Businesses that deal with large amounts of data need data warehouses to store that data. They also need to organize the data before they can glean any valuable insights. However, moving so much data to a data warehouse and then making sense of it is a lot of work. 

That’s where Extract, Transform, Load (ELT) tools can help you. 

ELT tools let you: 

• Extract data from your data sources 
• Load it into a storage location 
• Transform it so you can do a detailed data analysis for future business decisions

In this article, we’ll explain what ELT is and how it’s helpful for businesses. We’ll also share the eight best ELT tools for you to choose from.

What are ELT tools and what are the benefits?

ELT tools are helpful for businesses that want to use data to achieve important goals. It can help you power more accurate analytics, deliver real-time personalized experiences and develop innovative digital products around data. 

Say you have a lot of customer data from Facebook, Instagram, LinkedIn and an in-house CRM system. The data integration process, consolidation and preparation of data from all these sources is difficult and time-consuming. In 2020, the average data scientist spent a whopping 45 percent of their time on data wrangling.

ELT tools allow you to collect and move data from many disparate locations to a central repository like a data warehouse (and since all the processes happen in the cloud, they’re also called cloud data warehouses).

ELT solutions then transform data into models analysts can use to construct metrics, reports and dashboards.

A few examples of transformation include: 

• Deduplication: Detecting and deleting duplicate data.
• Restructuring: Identifying relationships between tables. 
• Splitting: Making many columns out of one column.  
• Aggregation: Adding up data from various data sources and data sets. For example, calculating the total ad spend from ad campaigns in different marketing channels like Facebook Ads, Google Ads, LinkedIn, Quora, Pinterest, etc.  
• Format revision: Changing data formats or revising units of measurement. 

These capabilities lay the foundation of a modern data stack.

ETL vs. ELT tools 

ELT tools are quite different from ETL solutions. 

With an ETL tool, you first transform the data and then load it into the data warehouse (e.g., Google, BigQuery and Amazon Redshift). But with an ELT tool, you first load the data and then transform it. 

Here are four essential differences: 

1. ETL tools are an acceptable option when you have small amounts of data to process. However, if you want to work with big data analytics, ELT tools should be right up your alley. 

2. In ETL, you must move data to a staging server to transform it (before loading it into your data warehouse). Moving data takes a lot of time and also doesn’t let you permanently store raw data. 
   
   In ELT, raw data is processed and enriched in the data warehouse itself. This way, data is available (and archived) in the data warehouse permanently, so businesses can use that data anytime they want for any kind of transformation. 
   
   
3. ELT tools are flexible with any data size. However, ETL tools get wobbly and slow when data size increases. 

4. ETL tools are often on-premise and not cloud-native, so they’re difficult to scale. Destinations are traditional data warehouses, which means you can only deal with structured data (e.g., names, addresses, phone numbers, dates of birth, etc.).
   
   In contrast, ELT is cloud-native, which makes it easy to scale. With modern cloud-based destinations, you can easily accommodate structured and unstructured data (e.g., texts, powerpoints, PDF docs, etc.) 

8 best ELT tools for 2023 

ELT tools have gained much traction because of their faster data processing and on-cloud availability. In the section below, we cover eight ELT tool examples along with their benefits and key features, starting with our own ELT solution, Fivetran. 

1. Fivetran 

Fivetran is a cloud-based ELT tool that supports a range of use cases across marketing, finance, ops, sales, support and more. 

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Here are some of our most important features:

• Data connectors: We offer 200+ pre-built data connectors to automate data pipeline processes. Fivetran keeps up with any API changes so you can integrate and transform the newest data from a single or multiple data sets.
  
  
• Pre-built data models to a large suite of platforms: Fivetran comes with pre-built data models. These models help you extract and transform data into clean tables from popular ad platforms and social media sites like Apple Store, Facebook Ads, Mailchimp, HubSpot and more.
  

• Access to real-time data: With a 99.9 percent platform uptime, you get real-time data updates. This way, you always have fresh data for analytics.

• Robust security: Our built-in security features keep your data safe from source to destination. With Fivetran, data encryption happens in transit and at rest, you can set granular permissions (can decide who can access what data) and opt for SSO login with Okta and One Login.
  
• Fast onboarding: Fivetran’s easy-to-use interface ensures that you don’t have to spend too much time learning and using the platform. Plus, it’s a low-code platform, saving a lot on data engineering resources. 
• Pricing: Our pricing depends on the number of rows of data you want. A unique feature of Fivetran’s pricing is that many other ELT options use raw rows for their pricing plans, but Fivetran uses active rows. Raw rows mean rows will be counted even if you edit or delete rows. Active rows, in contrast, mean you can update a record 1,000 times, but we’ll count it as just one ‘active’ row.

Case study: Fivetran + JetBlue

JetBlue serves more than 100 cities across the world with an average of 900+ flights every day. Every flight and every individual generates data points that help JetBlue gauge more information on customer sentiments, budgeting, forecasting fuel consumption and planning aircraft upkeep. This means Jetblue has to deal with a lot of data. 

