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Extract, Load, Transform (ELT)

Extract, load, transform data ingestion process is great for processing structured, semi-structured, and unstructured data due to its flexibility.

ELT Basics explained

Data ingestion is the process of importing data from an external source into a database. This is an important phase for any data-centric process because without data, you can’t get data-driven insights.

This glossary page will focus on ELT, a modern data ingestion strategy organizations use.

You’ll learn everything you need to know about ELT, how it works, and its benefits. You’ll also learn when it’s best to use ELT and tools that can help you before we dive into some FAQs, such as how ELT differs from ETL.

What is ELT?

ELT—an acronym for extract, load, transform—is a data integration process that moves data from its source to a supported target repository before moving it for downstream transformation for insights. This is particularly different from ETL, which stands for extract, transform, and load.

This ELT data ingestion process is ideal for processing structured, semi-structured, and unstructured data. It’s also seen as more flexible and scalable because the processing power required for transformation is offloaded to the target data warehouse.

How does ELT work?

Extract, load, and transform involves three stages:

  1. Extraction

  2. Loading

  3. Transformation

You can move raw data from its source to another data warehouse or repository without worrying about transformation. Thus, this allows you to take advantage of the transformation and processing power of the target system.


The first step is to “extract” the data.

This step involves reading the raw data from the data source(s) based on your predefined rules. Predefined rules here mean you can specify that you want just a portion or subset of the data rather than everything.

The data source here can vary depending on you. It can be an

  • API connector;

  • SQL or NoSQL relational databases;

  • cloud software-as-a-service (SaaS) platforms;

  • time-series databases, like InfluxDB;

  • customer relationship management (CRM) or enterprise resource planning (ERP) systems;

  • flat files, like CSV (comma separated value), TXT, or XML files; or

  • social media platforms like Twitter.


The next stage for ELT is loading.

The raw extracted data is delivered directly to the target data warehouse or repository, so it can be analyzed by business intelligence (BI) tools. The target data warehouse is usually a cloud, data lake, or relational database.

There are multiple ways your data gets loaded into the target repository.

  • Bulk loading involves loading large volumes of data from source systems directly into the target data warehouse.

  • Incremental loading loads any changes to the source data since the last load.

  • Delta loading loads changes to the data but at a higher level of granularity, such as the partition or file level. It’s a variate of incremental loading.

  • Full refresh replaces all the data in the data warehouse with new data from the source data source.

  • Real-time loading loads the data as soon as it becomes available rather than in batches or at scheduled intervals.

  • Slowly changing dimension (SCD) loads the data into a dimension table to preserve historical changes.


The final stage of ELT is data transformation.

The sourced raw data in the target database is transformed based on your defined rules or BI requirements. The transformation here could involve

  • converting the data format and type,

  • converting coded data into usable data,

  • creating lookup tables and new columns,

  • data mapping and cleaning,

  • modifying your string data and performing various mathematical operations, or

  • ensuring data complies with data governance frameworks.

Real-world ELT use cases

ELT is ideal for structured, unstructured, homogenous relational, non-relational, and big volumes of data. Thus, this data integration technique is used in the following scenarios.

Data warehousing

ELT is mostly used to extract and load data from various source systems into a data warehouse, especially when both databases are of the same type. This is ideal since columnar storage engines for data warehouses typically improve data compression and query performance.

After this loading, the data can then be analyzed and used to generate insights that can help organizations make better decisions.

Big data processing

When the data to be analyzed is large, ELT is ideal because of its scalability and flexibility.

Once the data has been loaded, BI transformation can occur using SQL, other programming languages, or tools that can explicitly handle the data within the target platform.

This is particularly important when the data is unstructured and massive, like your log files and sensor data.

Real-time data processing

Because the process requires the data to be continuously extracted from source systems and loaded into a data warehouse, ELT works for real-time analysis.

Hence, transformation takes place in real time as well. Thus, organizations can take action once the data is available and easily debugs it if there’s any issue within the migration process.

