What is ETL

ETL is the process of reading data from one or many data sources, re-shaping the data to fit the needs of the business, and storing the data into a destination like a data warehouse or a data lake. It stands for “extract, transform and load”.

What is the ETL process?

ETL Process

The ETL process consists of three steps:

1. Extract: It involves connecting to all the data sources you have, such as Salesforce, Hubspot, etc., and extracting the raw data. Data can be in any file format like CSV, Excel, JSON, etc, and in any source like databases, Saas applications, APIs, files, etc.
2. Transform: It consists of transforming the collected data into a common structure so as to eliminate all the data quality issues of the original data. One would also need to re-shape the original data when it comes from different sources or it changes from time to time in order to make it suitable to be loaded.
3. Load: It includes loading the transformed data into data-warehouses such as Snowflake, BigQuery, and Redshift, etc which are designed to run reports on the data.

Once the data has gone through ETL, you can run analysis, build models, reports from the data.

Why do we need ETL? §

The ETL is needed to congregate data from various different sources, transform that data, and then send that data to a destination. Suppose, there is a multinational company that operates in various countries. In order to generate business reports, one needs to collect data about employees, payroll, finances, sales, marketing, etc. Instead of collecting all the data manually, we can use an ETL tool to automate the data centralization process.

Why are the advantages of using the ETL? §

1. Ready to use data: The ETL process makes it easy to transform huge amounts of data into the required format which can directly be used to perform analysis or build models.
2. Comprehensive and time-saving: The graphical user interface allows data engineers to quickly build the ETL thereby saving a lot of time that would have been otherwise spent building the data pipeline by writing code.
3. Data Quality: Data quality is essential for informed decision-making. ETL testing ensures that only accurate and relevant data is falling into the production systems.

What are some popular ETL tools? §

1. Informatica — PowerCenter: It is an ETL tool developed by Informatica Corporation. It is one of the best ETL tools which offers the capability to connect & fetch data from different sources.
2. Microsoft SSIS: SSIS is a product by Microsoft and was developed for data migration. The data integration is much faster as the integration process and data transformation are processed in the memory. It has one major disadvantage that it only supports Microsoft SQL Server.
3. Talend: It is a free open-source ETL tool. It is a data integration platform that supports data integration and monitoring.

What is the future of the ETL? §

In the ETL process, transforming data before loading it into a warehouse forces data analysts to predict the data models and insights the data will power. This means that data analysts often design and builds intricate processes and workflows beforehand in order to use data, and then redesign and rebuild them whenever reporting needs modification. This puts a lot of restrictions on data analysts and data scientists.

To solve this problem, a new process of extracting, loading, and transforming data(ELT) has been developed. Using the ELT process analysts can use data in its original form, and transform it using cloud-based technologies to extract insights without limits. Comprehensive ELT tools such as Fivetran and Stitch are becoming more popular every day and are leading the wave of the move from the traditional ETL process to the modern ELT process.