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What is ETL? (Extract, Transform, Load) Methodology & Use cases

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ETL stands for “extract, transform, load”. It is a process that integrates data from different sources into a single repository so that it can be processed and then analyzed so that useful information can be inferred from it. This useful information is what helps businesses make data-driven decisions and grow.

“Data is the new oil.”

Clive Humby, Mathematician

Global data creation has increased exponentially, so much so that, as per Forbes, at the current rate, humans are doubling data creation every two years. As a result, the modern data stack has evolved. Data marts have been converted to data warehouses, and when that hasn’t been enough, data lakes have been created. Though in all these different infrastructures, one process remained the same, the ETL process.

In this article, we will look into the methodology of ETL, its use cases, its benefits, and how this process has helped form the modern data landscape.

Methodology of ETL

ETL makes it possible to integrate data from different sources into one place so that it can be processed, analyzed, and then shared with the stakeholders of businesses. It ensures the integrity of the data that is to be used for reporting, analysis, and prediction with machine learning models. It’s a three-step process that extracts data from multiple sources, transforms it, and then loads it into business intelligence tools. These business intelligence tools are then used by businesses to make data-driven decisions.

The Extract Phase

In this phase, the data is extracted from multiple sources using SQL queries, Python codes, DBMS (database management systems), or ETL tools. The most common sources are:

  • CRM (Customer Relationship Management) Software
  • Analytics tool
  • Data warehouse
  • Database
  • Cloud storage platforms
  • Sales and marketing tools
  • Mobile apps

These sources are either structured or unstructured, which is why the format of the data isn’t uniform at this stage.

The Transform Phase

In the transformation phase, the extracted raw data is transformed and compiled into a format that is suitable for the target system. For that, the raw data undergoes a few transformation sub-processes, such as:

  1. Cleansing—inconsistent and missing data are catered for.
  2. Standardization—uniform formatting is applied throughout.
  3. Duplication Removal—redundant data is removed.
  4. Spotting outliers—outliers are spotted and normalized.
  5. Sorting—data is organized in a manner that increases efficiency.

In addition to reformatting the data, there are other reasons too for the need for transformation of the data. Null values, if present in the data, should be removed; other than that, there are outliers often present in the data, which affect the analysis negatively; they should be dealt with in the transformation phase. Oftentimes we come across data that is redundant and brings no value to the business; such data is dropped in the transformation phase to save the storage space of the system. These are the problems that are resolved in the transformation phase.

The Load Phase

Once the raw data is extracted and tailored with transformation processes, it is loaded into the target system, which is usually either a data warehouse or a data lake. There are two different ways to carry out the load phase.

  1. Full Loading: All data is loaded at once for the first time in the target system. It is technically less complex but takes more time. It is ideal in the case when the size of the data isn’t too big.
  2. Incremental Loading: Incremental loading, as the name suggests, is carried out in increments. It has two sub-categories.
  • Stream Incremental Loading: Data is loaded in intervals, usually daily. This kind of loading is best when the data is in small amounts.
  • Batch Incremental Loading: In the batch type of incremental loading, the data is loaded in batches with an interval between two batches. It is ideal for when the data is too big. It is fast but technically more complex.

Types of ETL Tools

ETL is carried out in two ways, manual ETL or no-code ETL. In manual ETL, there’s little to no automation. Everything is coded by a team involving the data scientist, data analyst, and data engineer. All pipelines of extract, transform, and load is designed for all data sets manually. This all causes huge productivity and resource loss.

The alternative is no-code ETL; these tools usually have drag-and-drop functions in them. These tools completely remove the need for coding, thus allowing even non-tech workers to perform ETL. For their interactive design and inclusive approach, most businesses use Informatica,, IBM Storage, Hadoop, Azure, Google Cloud Dataflow, and Oracle Data Integrator for their ETL operations.

There exist four types of no-code ETL tools in the data industry.

  1. Commercial ETL tools
  2. Open Source ETL tools
  3. Custom ETL tools
  4. Cloud-Based ETL tools

Best Practices for ETL

There are some practices and protocols that should be followed to ensure an optimized ETL pipeline. The best practices are discussed below:

  1. Understanding the Context of Data: How data is collected and what the metrics mean should be properly understood. It would help identify which attributes are redundant and should be removed.
  2. Recovery Checkpoints: In case the pipeline is broken and there is a data leak, one must have protocols in place to recover the leaked data.
  3. ETL Logbook: An ETL logbook must be maintained that has a record of each and every process that has been performed with the data before, during, and after an ETL cycle.
  4. Auditing: Keeping a check on the data after an interval just to make sure that the data is in the state that you wanted it to be.
  5. Small Size of Data: The size of the databases and their tables should be kept small in such a way that data is spread more horizontally than vertically. This practice ensures a boost in the processing speed and, by extension, speeds up the ETL process.
  6. Making a Cache Layer: Cache layer is a high-speed data storage layer that stores recently used data on a disk where it can be accessed quickly. This practice helps save time when the cached data is the one requested by the system.
  7. Parallel Processing: Treating ETL as a serial process eats up a big chunk of the business’s time and resources, which makes the whole process extremely inefficient. The solution is to do parallel processing and multiple ETL integrations at once.

ETL Use Cases

ETL makes operations smooth and efficient for businesses in a number of ways, but we will discuss the three most popular use cases here.

Uploading to Cloud:

Storing data locally is an expensive option that has businesses spending resources on buying, keeping, running, and maintaining the servers. To avoid all this hassle, businesses can directly upload the data onto the cloud. This saves valuable resources and time, which can be then invested to improve other facets of the ETL process.

Merging Data from Different Sources:

Data is often scattered across different systems in an organization. Merging data from different sources in one place so that it can be processed and then analyzed to be shared with the stakeholders later on, is done by using the ETL process. ETL makes sure that data from different sources is formatted uniformly while the integrity of the data remains intact.

Predictive Modeling:

Data-driven decision-making is the cornerstone of a successful business strategy. ETL helps businesses by extracting data, transforming it, and then loading it into databases that are linked with machine learning models. These machine learning models analyze the data after it has gone through an ETL process and then make predictions based on that data.

Future of ETL in Data Landscape

ETL certainly plays the part of a backbone for the data architecture; whether it would stay that way or not is yet to be seen because, with the introduction of Zero ETL in the tech industry, big changes are imminent. With Zero ETL, there would be no need for the traditional extract, transform and load processes, but the data would be directly transferred to the target system in almost real-time.

There are numerous emerging trends in the data ecosystem. Check out to expand your knowledge about tech trends.


Haziqa is a Data Scientist with extensive experience in writing technical content for AI and SaaS companies.