Cloud data integration

ELT works for cloud data integration scenarios because the data can be extracted from these services, loaded, and transformed within another cloud-based environment.

Therefore, it offers scalability, flexibility, and cost-effectiveness to organizations.

Unstructured data analysis and machine learning

Based on the ELT technique, we only need to rely on the source for extracting the data. The transformation stage can utilize the target’s processing power and machine learning capabilities.

Schema read and write

ELT is well-suited for schema read and write operations because it only requires data to be loaded into a target data store in its raw, unprocessed form before being transformed within the data store.

Also, because the stored data is often in a schema-less or semi-structured format such as JSON, transformation can occur without pre-processing or schema definition.

This provides flexibility and encourages data exploration and experimentation by eliminating the constraints imposed by a predefined schema.

Best practices for ELT

Here are some ELT best practices:

  • Choose your tool based on your business needs, objectives, financial capabilities, and the data team’s technical expertise.

  • Follow the garbage in, garbage out philosophy. Implement the proper data validation and governance checks to ensure data quality and accurate analysis.

  • Go for cloud-based solutions over on-premise, as they’re more flexible, scalable, and cost-effective.

  • Regularly monitor the performance of your ELT pipelines and optimize them when necessary.

  • Implement security measures and ensure you comply with relevant privacy laws and regulations.

  • Automate your process when possible, as this will reduce any incidence of data error and improve efficiency. It makes your processes more standardized, consistent, and repeatable.

  • Document every stage of your ELT process, from the extraction to the data transformations. It’ll come in handy during debugging.

Benefits of ELT

Here are some benefits of the ELT process:

  • Flexibility — especially when it comes to data transformation — Since the burden of the transformation stage falls just on the target data warehouse, the data can be transformed to address your specific BI use case. The built-in capabilities of the target platform can also be used.

  • Simplicity — ELT reduces the complexity of building a data pipeline as the transformation task has been offloaded, thus reducing any risk of data loss during the pipeline.

  • Data is less rigid — You can transform just the data you need. Unlike ETL models that force target warehouses to use predefined tables, ELT gives flexibility with its schema read and write capabilities. It also allows users to transform just what they need for BI analysis.

  • Cost-effectiveness — It removes the need for expensive ETL infrastructure, and you can take advantage of the scalability of cloud infrastructure and hosted services.

  • Fast consumption — Data can also be rapidly ingested because you no longer have to wait for the transformation process.

Common challenges with ELT

While ELT offers various benefits to organizations, there are also several challenges.

Data storage

The overhead for storing data falls on the target data warehouse, which can be costly for the on-premise data warehouse. However, this offers more opportunities for the data for relevant BI analysis in near real time.

Data quality

Data quality is important, as only accurate and consistent data can be used for effective decision-making.

In ELT, the transformation step relies on accurate and consistent data to produce meaningful insights.

If the data collected is of poor quality, errors will show up. Organizations must ensure their data profiling, validation, and governance techniques are as effective as possible to solve this.

Data modeling

Data modeling defines the structure and relationship of the data. It’s also the foundation of efficient querying and analysis and can impact the reliability of the data.

ELT pipelines require one to load and transform the data within the platform. This is only possible with a thorough understanding of the data and the relationships between the data entities.

However, the process can be challenging and time-consuming, particularly for complex data models.


Just like other data ingestion processes, ELT pipelines can have performance issues. This is mostly due to the volume of the data and its complexity.

Network latency often impacts ELT pipelines when source data is from remote or distributed systems. This can result in slow load times, increased processing times, and decreased performance.

Data security

Data security must be implemented when working with sensitive or confidential data to prevent unauthorized theft or security breaches.

Therefore, it’s important to implement security measures like data encryption during the transfer and, at rest, authentication, data masking, and access controls.

Data lineage also needs to be tracked to ensure data temperature didn’t take place.

