These days, as organizations, we’re surrounded by and producing more data than ever before. As a result, the term “Big Data” is not only eye-catching, but promising in solving the question of how best to leverage the vast amounts of data at our disposal. Each day big data is feeding innovation, such as machine-learning algorithms for artificial intelligence-driven processes and sensor data analytics.
Big data integration is the process that draws nuggets of gold from a tangled mass of data. It connects Big Data sources to traditional data sources and enables them both to feed into a data warehouse to produce new forms of insight that were previously unobtainable.
Given traditional data platforms are not often optimal for big data use, IT teams must now look to use and/or integrate non-traditional or newer environments within their data infrastructure that better suited to large or unstructured data. Data platforms such as Hadoop, Microsoft Azure Data Lake, and Amazon S3 among others. Big data integration also involves integrating other newer data sources such as streaming/IoT data, graph and NoSQL types of processing and analytics.
Additionally, the maximum benefit from an organization’s big data doesn’t come if it remains siloed. Integrating some or all of your big data with your smaller, current enterprise data sets and sources can provide comprehensive, robust insights.
Big data integration doesn’t require big budgets or months and years to accomplish. Automation can ease the learning curve and speed up the design, development, deployment and operation of big data integration delivering new big data projects within your organization faster and with less cost and risk.
Eliminate hand-coding and other repetitive, time-intensive tasks to fast-track big data integration and data management projects. Boost developer productivity and improve collaboration with the business.
Listen to industry analysts and consultants provide their insights into the impact of big data on data infrastructure projects.
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