Essential Trends in Data Integrations

Data integration is a critical component in modern information management- here’s why.

Whether you are a leader, manager or end user, you want to access data across multiple sources stored in disparate silos to get a complete picture of the organization. What developments create outside risk? What patterns can you detect to take advantage of changing markets? Data integration helps you get answers to vital questions faster and with more accuracy.

In this two-part series, you’ll get a closer look at key concepts in big data, and learn how technologies like HiPER help integrate datasets. This article will cover data and analytics trends such as real-time integration, NoETL or APIs, data hubs, and data lakes. The second part of the series will consider conglomerate trends such as master data management (MDM), legacy trends like cloud integration and orchestration layer trends such as microservices.

Real-Time Integration

Ongoing entity resolution is critical, especially in industries like healthcare, where data accuracy is vital, yet up to 30 percent of healthcare data is incorrect. Industries like healthcare, manufacturing and e-commerce are moving to real-time data availability to meet the expectations of consumers and the demands of a competitive marketplace. Out-of-date data frustrates customers and hinders company effectiveness. Whether applications are in the cloud or on-premises, real-time integration improves business operations and speeds up time to insight. With real-time processing, data is continually processed, which increases accessibility, reduces errors and increases uptime. Problems can be handled immediately by staff, increasing reliability and performance.


In line with the desire for real-time integration, NoETL is becoming more popular. ETL — or extract, transform and load — is a database process of reading data from a source, transforming it to a desired state using specific rules, and writing the results to another destination, either a new or existing database. ETL has advantages, but it is often not able to keep up with the large increases in data many companies produce every year. Performance suffers, and rewriting scripts to accommodate varying reports is time-consuming. Systems like Spark and Hadoop have plenty of processing power and low-cost storage, making NoETL approaches more feasible. Companies are moving to NoETL or APIs, which provides immediate linked data from raw data with different structures coming from multiple sources.

Data Hub or Data Lake

Because traditional data stores and analytics often lack the flexibility and speed organizations require for relevant insights, they are increasingly turning to data hubs, also known as data lakes. Data lakes let you store enormous amounts of structured and unstructured data in one place, allowing different divisions of the organization to process, assess and deploy the data downstream without first needing to convert it to a defined schema. For example, HiPER creates repositories that become the source of truth for all systems downstream, including analytics, compliance frameworks, databases and business applications. Data lakes remove the need to build and refine relational databases that may be outdated by the time they are deployed, as well as make it difficult to handle increasingly large datasets from inside and outside the organization.

Business today requires a flexible, rapid approach to data that integrates multiple sources quickly for analysis and processing. Data integration trends such as real-time, NoETL and data lakes are meeting this demand. Check out the next entry in this two-part data integration series to read more about MDM, cloud integration, microservices and entity matching tools. And if this all seems a bit over your head, don’t worry, we’re here to help. Contact us so that we can take care of your data concerns.

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