ELT tools

The whole data ingestion process can be complex because it involves a lot of codes, engineering, and pipeline building, especially if done from scratch.

ELT tools are designed to take this burden. They are designed to automate the building, management, and data migration process with fewer codes and steps.

Other features of ELT tools include:

  • data connectors make data extraction from various sources easier;

  • visual interfaces for mapping data from source systems to target systems;

  • pre-built transformations, data validation, and scripting tools;

  • data quality and validation checks;

  • tools for scheduling, automating the integration, and managing dependencies between jobs;

  • visual dashboards for monitoring; and

  • alerts for early detection for proactive monitoring and notifying administrators of potential issues.

Let’s look at some ELT tools.


DBT (data build tool) is an open-source tool that allows your data team to transform data using defined SQL transformation and build robust data pipelines. It also allows users to run the process as a repeatable and scalable workflow effortlessly.

Some features include:

  • its ability to manage the transformation stage in a modular and version-controlled way,

  • a testing framework for your data transformations

  • data pipeline management


InfluxDB is an open source time series database optimized for storing, querying, and visualizing large volumes of time-stamped data. Its architecture and sharded storage engine make its ability to handle high write and query loads seamless.

Just like most tools, InfluxDB has a SQL-like query arm called InfluxQL.

Some features that make it powerful are its:

  • powerful visualization tool called Chronograf;

  • robust data collectors and API that adapts to multiple products and environments;

  • wide range of integrations with other tools and platforms;

  • time series management; and

  • open source community with a growing ecosystem of plugins, libraries, and integrations with other tools and platforms.


Matillion is a cloud-based data integration platform with an ELT architecture. It takes away the compute-intensive activity of loading data from your on-premise server by sitting between your data source and BI tools.

Some key features include

  • its numerous pre-built connectors and a drag-and-drop interface for extraction and transformation;

  • the fact it’s built on cloud-native technology and, therefore, can scale up or down; and

  • automation and scheduling features that allow you to schedule and run data transformations.


What is an ELT pipeline?

An ELT pipeline is an automated data pipeline that extracts data from a source or multiple sources and loads it into the target repository to transform it.

The data migration technique allows organizations to take advantage of the analytic capabilities of the target platform.

What is the difference between ELT and ETL?

The main difference between ELT and ETL is where data transformation happens. Besides that, ETL — which stands for extract, transform, and load — is similar to ELT and is just another three-step data ingestion process.

In ETL, the transformation stage occurs in transit before the migration (source data is sent to the destination). However, in the ELT approach, the reverse happens, and transformation occurs after migration.

You should check out the ETL glossary to learn more.

Are there any benefits of ELT vs. ETL?

That transformation difference gives ELT some edge over ETL. Because the transformation happens after the migration:

  • Organizations can model the data exactly as they need it. If the business requirement changes in an ETL pipeline, you’ll need to apply the new logic and transformation on the source database and resync the data. ELT, on the other hand, will only need the logic applied to the target warehouse.

  • You can always access the original data without compromising on integrity. Thus, source data can always be retransformed wherever and on-demand, allowing for ad-hoc reporting or analysis.

  • Shorter time-to-value and time-to-insight as transformation and loading can take place in parallel within the target platform. Thus, real-time analysis can happen, even with large volumes of data.

Is there a shift from ETL to ELT in modern data pipelines?

ELT is often associated with the modern data stack, especially with the rise of big data technologies and cloud computing.

These ELT pipelines deliver faster data ingestion, as transformation is no longer required preload. Transformation and loading can even happen simultaneously.

The data archive is also great for on-demand data transformation.

Key takeaway

InfluxDB is a great platform for data ingestion, whether ELT or ETL. InfluxDB allows you to get data from any source and store it in a powerful business-ready scalable database.

Besides ingesting millions of data in seconds, you can perform and visualize your time series and real-time analytics with InfluxDB and its user-intuitive dashboards.

Take charge of your operations and lower storage costs by 90%

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