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In most basic terms, ETL stands for Extract, Transform & Load. It is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML). A brief summary of each stage with respect to data analytics is mentioned below:

  • EXTRACT data from sources.

    • During Data Analysis, this is the very next step after identifying the requirements and Data Sources. There will be almost always multiple data sources.

  • TRANSFORM data into data models.

    • During data analytics, transformation falls under the Data cleansing phase where data is cleansed & transformed according to the requirements and business needs.

  • LOAD data into the target database / data warehouse.

    • This is the place where the data sits after the Data collection is complete and from where the further exploratory analysis is done.

ETL tools enable data integration strategies by allowing companies to gather data from multiple data sources and consolidate it into a single, centralized location. ETL tools also make it possible for different types of data to work together.

A typical ETL process collects and refines different types of data, then delivers the data to a data lake or data warehouse.

ETL tools also make it possible to migrate data between a variety of sources, destinations, and analysis tools. As a result, the ETL process plays a critical role in producing business intelligence and executing broader data management strategies.


As the ETL process involves Extracting data from sources, transforming it according to business needs and loading it in Data Warehousing – ETL process can come in during Data processing (Organizing) stage & Data Cleansing stage. However, further cleansing of data might be required in the Data warehouse where the collected data sits before Exploratory data analysis comes in.

Various phases of Data Analysis are shown in the below figure,


Now let us look into further detail at how each stage in the ETL process works. There are three unique processes which are:

Extraction, in which raw data is pulled from a source or multiple sources. Data could come from transactional applications, such as customer relationship management (CRM) data from Salesforce or enterprise resource planning (ERP) data from SAP, or Internet of Things (IoT) sensors that gather readings from a production line or factory floor operation, for example. To create a data warehouse, extraction typically involves combining data from these various sources into a single data set and then validating the data with invalid data flagged or removed. Extracted data may be several formats, such as relational databases, XML, JSON, and others.

Transformation, in which data is updated to match the needs of an organization and the requirements of its data storage solution. Transformation can involve standardizing (converting all data types to the same format), cleansing (resolving inconsistencies and inaccuracies), mapping (combining data elements from two or more data models), augmenting (pulling in data from other sources), and others. During this process, rules and functions are applied, and data cleansed to prevent including bad or non-matching data to the destination repository. Rules that could be applied include loading only specific columns, deduplicating, and merging, among others.

Loading, in which data is delivered and secured for sharing, making business-ready data available to other users and departments, both within the organization and externally.


What are ETL Tools?

Just as the name suggests, ETL tools are a set of software tools that are used to extract, transform, and load data from one or more sources into a target system or database.

Key considerations of ETL Tools

Here are three key considerations while selecting ETL tools.

  1. The extent of data integration. ETL tools can connect to a variety of data sources and destinations. Data teams should opt for ETL tools that offer a wide range of integrations. For example, teams who want to move data from Google Sheets to Amazon Redshift should select ETL tools that support such connectors.

  2. Level of customizability. We should choose ETL tools based on the requirements for customizability, size of data and technical expertise.

  3. Cost structure. When choosing an ETL tool, organizations should consider not only the cost of the tool itself but also the costs of the infrastructure and human resources needed to maintain the solution over the long term. In some cases, an ETL tool with a higher upfront cost but lower downtime and maintenance requirements may be more cost-effective in the long run. Conversely, there are free, open-source ETL tools that can have high maintenance costs.

  4. Some other considerations include:

    1. The level of automation provided.

    2. The level of security and compliance.

    3. The performance and reliability of the tool.

ETL tools in market:

  • Informatica PowerCenter. Informatica PowerCenter is one of the best ETL tools on the market.

  • Apache Airflow

  • IBM Infosphere Datastage

  • Oracle Data Integrator

  • Microsoft SQL Server Integration Services (SSIS)

  • Talend Open Studio (TOS)

  • Pentaho Data Integration (PDI)

  • Hadoop.


Every data integration approach has strengths and weaknesses to consider. When choosing the most suitable method, we should focus on what the ETL tools’ pros and cons mean for our organization.

Pros This established integration method is supported by many different tools. ETL has been around for decades, and data teams are very familiar with it. The transformation process helps to improve the data’s accuracy and integrity, with audit results that meet advanced compliance requirements and protect end customers’ privacy. Being able to upload the data in bulk improves efficiency. It provides access to historical data, while smart automation enables teams to cover plenty of ground without compromising quality or doing too much manual coding.

Cons For high-scale, high-volume extractions, the data transformation phase can be very heavy, in terms of I/O and CPU processing. This limitation often forces data engineering teams to settle on smaller extractions. Data teams also have to provide the business rules in advance, which offers less flexibility, can cost more to maintain, and might make the process more complex. The time-to-insight is relatively long, and the data only reaches its destination after it has been processed, denying analysts access to raw information.

In conclusion, there are many different ETL and data integration tools available, each with its own unique features and capabilities. Some popular options include SSIS, Talend Open Studio, Pentaho Data Integration, Hadoop, Airflow, AWS Data Pipeline, Google Dataflow, SAP BusinessObjects Data Services, and Hevo. We should carefully evaluate the specific requirements and budget to choose the right solution for our needs.


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