3 Data Integration Trends That Can Make ETL Obsolete
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3 Data Integration Trends That Can Make ETL Obsolete

By CIOReview | Thursday, November 21, 2019

Businesses should make use of data integration services to ensure that they can make the best use of resources.

FREMONT, CA: With the emergence of new cloud-based native tools and platforms, the traditional methods like ETL to build a data warehouse is becoming obsolete. ETL is a type of data integration referring to extract, transform, and load, which are the activities to blend data. Over time, the amount of data has expanded tremendously, such that ETL has lost its significance and is now one of the methods organizations use to collect, import, and process data. Here are some of the emerging trends replacing ETL.

Unified Data Management (UDM) Architecture

It offers a holistic data model that can be used by companies of all sizes to meet their business objectives. The UDM system helps combine disparate data sources to create a single data narrative within a data warehouse. It fosters interdepartmental cooperation by creating a centralized data repository through which the company data is parsed, cleaned, and analyzed to produce actionable insights. UDM is a cost-effective method enabling businesses to take control of the enormous datasets. It can also be integrated with cloud, on-site, or hybrid data infrastructures to optimize data for business applications.

Machine LearningTop Data Integration Solution Companies

Machine learning can perform almost all the work associated with the entity discovery and relationship related to the development of data warehouse, data lake, and search indexes. It can be applied for anomaly detection and structure discovery. Machine learning forms the basis of smart data integration that will embrace a No-ETL approach that supports structured as well as unstructured data.

Event-Driven Data Flow Architecture

A growing number of businesses are adopting the approach of event-driven architecture as it helps to offer actionable insights and solutions in real-time. It is used to produce highly scalable applications. It is also highly adaptable and can be used for small applications and as well as large, complex ones. As businesses are making use of better data integration, distributed messaging system, and implementing newer concepts, it helps better data flow pattern to identify necessary data.