How ETL Evolved?
Businesses have been collecting the data for a long time but in the modern era the possibility of storage of data will be only with the computers and digital storage.
- 1970s – Introduction of ETL: In 1970’s larger centralized databases will be invented with ETL (Extract, transform, and load ) also introduced and processed and merged processed for the data analysis.
- 1980s – Rise of Data Warehouses and Relational Databases: In 1980’s data warehouses (Storage which used for data storing purpose) and relational databases will be popular for making better analytics and decision making. In older transactional database will store the database with transaction by transaction with include the duplicate customer information stored with their transaction, that’s why there is no way to access the data in a unified way with time. with relational database the analytics will made the basic foundation of the business intelligence (BI) and the write tool for the decision making.
- 1990s – Automation and Big Data: Until the invention of the ETL software the all process of carry with manual efforts by the IT team for extracting the data from the different systems and connectors for the transformation of the data into the common well known format. Then transfer into the interconnected tables. Still the early ETL will be best option as a algorithm, addition of neural network will be making more opportunities for the analytics of the data.
- 1990s – ETL in the Cloud: In 1990’s the Big data invented as result the computing speed and the storage capacity will increasing efficiently, where in which large amount of data will get from the different sources like social media and IOT (Internet of Things).
- 2000s and Beyond – Advanced Analytics and AI: In 1990’s ETL and cloud computing were become more popular. With using of the data warehouses such as (AWS) Amazon Web Services, Microsoft Azure and Snowflake which making the availability of these data around the world.
What is ETL (Extract Transform Load)?
In analytics and data integration, ETL is an essential procedure. It involves collecting data out of multiple sources, formatting it uniformly, and then feeding it into a target location like a database or data warehouse. In order to provide organizations with actionable insights and the ability to make well-informed decisions, ETL is essential to the consolidation and preparation of data for analysis.
Table of Content
- What is ETL?
- How ETL evolved?
- ETL VS ELT
- How ETL works?
- ETL and other data Integration methods
- Benefits and challenges of ETL
- ETL tools
- The Future of Integration-API’s using EAI
- Conclusion
- Frequently Asked Questions on What is ETL?
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