In one Forrester report, customers said that up to 80 percent of their data warehouse workloads were ETL jobs

According to MarketsandMarkets, "The data integration market is expected to grow from $6.44 billion in 2017 to $12.24 billion by 2022, at a compound annual growth rate (CAGR) of 13.7 percent."

The term ETL is defined as a type of data integration that uses three steps- Extract, Transform and Load. It is a software tool that blends data from multiple RDBMS source systems and makes it suitable for entities to be used for business analysis and decision-making processes. In simple terms, the data is extracted from a data source, transformed into a proper format and is stored in a data warehouse to be used in the future. The process needs active efforts of various stakeholders like top executives, testers, developers, etc. To be used for future decision making, data warehousing system must be changed from time to time with changing business needs and trends.

Extract- It extracts the data from the source system. The extracted data is then put under the validation process to ensure whether the data grabbed from the source has the right value in the given domain or not. The data that do not pass validation are rejected and sent back to the source for further analysis.

Transformation- Transformation is the second phase followed after Extraction in which the extracted data is transformed into a single format and the consistency is maintained throughout the entire process. The outlined errors, outliers, inconsistencies are kept aside for further analysis and review.

Loading- Once the data has been organized, optimized, structured and transformed, they are ready to be written in the final location storage which is ‘Data Warehouse’ either by direct loading a new set or by updating the old one. The data warehouse can be accessed by the business entity to get real-time insights and make use of it for various decision makings.

Evolution of ETL

1970 is the year known to address the need for ETL with growing complexity in handling and storing multiple data to obtain information. It was since then organization started using multiple data sections, or databases to store different types of business-related information. The year between 1980 and 1990 marked the trend of a distinct type of database known as data warehousing. It provided combined access to information from multiple systems. The need to consolidate various important data grew quickly. Within a few years, ETL became an extensively popular method of collecting data from the diverse source, and transform it before loading.

ETL tool varies from company to company. Different industry chooses different ETL tools for their business simplification.

Why is ETL Imperative?

ETL has now become a core component of a business activity whether being small or large scale organization. Data being the backbone of any entity is needed to be extracted, manage and store adequately. Organizations have relied on the ETL tools for years to get an integrated form of data to drive better business insight.

• To gain a deep historical context for the business
• Improves data professionals’ productivity by codifying and reusing process
• Makes it easier to analyze and report relevant data
• Provides a common data repository
• Move data from various sources into a data warehouse
• Accessing and Manipulating source data into the target database

ETL is must for businesses in 2020

It’s the tool that transforms data into a high-quality business asset. Ensuring consistency, uniformity, and homogeneity of data sets, it manages diversified and voluminous data structures.

Greater Speed- Unlike manually written codes and traditional methods of movement of data from source to storage units, ETL is much fast and automated. It gives a higher ROI as productivity increases and cost decreases.

Greater accuracy-The data integrated by ETL are of high quality, reliable, transparent and important for the organizational operations. ETL tools move voluminous & complex data by filtering, reforming and merging them and further convert them for advanced analytics.

Use GUI

The Graphic User Interface in ETL tools does not only monitor the data streams but also controls the process and set rules. It enables companies to easily track and understand data to all hierarchy levels.