Data Integration: What is it and what are its processes

Big data is, forgive the pun, big with businesses. Enterprises are trying to find a way on how to gather, store, interpret data from various sources and use that to create insights about everything that they may find important and help in the decision-making process.

The thing is, preparing, accessing and delivering unconventional types of data from unstructured big data sources presents a different scenario than the traditional way that we work with data, for example in business intelligence. You need new practices, tools and methods to do just that. To effectively mine data or get into predictive analysis, machine learning and other forms of analytics that involves big and unstructured data sets should be applied. You will still be using the traditional data sets that you are using for your business intelligence, and then couple in the additional unstructured data from big data, in a process called data integration, or combining data from different sources for better analysis or to come up with valuable, meaningful and important information and insights.

Traditional structured data, such as those used in business intelligence is no longer enough. You can still get valuable insights from business intelligence, but you will appreciate the additional information from big data sources. Data integration helps you understand your data better, while also cleaning your data and upholding the quality of your data. Plus, it helps you transform your data into any format so that it could be used by any system.

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Data integration involves four different processes:

  1. Exploring, identifying, and mapping of different sources.
  2. Creating and maintaining metadata and documentation.
  3. Automating and accelerating quality assurance and testing.
  4. Deploying new OLTP systems, BI and analytic applications, and data warehouses.

These processes can be automated. But there are challenges along the way. For one, new data integration tools and services for big data are still quite immature. That means that if you want to do data integration with big data, you would need to have your developers work on it more as there will be features and functionalities that you need that are not offered out right.

Still there is a silver lining with cloud computing coming into the picture. The cloud is an important context for data integration. Most as a service providers today now have export functionalities that allow you to access cloud data. Plus, cloud analytics providers make data integration on the cloud easier.

Data integration is constantly evolving. Improvements and changes are always present and there is too much going on in the space. Moreover, the fact that there are several platforms now available that can help you do your data integration. You have Hadoop and several contenders, such as Apache Spark. Apache Spark runs in a Hadoop context and can persist data as well as run in-memory.

Can’t get your head wrapped around data integration? Or are you looking for ways to get into data integration as part of your big data initiative? You are lucky that Four Cornerstone is just a phone call away? We recommend using Oracle Data Integration so that you could access and manage data from disparate sources, transfer or move data, and other functions.

Four Cornerstone provides Oracle consulting in Dallas. Call us at 1 (817) 377 1144 and learn more how you could use Oracle products for your big data projects and data integration initiatives.

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