Data project in to their architecture, platform, and support contracts. All the open source Hadoop derivatives have boiled down to the leaders of Cloudera Impala, Teradata SQL-H, EMC Pivotal (formerly GreenPlum) MongoDB, and one of best–IBM’s BigSQL.
All of these vendor’s solutions . . . → Read More: 5 Reasons Why DB2 Is Still the Best Big Data Solution
Big Data disaster recovery is a big issue. Of course, any Big Data business sponsor vows that the entire Big Data database is necessary, and demands it be included in the disaster recovery plans. This requirement makes getting a Big Data disaster recovery sync point and understanding the intimate Big Data processing transaction details to . . . → Read More: Big Data Disaster Recovery: 4 Reasons Why DB2 Cloning is Excellent
During the Big Data DAMA-NCR Meeting in Washington D.C. this week, I heard from Svetlana Sicular, Research Director of Data Management Strategies at Gartner Group; Raul F. Chong, Senior Big Data and Cloud Program Manager at IBM; and John Adler and Madina Kassengaliyeva from Think Big Analytics.
Their insights into Big Data projects were quite . . . → Read More: Big Data Day Recap — 5 Very Interesting Items
As I talked recently in my blog “Big Data: 5 Considerations for Disaster Recovery,” Big Data disaster recovery drives companies to intense analysis of the big data usage and its importance. Getting the business to discuss and define the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for Big Data is a . . . → Read More: 3 Big Data Disaster Recovery Performance Factors
Big Data drives the issue of disaster recovery to the front of the discussion. How critical or how much benefit your Big Data business application is to the bottom line of the business is directly proportionate to your Big Data disaster recovery planning efforts. The following five considerations will help your evaluation of the data . . . → Read More: Big Data: 5 Considerations for Disaster Recovery
Big Data analytics are all in fashion these days, but there are many issues with how analytics are used, and how developing the appropriate analytics takes a correct and thorough data model. As I talked about in my previous Big Lies, Big Damn Lies and Statistics blog post, there are many different ways . . . → Read More: Big Data: Leverage the New Fantasy Football Data Model?
The last two weeks I talked about evaluating reporting tools, the different tools criteria categories, and determining the particular important sub topics that are unique to your company. This week we are going to talk about the Big Data itself, and four ways to determine the best areas to cover in your analytical research for . . . → Read More: Big Data: Four Ways to Identify Domains Areas for Analysis
The following are the remaining five criteria for your Big Data analytics reporting tool. As I mentioned last week, weightings for each criteria category should be discussed, along with adding your company’s sub-topic considerations, to calculate the total best score. By using these criteria and attributes as a starting point your company can quickly understand . . . → Read More: Big Data: Ten Criteria for Evaluating Analytics Reporting Tools (Part 2)