Data warehouse News
September 15, 2017
This is a guest post for the Computer Weekly Developer Network by Barry Devlin in his capacity as founder and principal of 9sight Consulting. A TechTarget contributor is his own right, Devlin is a ...
July 31, 2017
There's an uprising in the Middle East, but this time it's no Arab Spring... or if it is, it is a new beginning for the software application developers native to the region. The Arabic-speaking ...
July 12, 2017
Spirited competition is under way among cloud providers as they enhance large-scale relational data warehouses in the cloud. An Azure SQL Data Warehouse update by Microsoft is the latest example.
June 19, 2017
At its Pure Accelerate 2017 conference this week, Pure Storage gave a new interpretation to the term data warehouse. A reported 3,000 analysts, customers and resellers gathered in a ...
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You can create SQL Server databases manually, but knowing how to do a scripted database setup is valuable. Here are the steps involved in executing a create database script. Continue Reading
Microsoft has recently added a SQL Database Query editor to its Azure cloud service. Expert Michael Otey walks you through the new T-SQL development process step-by-step. Continue Reading
Big data architectures typically involve multiple processing platforms. In this essential guide, you'll find information and advice on managing Hadoop, Spark and other big data technologies. Continue Reading
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Your choice of a cloud database, data warehouse or data lake depends on the structure of your data and your analysis needs. Follow these guidelines to know which to choose. Continue Reading
A data lake can provide lots of analytics value to an organization. This quiz can tell you if you know what it takes to deploy and manage one; it also contains info to help boost your knowledge. Continue Reading
Data lakes combined with the proper analytics technology are well suited for IoT implementation. Red Hat chief architect James Kirkland explains why. Continue Reading
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In some regards, the term big data management can be viewed as an oxymoron. In fact, oxymorons abound in this industry and society -- virtual reality, artificial intelligence, science fiction and awfully good, the latter of which can apply to the challenges encountered in managing the onslaught of big data from multiple sources. There are countless tools, techniques and practices available for the big data ecosystem to properly gather, mine, prep, store and analyze data and help smooth operations, build marketing campaigns, improve customer service and develop the next new product disruptor. As simplistic as this may sound, it's up to data managers to sort it all out as their data lakes swell beyond capacity.
"The data lake isn't where data goes to die," Gartner analyst Merv Adrian said at the 2017 Pacific Northwest BI Summit, "it's where data goes to live."
October's Business Information opens with our editor's note and advice for data managers to move beyond traditional data control to the critical task of improving data quality and delivery -- taking all that raw data and making it useful. Whether for internal or external business use, the demands for instantaneous data access continue to accelerate, spurred on by mobile apps, artificial intelligence (AI), machine learning and internet of things (IoT).
In that vein, our cover story examines companies that use their big data ecosystem to divert data lakes toward developing new strategies, products and revenue streams -- in the process, smashing their old business patterns. In another feature, IoT and machine learning technologies help take the guesswork out of estimated times of arrival for transport companies whose businesses depend on shipping and receiving goods.
Also in this issue, a business intelligence project combined three data warehouses into one to reduce warehouse size by 80% and data load time from several weeks to just days while causing IT staffing problems in the process. In other features, learn how metadata programs can ease mega management woes; semantic technology could be a blessing or curse to AI; companies are gearing up for greater big data management deployments; and all data must be treated equally in the search to find value.Continue Reading
Changes in data management processes -- including self-service data preparation, data lakes and real-time analytics -- create a new landscape in companies, according to analyst Matt Aslett. Continue Reading
The growing use of mobile devices by business users broadens the risk of data inconsistencies, a hazard that an integrated process for managing metadata and master data can help address. Continue Reading
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Fact tables and dimension tables are used together in star schemas to support data analytics applications. But they play different roles and hold different types of data. Continue Reading
Operational data stores and data warehouses both store operational data, but the similarities between them end there -- and they both have a role to play in analytics architectures. Continue Reading
Providers can now do more than analyze past patient data with healthcare BI tools. They can possibly reduce readmissions and assign facility resources with predictive analytics. Continue Reading