5 Big Wishes For Big Data Deployments - InformationWeek
Data Management // Big Data Analytics
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Doug Henschen
Doug Henschen
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5 Big Wishes For Big Data Deployments

Big data project leaders still hunger for some key technology ingredients. Starting with SQL analysis, we examine the top five wants and the people working to solve those problems.
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Wish 2: Simplified Deployment And Management
There's no shortage of efforts to simplify the deployment and management of big-data platforms including Hadoop and NoSQL databases. It seems each and every software update brings new management features and new built-in capabilities. 10Gen, for example, added built-in text search capabilities and on-premises monitoring capabilities with the latest release of MondoDB. And Hortonwork's distribution of Hadoop for Microsoft Windows ties into Active Directory, Microsoft's System Center, and Microsoft virtualization technologies to simplify deployment and management.

We haven't heard a lot of complaining about the hardware-related challenges of building out Hadoop clusters. Nonetheless, EMC, IBM, Oracle and Teradata insist their released and pending Hadoop appliances make deployment faster and easier than the build-it-yourself approach. The cost of commodity hardware might be alluring, but Oracle, for one, says its appliance costs less less than build-it-yourself deployments when taking into account the price of individual components, time saved on provisioning and tuning the system, and support and upgrade efforts. Oracle's appliance includes pre-configured, ready-to-run versions of Cloudera software and Oracle's NoSQL database.

The real messiness and complication of managing Hadoop usually involves the software, not hardware configuration. HBase, for example, is the Hadoop framework's increasingly important NoSQL database, but many practitioners have found it hard to model and analyze data on the database. Vendor WibiData provides open-source libraries, models and tools that make it easier to store, extract and analyze data on HBase. The idea is to make the hard, technical parts of running HBase repeatable so you need fewer engineers and data scientists when trying to solve business problems. That's a formula that should and will be applied across many big-data platforms.


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