Data integration is more important than ever as organizations look to leverage the data they have to create greater value. Yet, the task of data integration has only become more complex because the amount of data collected, ingested, stored, and analyzed has increased.
Enterprises already collect a great deal of data merely by operating their enterprise applications such as enterprise resource management (ERP) and customer relationship management (CRM). Add in social media data about your brand -- Tweets, Facebook posts, Instagrams. Even more new forms of data are being introduced to the data streams in the form of new IoT data.
IT pros are tasked with creating an infrastructure that enables business users and analysts to look at all this data together and glean new insights. These users want to see what Tweets are coming from potential customers. They want to know which existing customers are complaining on Facebook.
They want a unified view of these customers and potential customers, regardless of the source of the data. They want a way to query all of this data simply, because they are not script-writing, PhD-holding data scientists.
That leaves most enterprise IT organizations and their data teams with a big messy job. Integrating data from different sources contained in different types of databases, has never been easy. That's one of the reasons the data lake became such a popular concept as organizations sought to query structured data and unstructured data. The rise of Apache Spark and Apache Kafka has added more real-time streaming data into the mix.
How do IT pros integrate all this data without breaking it? We've assembled the following critical elements of a successful data integration strategy to help you on your journey. As always, if there are other tips you've found useful in your own practice and you don't see them here, please add them in the comments section.