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Today’s big data provides more analysis opportunities and puts greater pressure on every company’s business model to produce profits. Now big data analysis can be applied and used to optimize any and all phases of your company’s business model. Customer sentiment, website clickstreams, sensor data, geo location data, all operations logs, and any structured or . . . → Read More: Three Ways to Pursue the Correct Big Data Project After implementing a big database with billions of rows, it’s time to run your applications and reports. Hopefully, the database is partitioned so the applications can utilize parallelism and complete your application and reports quickly. When you begin to write your SQL against these tables, take into consideration the first set of DB2 SQL . . . → Read More: 5 Big Data SQL Performance Tips — Fixing Generated SQL Everyone is talking about implementing analytics within their company or department and using the power of big data to gain a competitive advantage. Previously, I talked about how to get your analytics efforts going in this blog post (http://davebeulke.com/big-data-five-ways-to-get-analytics-started/). The truth is getting an analytics reporting process into everyday business activities is a difficult task. . . . → Read More: 5 Big Data Analytics Success Factors 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 analytics . . . → 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) |    | ||
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