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Important Terminologies Involved in Working with Data Integration Tools

Alen Parker

Data integration is one of the hottest topics in the data science industry. There are many reasons why organizations are investing heavily already, or at least planning to do so in the next 12 to 18 months. One of the most promising reasons for the adoption of data integration tools is the data governance and compliance these tools bring to the whole DataOps framework.

In this article, I have listed a few of the key terminologies that have emerged in recent years. You should be aware of these words if you want to succeed in the DataOps environment. Familiarity with these terms would help you get hands-on expertise on data integration tools.

Data Quality Management

Data quality management is a vital part of data governance. With high-end Data Integration tools involved in DataOps, data engineering teams often require a bird’ eye view on the “fit for use” checklist of their entire dataset. DQM tools work in tandem with DI tools ensuring that data meets the critical dimensions of data governance such as:

  • Completeness
  • Conformity
  • Consistency
  • Validity
  • Uniqueness
  • Truthfulness
  • Accuracy
  • Integrity

DQM makes your investments in DI tools more effective, ensuring efficiency and security stay the hallmark of your DataOps workflows.

Once DQM is established, Cloud engineers can proceed with the definition of KQIs, KPIs, and KDEs that help govern the relationships and lineage of data for better profiling, classification, collaboration, and analytics within the framework of data quality rules.


ETL refers to “Extract, Transform and Load”, a three step data integration process. ETL workflows are deployed to transform raw data (structured or unstructured) from a varied data source into usable data before loading or moving it to a data repository such as a relational database (RDBS), data lake, or data warehouse. In the modern IT industry, ETL pipelines are widely used for data integration as well as Cloud integration operations, enabling organizations to stay up to date with their database goals.

Cloud Data Integration

Cloud data integration is an integral DataOps technique that binds together all the moving and static parts of your IT components. It is impossible to automate all the business processes in a siloed IT structure. Cloud data integration simplifies this challenge by combining all the different types of data – enterprise, content, IT, customer, finance, HR, etc, in a unified Cloud data warehouse or data lake that is accessible to the data management teams at enterprise and individual levels, providing DataOps with unmatched visibility and flexibility to work with complex data integration mapping operations. The future of business is serverless data integration, and Cloud DI definitely puts modern users at ease while migrating their Cloud data warehouses to meet serverless and virtualized ecosystems without losing agility and speed of operation.

Cloud Data Ingestion

Cloud data ingestion, in contrast to integration, is a powerful cloud modernization technique that speeds up real-time data analytics from different cloud repositories such as data warehouses and data lakes such as MySQL, Oracle, SQL Server, etc. Typically, Cloud data ingestion tools include many applications, and Cloud data integration is one of these.


IPaaS stands for Infrastructure Platform as a Service, which refers to a single stand-alone provider of fully-managed service for Cloud and Data integration and applications development. IPaaS essentially removes the need to build a data management team within an organization. By acquiring the IPaaS services from a third party platform, organizations gain speed and agility of IT cloud migration and data integration at a much lesser cost of expense and resources management.

AI Cloud

Can we think of a cloud data integration platform without an AI component? Not at all!

AI Cloud computing has established itself as the driving force for ETL vendors who provide pre-built data integration applications and purpose-built logical workflows for data warehouse specific functions. As use cases evolve, the role of AI in data integration would meet bigger opportunities.

Based on data integration requirements, DataOps teams can tweak their data management architecture to perform ETL/ ELT operations in a swift manner from the same dashboard with minimum risk and threats. Due to the recent involvement of newer concepts in DataOps such as Containerization, Automatic Machine Learning, AI Ops, and Virtualization, the whole paradigm of data integration tools management has definitely shifted to a higher plane. It is important that data management teams understand the various concepts and techniques involved in the use of DI tools for DataOps.

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