Popular Data Integration Use Cases

Paul Jhon, CIOReview Europe | Tuesday, May 11, 2021

Data integration programs will help businesses save time by cleaning, enriching, and labeling data. They produce data that is ready to be fed into algorithms.

FREMONT, CA: By 2025, over 80 percent of businesses will be using several cloud providers for their data and analytics needs. Platforms for real-time data integration are critical to making these plans a reality. They help transfer data around in real-time by connecting various cloud and on-premise sources. Take a look at the popular data integration use cases below.

Shifting On-Premise Data to The Cloud

Real-time data migration from existing databases to the cloud eliminates downtime, avoids business interruptions, and keeps databases in sync. Change Data Capture (CDC), a software operation, is critical for reducing downtime. CDC enables real-time Data Integration (DI) to monitor and capture changes in the legacy system, which can then be applied to the cloud after the migration is complete. Later on, CDC functions as well, constantly syncing two databases. This technology enables businesses to migrate data to the cloud without having to shut down their legacy databases.

Bidirectional data transfer is also possible. Some users can be stored in the cloud, and others can be stored in a legacy database. If a business deals with mission-critical systems and cannot afford any downtime, data can then be progressively transferred to minimize risk. Companies can deliver creative services by transferring data to the cloud in real-time. Courier companies can use Real-time DI to transfer data from on-premise Oracle databases to Google BigQuery and perform real-time analytics and reporting. They will then provide real-time shipment monitoring to their customers.

Assisting Machine Learning Solutions

Teams can use real-time DI platforms to run ML models more efficiently. DI programs will help businesses save time by cleaning, enriching, and labeling data. They produce data that is ready to be fed into algorithms. Also, unlike in the past, real-time architecture means that ML models are fed with up-to-date data from different sources rather than outdated data. These real-time data streams can be employed to train machine learning models and get them ready for deployment. Companies can create an algorithm that correlates data from different sources to detect a particular form of malicious activity. One could also run the streams via algorithms that have already been trained to get real-time results. ML programs will process cleansed data from real-time pipelines when a predefined event is observed and raise an alert or perform an operation. These revelations can then be used to inform future decisions.

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