Extract, load, transform (ELT) tools offer no-code connectors that sync data from systems across the enterprise into a data warehouse or data lake to power analytics and process automation.
Instead of writing custom scripts - extracting data from source systems, formatting it for delivery, and loading it into a data warehouse - ELT software offers teams a no-code solution. Users simply authenticate with the source system, configure a destination environment, and start the flow of data.
How do ELT tools create value for users?
- Improve strategic decision making by powering new data-driven insights and dashboards.
- Reduce work for data engineers and data analysts that would otherwise build and maintain connectors.
- Streamline manual workflows and save time by powering warehouse centric process automation.
ELT tools are core to the modern data tech stack. When starting out with data analytics, most companies will hire a data analyst and leverage an ELT tool, a data warehouse, and a visualization tool to power insights.
Who uses an ELT solution?
Data engineers and data analysts are tasked with managing enterprise data infrastructure - including data integrations. They are the typical buyers and users of ELT tools.
Data engineers and analysts see the immediate value of connecting to a new system to power the next dashboard, or generate the next insight. Once they’ve decided to connect to a system, they make a build vs. partner tradeoff - determining whether to script the connector in-house, build on top of an open source solution, or leverage an off-the-shelf solution from an ELT provider.
What data can ELT tools extract?
There are common data types that ELT tools connect to and extract data from. Particularly, application programming interfaces (APIs), files, databases, warehouses, event sources, and webhooks. Using these building blocks, ELT tools can extract data from almost any tool across the enterprise and load information into a centralized analytics environment ready to query.
What are the most common data sources for ELT tools?
- Accounting And Bookkeeping Software
- Advertising Platforms
- Applicant Tracking Systems (ATS)
- Collaboration Tools (i.e. Messaging Systems)
- Customer Data Platforms (CDPs)
- Customer Relationship Management (CRM) Systems
- E-Commerce Platforms
- Email Service Providers (ESPs)
- Enterprise Resource Planning (ERP) Systems
- Human Resource Information Systems (HRIS)
- Personalization And Customer Engagement Tools
- Subscription Billing Software
- Ticketing And Support Software
Note: ELT solutions are not data collection tools. If you need to start tracking notes on meetings, or collecting product signals from your website or mobile app, you should find the correct tool for the job, and then have the ELT solution extract the data from that system and load it into your data warehouse.
What is the difference between ETL and ELT?
Extract, transform, load (ETL) tools differ from ELT solutions because they have more complexities baked into the configuration and delivery process. Instead of relying on a cloud warehouse to transform data into insights once the information has been loaded, they transform the data while it is in the process of being delivered.
Before cloud data warehouses and data lakes came into existence, centralized analytics environments were nothing more than a physical database, and they were limited by physical storage and compute. ETL tools were created during this era when it was the job of the integration tool to ensure data being delivered didn’t break the analytics environment. They had to summarize and aggregate the data before loading it into the destination.
With the advent of cloud data warehouses, you can now replicate data in its raw form at almost infinite scale and then leverage the scalable compute of the warehouse to turn raw data into insights. As a result, ELT tools offer two major benefits: 1) they are simpler to get started, because you can replicate all of the data at its original scale without needing any transformation, and 2) they can manage transformation once data has been delivered to the warehouse by tapping into the scalability of the cloud data warehouse.
Today, there are very few scenarios where a company that has a cloud data warehouse would need, or want, the complexity of an ETL solution, and most companies are moving to ELT based architectures for the simplicity, speed, and scalability.
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