ELT, or Extract, Load, Transform, is a process in data warehousing where data is extracted from one or more source systems, loaded into a destination system, and then transformed into a format that can be used by downstream applications like business intelligence.
Though the ETL process is still popular, ELT is starting to become more widely used when migrating data from one system to another, or when creating a cloud data warehouse. The transformation step is often the most complex part of the process, as it may involve complex data cleansing, aggregation, and other data processing tasks. Read on if you want to see if the ELT process is for you.
An ELT tool can be very beneficial for organizations that want to leverage analytics and develop data driven decision making in their businesses. ELT is an essential step in building modern data pipelines, getting the data from source systems like points of sale or mobile applications into a central system, then manipulating the data into a usable, useful format. The most effective of these pipelines are often built by an analytics engineer, who leverage software principles and best practices for data processes. This can lead to a variety of benefits, including:
Too much data movement can slow down any analytics project. An ELT tool can help centralize your data in a cloud data platform such as Snowflake, Databricks, Amazon Redshift, or Google BigQuery. By doing this, data arrives ready for analytics after it’s been extracted. This makes it easier to transform data inside cloud data warehouses instead of outside them, which avoids the long, painful, and expensive process of moving data from platform to platform.
The right ELT process allows you to quickly and easily bring data from a variety of sources into a destination where it can be analyzed and used. When paired with the right self service analytics platform, this creates a seamless means to identify trends and patterns in your data quickly, so they can be acted upon.
Extracting, loading, and transforming data can be a time-consuming process. This is especially true with more antiquated extract, transform, load (ETL) frameworks. However, by using an ELT tool, you can automate this process, saving time and resources along the way.
Organizations’ use of data will continue to change dramatically over the coming years. As your data needs shift, you can easily scale up or down your use of an ELT tool as needed. This flexibility makes it easy to keep pace as market conditions, customer expectations, and technology continue to evolve.
Data security is a top concern for many organizations. By using an ELT tool, you can help to ensure that your data is well-protected from end to end, giving you peace of mind without sacrificing flexibility or scale.
When comparing ELT vs ETL tools, ELT tools are typically much less expensive to purchase and implement. They can provide significant cost savings for your organization, both in terms of actual spend on software, as well as resources and talent required to maintain them
The most successful, modern organizations have adopted ELT as a critical process to make data more useful by getting it out of source systems and into a central platform in a manner ready for analysis and use. While there are many different use cases for ELT, each with its own set of benefits, here are three common ELT use cases.
Many businesses use a variety of apps to manage customer data and track sales or other processes like Marketo, HubSpot, ServiceNow, and Jira. Extracting this data from these applications can be difficult and time-consuming without an automated ELT process. By using an ELT process, companies can quickly and efficiently extract the necessary data from their business apps and move them into a centralized performant cloud.
Data warehouses are a common destination for business data. In most cases, this data originates from a different source system, like an ecommerce application or CRM system, that needs to be brought into a data warehouse or platform to be leveraged. ELT can be used to load this data into the data warehouse. This can be done using SQL or other tools.
In some cases, businesses need to transform data to create a new dataset. This can be done for many reasons, such as to combine data from multiple sources, cleanse data, or to format data in a specific way. ELT can be used to transform data. This can be done using SQL or other tools. Once the data is transformed, it can be used for analysis in tools like ThoughtSpot.
There are many different tools that can be used for ELT, but the most common in the modern data stack include:
Airbyte is an open-source ELT tool that helps you unify your data integration pipelines under one fully managed platform, so you can easily move your data where you need it. It's easy to use and lets you replicate your data in minutes with over 200+ pre-built and customer connectors to fit your specific needs. Plus, Airbyte integrates with a variety of popular data storage platforms, making it a great option for anyone looking to move their data to a new platform or simply build in whatever way your organization needs. Some of Airbyte's customers include Dribbble, Mercato, and Safegraph.
Fivetran is a leading ELT platform that helps organizations centralize and transform data. Fivetran enables companies to quickly and easily connect their data from any source - including databases, applications, cloud services, and more - to their data warehouse or business intelligence solution for analysis. Fivetran provides over 160+ fully managed connectors that continuously and automatically update to save you time. Some of Fivetran's customers include Dropbox, Hubspot, and Zendesk.
Matillion is a cloud-native ELT tool that makes it easy to collect, clean, and prepare data for analysis. It includes a drag-and-drop interface that makes it easy to get started, and it offers a variety of features to help you get the most out of your data. With no hardware or software requirements, Matillion leverages the performance and scale of the cloud, making complex data transformation fast, secure, and cost-efficient. Some of Matillion's customers include Accenture, Docusign, and Merck.
In today's business world, data is key to success. ELT helps bring that data together in a format that’s useful for businesses. For more organizations, however, this leaves the last mile - getting data into the hands of business users who can make decisions - still a challenge.
That's why having self-service analytics is essential for any company that wants to make the most of its data. ThoughtSpot enables everyone within an organization to limitlessly engage with live data once it completes the ELT process in a cloud data warehouse, making it easy to create personalized, actionable insights through Live Analytics. With ThoughtSpot, you can easily and quickly create reports and Liveboards from your data without relying on IT or even knowing SQL. If you're looking for an easy-to-use analytics solution to help you get more insights from your data, sign up for a ThoughtSpot free trial today!