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
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)
Last week I talked about how to get involved in the latest conversations about Big Data analytics. When working with the Big Data analytics, the end business users reporting tools are critical. Your older tools may not be up to today’s Big Data analytics capabilities, such as delivering answers to the “bring your own device” . . . → Read More: Big Data: Ten Criteria for Evaluating Analytics Reporting Tools (Part 1)
Last week I talked about five new data management perspectives that are driving Big Data. In Part Two of Big Data New Data Management Perspectives I would like to focus on the Big Data analytics.
In my blog over the years I have mentioned how business analytics are making the difference between winning companies and . . . → Read More: Big Data: Five Ways to Get Analytics Started
In last week’s blog post on company and government analysis of our digital footprints, I mentioned several aspects of the Big Data digital horizon. The last point that I made in that post about the other data aggregators was highlighted in the movie preview of “Terms and Conditions May Apply” that details these . . . → Read More: Big Data: 3 Criteria for Invaluable Analytics
All types of systems exist, and they were developed for the primary purpose of showing something that needed to be discovered or verified, or have its actions revealed. After specializing in data management for a long time, I am not surprised by the latest data disclosure about the government collecting any type of data on . . . → Read More: Big Data: 3 Reasons to “Get Over It!”