What Resources Are Used To Integrate Data?

 

  • Extract, Transform and Load (ET-L)

ETL manages raw data, extracts the necessary information from them, and performs the correct transformations to meet the needs of a business. Extraction, transformation, and loading are steps in which data engineers and developers from best data science training in Bangalore deal with the limitations of traditional data repositories to come into compliance to be used in generating insights.

The solution, therefore, is the most appropriate in situations where data is moved to traditional, more rigid, and limited structures. They are less common now that Data Lakes and Data Warehouses are in the cloud.

  • Extract, Load, and Transform (ELT)

ELT tools (extraction, loading, and transformation) are a different approach to data integration. The goal is to load the data into a Data Lake, making it available to different consumers and applications.

However, it is a different way of looking at the data compared to the method used in ET-L. The great benefit of ELT is that once the data is extracted, it can be quickly loaded, without any transformation, in the Data Lake to be transformed as needed at the time of data consumption, such as in an analytics project. This is only possible with the evolution of consumer tools capable of handling high volumes of data.

This is the most modern solution, best suited to cloud environments as naturally giant volumes of data are involved.

  • Microsoft Data Platform

Microsoft also offers a set of tools for data integration for personal and business use. The platform allows organizations to track the entire data lifecycle, extract, transform and consolidate information from various SQL, NoSQL, and XML files.

Microsoft differentials over other methods ease implementation and flexibility in licensing, easily scalable according to your company’s use of the service. Working under the PaaS model (Platform as a Service, or platform as a service) can also be implemented on-premise.

Why Is An Integration Strategy Important?

For the generation of insights, data integration is essential. It allows companies to transform them into actionable and relevant information about the performance of the business.

The first step towards data-driven management is an integration strategy, as, without it, it is impossible to obtain tangible benefits with Business Intelligence and Business Analytics solutions.

Even if a company has all the data it needs to decide, it will undoubtedly face problems if it is not integrated. Accessibility is essential to obtain a 360º view of projects, customers, processes, and business operations.

Benefits

There is no lack of advantages in the integration of business data. It allows real-time access to panels containing the essential information for decision-making in the various areas of the business.

Only with tools dedicated to the task can managers generate, promptly, value with the data that the organization has accumulated over the years and create models for prescriptive and predictive analysis. Other advantages that data integration tools bring to the enterprise are:

improvements in the user experience;

optimized operations;

greater productivity.

Challenges

The main challenge to integration is the transposition of data stored in existing systems and new resources. Therefore, automating the process with tools made for this is the simplest way to solve formatting problems, duplicate data, and typos. Thus, we obtain clean and reliable data that facilitate the migration to new solutions, such as generating analytics in the cloud.

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