The Perfect Blend of ETL Tools & Data Warehousing

by | Dec 22, 2020 | Data Science | 0 comments

The modern business marketplace is data-driven. The word “Data” has gained more significance with time as it empowers businesses to make decisions based on facts and stats instead of hunches. 

But the turbulent data generated daily is restricting an organization’s capability to filter out the noise and get the right information at the right time. Without useful data in hand, it becomes difficult for businesses to make decisions that can help them become game-changers in their respective niche.

That’s when the need for an effective data warehouse enters into the frame. Managing a data warehouse isn’t that easy as you may think. There is a lot to consider when it’s about building a robust data pipeline, like from which sources you need to collect data and how to drive the gathered information into the warehouse for analytics. 

To sort the issues, the concept of ETL came into existence.

ETL is a three-tier architecture that extracts, transforms, and loads large amounts of data from disparate sources and platforms to a data warehouse for easy access. The process of ETL is all about turning the complex data silos into useful information by passing it through a series of operations like aggregating, sorting, formatting, filtering, and merging. The process is backed with graphical interfaces for instant and better results.

There are a number of ETL tools available in the market that can help you carry the data integration process with ease. The tools break down the big data silos and make it easier for data analysts to access and analyze data and turn it into business intelligence. In a nutshell, ETL tools are the first essential step in the data warehousing process that helps you make fast, informed, and reliable decisions.

Understanding the ETL Process

ETL is a data integration process that involves three steps — Extract, Transform, and Load. In short, ETL systems take large volumes of raw data from multiple platforms and sources, transform it into a common format, and load that data into a data warehouse of your choice. Let us explain the three primary ETL steps to you:

Etl

Extraction

In the first step of the ETL process, raw data is extracted from the source of its origin and placed in a staging area. The area acts as a shield between the data warehouse and the source data, mainly used to cleanse the data. 

Transformation

In this step, the data goes through a series of operations like cleaning, filtering, joining, sorting, splitting, deduplication, and summarization. All the data collected from different source systems are normalized and converted into a single format, along with improving the data quality.

Loading

The final step of the ETL process is loading. It involves the loading of transformed data into a warehouse of your preference. Depending on your business needs, you can load data in batches or all at once.

The benefits of ETL and Data Warehousing

The perfect blend of ETL tools and data warehousing can help you break the big data silos and drive actionable insights that can fuel your decision-making power. The right data warehousing solution will allow you to process large volumes of data effectively and get hands-on information that can make your business thrive. Here are a few benefits you will get post combining the power of ETL tools & data warehousing:

Data Warehousing

  • Improved query and system performance: The modern data warehouses are designed, taking into account the speed of data retrieval and analysis in mind, making it easier to store loads of data and run queries faster. The main focus is on making the data analysis-ready and providing businesses with relevant data to make better decisions.
  • Timely access to data: Organizations backed with the best data warehousing and ETL tools can access data from multiple sources. The solution automates the process of data integration and helps users have real-time access to crucial business information, lessening their reliance on the IT team. This way, users can spend their time and effort on data analysis instead of data gathering.
  • Better quality and consistency of data: Deploying a data warehousing solution will help you with converting the data extracted from numerous sources into a common format. Apart from this, all the departments across your organization, like marketing, operations, and sales, will have consistent data to work on. This will help them coordinate their tasks and produce results that will set your business on the path to success.
  • Higher return on investment: With a data warehousing solution, you can build a robust data integration pipeline and save time and money, formerly involved in the manual process of data integration. With cost-saving and focusing on data analysis, you can outline strategies that ensure better outcomes and a higher return on investment. 

The Role of DataChannel in providing an effective Data Warehousing Solution

DataChannel provides an effective and comprehensive data integration technology that takes minutes to set up and integrates numerous sources at once. With our offered cloud-based SaaS platform, you can efficiently collect data strewn over multiple platforms under one roof to perform analytics and reporting.

We offer over 100+ API connectors that allow you to import data directly from the marketing platforms and run analysis to drive insights that can help your business grow. We understand the different data integration needs of different organizations, and that is what made us offer tailored solutions to our valuable clients.

If you are also looking to customize a data integration platform that helps you conquer all your integration requirements, please feel free to contact us at https://datachannel.co/.

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