As we end our Improving the Daily Life of a Database Developer blog series, we outline how to manipulate data for visual analytics. Be sure to check out our previous post, Comparing Data Sets and Object Scripts.
Within database teams, database developers and administrators routinely manipulate data through intermediate formats like CSV and XLS. The utilities they use to edit, import, and export records among tables, databases, and DBMSes are table stakes in the world of database development.
One example of a business objective for database developers involves analysis of a data set on which a user decides or takes an action. Instead of handing off a data set to data analysts and sending them into different applications to create charts and dashboards, the IDE approach includes features that enable visual analytics in the same interface where the data sets were generated.
Visual analytics features allow users to pull query results into worksheets and easily create visualizations of data comprehensible to the decision makers who most need them. Through the GUI, database professionals drag and drop data sets to create charts, graphs, and dashboards they can save and share throughout the organization. Working in the same tool used to build and run the original queries, data analysts can build data visualizations including diagrams, pivot tables, trend lines, reference lines, box plots, and other graphs commonly used by business managers to analyze their data.
The visual analytics capability results in higher productivity among the database developers, DBAs, and data analysts who make analysis of the data more useful to the entire organization.
Database development in many organizations is expanding beyond traditional DBMSes to emerging platforms like NoSQL and cloud. It involves collaboration among database developers, DBAs, and data analysts. As the variety of database platforms and vendors grows, it is natural for users to adopt more tools to accomplish their tasks.
The IDE approach to database development tools accommodates the widest variety of needs. It smooths the way for database professionals — even those working on unfamiliar platforms and dialects of SQL — to maximize their productivity while minimizing their learning curve. Organizations find it to be cost-effective because they can reduce their overall investment in database development tools by selecting a single product that addresses multiple platforms, instead of needing separate products for each platform.
With a single product, users can accomplish their tasks across dozens of DBMSes with a common interface and consistent workflow.
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