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10 Robust Enterprise-Grade ELT Tools To Collect Loads of Data

Enterprises in 2021 deal with a massive amount of data on a regular basis. The Global Data Fabric market analysis says, "businesses that use insights from data extraction will earn $1.8 Trillion by the end of 2021". With such great amounts of data, it is becoming increasingly hard to maintain and categorize the collected data. Moreover, manually processing the data only became more time-consuming and monotonous. With rapid technological advancements, companies are finding ways to find even the slightest advantages to be the best in the market.  Hence, adopting the right ELT tools/platform can greatly contribute to enterprise productivity. ELT tools can collect data, segregate the data based on common characteristics and provide clear-cut insights about the collected data. 

Below is a list of the 10 enterprise-grade ELT tools that I rate above 4 (out of 5).  These can provide great advantages to enterprises that adopt them.

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AI and BI Projects Get Bogged Down With Data Preparation Tasks

IBM is reporting that data quality challenges are a top reason why organizations are reassessing (or ending) artificial-intelligence (AI) and business intelligence (BI) projects.

Arvind Krishna, IBM’s senior vice president of cloud and cognitive software, stated in a recent interview with the Wall Street Journal, “about 80% of the work with an AI project is collecting and preparing data. Some companies are not prepared for the cost and work associated with that going in. And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.” [1]

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Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects. 

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The Process of ETL Testing: How it Maintains Data Integrity and Consistency

First, let's understand what is ETL. This notation stands for Extract-Transform-Load. For large-scale firms, initially, the data is extracted from the source systems and then transformed into specific data types and, ultimately, loaded into a distinct repository. And this process should be tested efficiently to make sure that the data is managed properly in the warehouse.

What Does Testing of ETL Refer To?

It is a procedure that tests the withdrawal of data for further transformation, authentication of data during the transformation stages, and loading or filling of data in the endpoint.