JetBlue used Fivetran’s powerful high-volume data replication solution to move data from multiple destinations to its Snowflake data cloud. This allowed JetBlue’s data engineers and analysts to not only get faster access to data but also to implement a completely new data-driven maintenance system for their fleet.

Sign up for a 14-day free trial to explore all of Fivetran’s features and see how it fits your workflows.

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2. Matillion

Matillion is an ELT tool that connects a wide variety of data source systems to your analytics tools. 

It comes with a drag-and-drop UI that helps you create ELT jobs in a few minutes, which helps non-technical users a lot. However, you can also choose to code it yourself if you have data architects in-house.  

Matillion is specifically designed for Azure Synapse, Snowflake, Amazon Simple Storage Service, Google BigQuery and Amazon Redshift, so it may not be best suited for data warehouses other than these.  

Key features:

• Process millions of rows within a few seconds
• Launch ELT jobs within minutes
• Get real-time feedback when you’re  building ELT jobs
• Access over 100 data connectors

3. MuleSoft

MuleSoft’s open-source ELT tool makes it easy to filter, extract and transform data with its numerous offerings: Anypoint Platform, Anypoint Connectors and more. 

Anypoint Platform lets you integrate, share and migrate data with a simple graphical data mapping interface. Plus, it also offers a special tool called Dataloader.io for companies that want to move, import or export data specifically from Salesforce. 

With Anypoint Connectors and APIs, you can quickly integrate data from hundreds of apps and services. Then, DataWeave lets you map and normalize any data format (i.e., XML, JSON, CSV) so you can load data into your data lake or target database.

Key features:

• Build APIs quickly 
• Use machine learning and transform data
• Test APIs and integrations on one platform 
• Connect to the CI/CD pipeline and guarantee 99% uptime

4. Informatica

Informatica’s ELT tools let you connect source data with thousands of integrations and recognize metadata to simplify complex integrations. 

Informatica lets you ingest databases, files and even streaming data for real-time data replication. You can integrate data on any cloud, integrate any apps (on-prem or cloud) and quickly process petabytes of data. It also identifies and fixes any data quality problems during the data replication process.

Key features:

• Extract data from various databases with different data types like unstructured, semi-structured and structured data
• Execute multiple processes at the same time (parallel processing) 
• Easy-to-use interface for job scheduling, debugging and session monitoring 

5. Talend

Talend’s open-source ELT tool offers a self-service platform that makes data ingestion from any source effortless and prepares data so that it’s available for analysis from day one.

You can integrate any kind of data from any data source to any destination: both on-prem or in the cloud. Talend lets you build data pipelines that can be run on any cloud technology without platform lock-in limitations. With its data capture, you can speed up the data replication and have the most updated data for analysis.

Key features:

• Access over 900 data connectors so you can easily move data to any source
• Create visualized graphs and charts and see the whole data integration process
• View historical data records for audits or future data correction

6. Qlik

Qlik’s ELT platform makes real-time data available for analysis with its data integration functionalities.

Qlik comes with a large offering of products that let enterprises ingest data, replicate data, store data in data lakes and data warehouses, automate pipelines and catalog data for efficient data management. 

You can then automate data streaming, cleaning and publishing on the cloud of your choice and make that data available for your data analysts and experts. 

Key features:

• Analyze data faster with advanced visualizations
• Prepare and load data with a drag-and-drop editor
• Collaborate with other users via a unified hub

7. Azure Data Factory

Azure Data Factory is a fully managed, serverless and scale-on-demand data integration platform.

You can bring in data from many data sources like Google BigQuery, Amazon Redshift, Marketo and Salesforce. 

Key features:

• Access over 90 built-in connectors that don’t require any upkeep
• Build ELT processes without writing any code and send that integrated (on-prem or SaaS) data for business analytics and insights
• Run pipelines on a set schedule (daily, weekly, etc.) 
• Get support in transformation activities like Hive, Spark and MapReduce

8. AWS Glue

AWS Glue is another serverless data integration platform on our list. It lets you discover, move and integrate data from 70 data sources for machine learning, analytics and app development.

For ELT jobs, you can build and run your own pipelines that’ll let you store data into your data lakes. It also allows data engineers to prepare data using an integrated development environment (IDE) or notebook of their preference.

Key features:

• Store metadata in data catalogs
• Automatically identify the schema of your data
• Maintain historical records of all data schema modifications
• Transform your data with a visual drag-and-drop interface

Use ELT tools for data-driven decision-making 

In 2020, businesses generated 64.2 zettabytes of data across industries. With so much data, companies need a new way to store, process, standardize and analyze it. 

ELT tools help businesses simplify the entire process of ingesting data and transforming it so that it becomes easy to analyze. 

Access to centralized data in real-time helps them understand customer behaviors, patterns and trends. With this analysis, businesses can create better customer experiences and boost their bottom line. 

Companies like Intercom, Lufthansa, Square and DocuSign already use Fivetran to make data-based decisions. We help them:

• Automate data integration
• Perform parallel processing
• Synchronize data from a source location to any other location
• Transform data within the warehouse for real-time and fast data analysis 

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