The world runs on big data and lots of it. Businesses need an efficient way to handle all that data to create effective business strategies. This is where ELT comes in.
ELT is an acronym for “extract, load, and transform.” It describes a data integration process that extracts, loads, and transforms data from one or more sources into a repository such as a data warehouse or data lake.
The ELT process consists of three steps:
For a more in-depth overview, check out “What are the differences between ETL and ELT?”
ELT can be thought of as the newer, more modern variation of the traditional ETL (extract, transform, and load) process. It differs from ETL in a couple distinctive ways.
With traditional ETL, relevant data is transformed before it is uploaded to a data warehouse, and then it must be pushed out of the warehouse for analysis or processing. This data pipeline works, but it can take more time to migrate data from the source to the target system.
The cloud-native ELT process saves you steps — and time. Data is first loaded into the target ecosystem such as a data lake or data warehouse, and then transformed. Authorized users can securely access the data without returning it to source systems. No download is necessary.
There are reasons to continue using ETL tools. For example, some companies want to keep all their data on-premises. If there’s a small amount of data, and it’s relational and structured, traditional ETL is effective for businesses that favor hands-on data integration. However, the ELT approach has several benefits for most industries.
ELT allows you to integrate and process large amounts of data — both structured and unstructured — from multiple servers. Both raw and cleansed data can be accessed with artificial intelligence (AI) and machine learning (ML) tools in addition to SQL and NoSQL processing.
ELT doesn’t have to wait for the data to be transformed and then loaded. The transformation process happens where the data resides, so you can access your data in a few seconds — a huge benefit when processing time-sensitive data.
Larger enterprises typically have multiple, disparate data sources like onsite servers, cloud warehouses and log files. Using ELT means you can combine data from various data sets regardless of the source or whether it is structured or unstructured, related, or unrelated.
Technological advances allow organizations to collect petabytes (a million gigabytes!) of data. ELT streamlines the management of massive amounts of data by allowing raw and cleansed data to be stored and accessed. If you’re planning to use cloud-based data warehousing or high-end data processing engines like Hadoop, ELT can take advantage of the native processing power for greater scalability.
ELT reduces the time data spends in transit and doesn’t require an interim data system or additional remote resources to transform the data outside the cloud. Plus, there’s no need to move data in and out of cloud ecosystems for analysis. The more your data moves around, the more the costs add up. The scalability of ELT makes it cost-effective for businesses of any size.
Transforming data after uploading it to modern cloud ecosystems is most effective for:
The ELT process improves data conversion and manipulation capabilities due to parallel load and data transformation functionality. This schema allows data to be accessed and queried in near real time.
However, you might want to stick with ETL if you have dirty data; like duplicate, incomplete, or inaccurate data that will require data engineers to clean and format prior to data loading.
If you need to transform large amounts of data, you’ll likely need a data management solution that includes ELT. A combination of ETL and ELT is often necessary for enterprise businesses. A software development company specializing in AI and cloud-native data integration can help determine if the ELT process is right for you. Then, you can create a flow, define the business logic and push the processing to cloud data warehouses and data lake ecosystems like Amazon Web Services (AWS), Microsoft Azure, Google, Salesforce, Databricks and Snowflake, so the processing can happen locally there.
ELT enables limitless data management and analysis. You can run complex integrations at scale without being a seasoned data engineer. A cloud platform including thousands of prebuilt AI-driven functions and templates allows you to perform codeless integration with ease.
You can move your data freely between any number of cloud ecosystems and access it anytime.
Business intelligence requires masterful data collection, data storage, data transformation and data analysis. When your company processes data faster using ELT, you can quickly deliver projects and identify and eliminate inefficiencies sooner.
While speed is important, you should also optimize data governance and security and remember to keep the end-user experience in mind so your organization’s data is easy to access and use. ELT checks all the boxes for these business requirements.
ELT works wonders for healthcare patient satisfaction, care coordination and value-based care. Because ELT securely extracts, loads, and transforms both structured data and unstructured data, it can quickly compute data from electronic health records (EHR), electronic medical records (EMR), practice management software, patient portals, remote patient monitoring and other data storage systems used by healthcare entities.
With ELT, Intermountain Healthcare can load 300 CSV files in 10 minutes1 — a process that used to take a week. There’s no need for hand coding because the data transformation process is automated. With ELT making the data more understandable and highly digestible, data analysis is a snap.
Some government agencies and educational institutions may prefer to keep their data on their premises rather than in the cloud. While the ELT process is secure, it might not be the best schema for them. Rest assured though that public sector organizations can integrate complex siloed data, perform self-analyses, comply with regulations, achieve cross-agency collaboration, and modernize their operations with ELT and cloud-based ecosystems.
One of New York City’s largest child welfare organizations — The New York Foundling — securely loads and transforms data from Netsmart, Office Practicum, UltiPro, ServiceNow and Microsoft SQL Server in the cloud and quickly delivers it to EHRs. Social workers can access care plans on the go, so they can spend more time with clients. They can also easily collaborate with other client care providers.
ELT helps propel manufacturing into the future by rapidly integrating data from production lines with warehouse systems. It also connects ecommerce data sources and touchpoints such as CRM, PIM, ERP, and CPQ systems. Data processing at scale provides quick access to customer and product data — useful for initiatives to increase efficiency and improve customer service. Plus, analysts can process vast quantities of data where the data resides — and get results in seconds.
Rapid access to relevant data allows businesses operating in the manufacturing sector to make sound data-driven decisions that boost production and ensure resiliency. For example, ELT helps Rockwool integrate and analyze data from offices and manufacturing facilities in 39 countries. Improved data flows have increased overall sales by 23 percent.2 They’ve been able to create automated guided vehicles and warehouse robotics for stock picking and fulfillment.
ELT is a trusted way for financial institutions like banks, capital markets and insurance agencies to successfully function in agile environments, combat fraud, comply with government regulations and promote consumer satisfaction by providing personalized, secure and contextualized interactions with their financial institution.
Western Union stays relevant in today’s financial service market with ELT as part of its data management system. They process more than 1,700 transactions a minute and need to cost-effectively deal with complex data.3 ELT quickly transforms raw data into information that’s consumable by people, apps, and AI, so Western Union can grow their web and mobile channels and provide a personalized customer experience.
Retailers need customer data to personalize the customer experience and increase revenue. ELT delivers relevant, timely data compiled from as many sources as necessary. And you can read and write complex queries without knowing XML, JSON, AVRO or other coding languages.
Data was a game-changer for the Chicago Cubs. They needed to integrate and use data from 24 sources, including CRM, wireless, social media, and ticketing data. Thanks to ELT and intelligent data management, they’ve made faster, more profitable decisions, opened new lines of revenue, and strengthened customer loyalty.
Cloud-based, AI-powered data management is essential for business success. ELT offers the speed and flexibility you need for forward-looking business intelligence. It easily and securely integrates data from multiple sources and provides actionable metadata at scale. Informatica is a cloud-native data management leader at the forefront of SaaS and cloud ecosystem innovation. We can help you accelerate digital transformation.
1 Intermountain Healthcare internal data.
2 Rockwool internal data.
3 Western Union internal data.