Here are 10 of the most important ELT use cases in 2025.
The world runs on data. And that means the world runs ELT, because medium-sized and enterprise orgs across dozens of industries are leveraging Extract, Load, and Transform pipelines to get data where it needs to be and deliver services, information, and support that customers demand.
They span multiple industries, including finance, healthcare, retail, manufacturing, and technology. In each case, ELT is solving core business problems and driving significant bottom-line value.
1. Customer 360 analytics & personalization
ELT use case #1: customer analytics
Retailers and e-commerce companies struggle to unify customer data from online stores, mobile apps, CRM systems, ad campaigns, and social media. And they’re not the only ones: companies across all industries are challenged with consolidating customer data and making it actionable.
Siloed data makes it almost impossible to achieve a 360° customer view for personalization and loyalty. And that means you’re leaving money on the table in missed opportunities and inconsistent customer experiences.
The ELT solution: building a 360° customer view for personalization
ELT pipelines let businesses extract customer interaction data from all their channels, ad partners, and marketing platforms. Then they can load it into a central cloud data warehouse. Once there, they can transform it in-place when needed to build unified customer profiles.
When you consolidate purchase history, web behaviors, and demographics, you can generate personalized product recommendations, deliver smarter marketing campaigns, and present real-time offers that aren’t random.
Modern cloud data warehouses like Snowflake and BigQuery are ideal for this, since they handle large volumes of varied data and enable fast SQL transformations.
Tools like Extract can continuously ingest source data, while transformation frameworks like dbt apply business logic to create a single source of truth for customer metrics.
Unified data enables faster, more profitable decisions, new revenue streams, and stronger customer loyalty through personalized experiences. Retailers report higher conversion rates when they leverage ELT to compile timely customer data for personalization.
2. Fraud detection & financial risk analysis
ELT use case #2: fintech
Banks, payment processors, and other fintech companies need to detect fraud and assess risk in real time across millions of transactions to avoid scams and protect their customers. Traditional ETL processes and legacy systems can’t always keep up with the volume and velocity of financial data: transactions are happening from 10s to 100s of times per second, and need to be checked in real time.
The ELT solution: real-time fraud detection and risk management
ELT quickly loads raw transaction data, logs, and customer records into whatever cloud platforms you want. Then you can run fraud detection algorithms and anomaly queries directly on the data in BigQuery, Snowflake, or wherever it is.
By waiting to transform until after loading, you can ingest high-volume streaming data without bottlenecks. Then you can flag suspicious patterns using the full power of your endpoint, leveraging the massive parallel processing of cloud warehouses to analyze transactions and apply machine learning models for fraud scoring or risk analytics.
Speed is critical, because some payments companies process thousands of transactions every minute.
Many banks use Snowflake or Databricks to analyze transactions and customer behavior in near real time, helping them run risk management assessments as needed.
3. Regulatory compliance & reporting
ELT use case #3: compliance
Industries like financial services and healthcare work with strict regulations like GDPR, HIPAA, and SOX.
To comply, organizations need to integrate data from multiple systems to produce accurate regulatory reports while ensuring data privacy and security. That’s hard: you’ve got sensitive data like patient records and financial transactions coming from siloed sources. To make it all work, you have to enforce consistent data governance, and maintain clean audit trails.
Non-compliance can result in heavy fines, not to mention serious reputational damage.
The ELT solution: streamlined compliance and secure regulatory reporting
ELT feeds a unified, governed data repository where you can bring raw data from multiple systems like core banking, electronic health records, or insurance systems. Because ELT loads before transformation, compliance teams have all the details in a single place for auditing, and the business can apply transformations like masking, encryption, and validation centrally within the warehouse without impacting the original audit trail.
For example, an ELT pipeline can load raw patient data into a secure healthcare data lake, then run transformations that anonymize or encrypt personal identifiers and filter data according to HIPAA rules before analysis.
Or in finance, ELT can aggregate and reconcile financial records for Basel III or other regulatory reports, while maintaining an unchanged raw data archive.
The result is faster and more secure reporting, with all the original data, all of it compliant with all applicable regulations.
4. Predictive maintenance & IoT analytics
ELT use case #4: IoT
Everyone wants digital twin tech. No-one realizes the data impact of today’s IoT tools that enable digital twins.
Manufacturers and industrial companies are flooded with IoT sensor data from machines, equipment, facilities, and vehicles. What they want is to be able to predict equipment failures and schedule maintenance proactively to reduce unplanned downtime.
But raw sensor readings on things like vibration, temperature, pressure, or unauthorized access can be high-volume and are often unstructured. Plus, they’re coming from many machines and factories, and aren’t always in some magical instantly compatible format.
Traditional ETL can’t easily keep up with the volume or variety for timely insights.
The ELT solution: predictive maintenance and IoT analytics at scale
ELT pipelines excel in IoT scenarios. They load raw sensor and machine log data directly into scalable data lakes or warehouses, then transform it into meaningful metrics and features for predictive analytics.
For instance, data from dozens of PLCs or IoT devices can be sent to a cloud data lake using Extract, and then transformed with cloud analytics engines or other tools. That can help enterprises compute rolling averages, detect anomalies, or just keep maintenance records. By doing transformations in-place on a cloud platform, manufacturers can leverage massive parallel processing to handle streaming data and complex calculations.
By integrating readings from machine sensors, operational systems, and maintenance logs into a single repository, companies can predict when equipment will need servicing. And that extends machinery life while also boosting production efficiency.
5. Supply chain & inventory optimization
ELT use case #5: supply chain & inventory
Medium and large enterprises in retail and manufacturing need to optimize their supply chains and inventory levels across warehouses, stores, and distribution networks. Keeping too much inventory is a drain on profitability. Too little impacts gross sales and impacts customer relationships.
The challenge is to consolidate data from multiple systems:
- ERPs (inventory, orders)
- WMS (warehouse management)
- Transportation systems (shipping status, GPS)
- External sources (supplier info, weather or market data)
Without integration, companies face stockouts or overstock, inefficient routes, and slow response to demand changes, impacting revenue and cost.
The ELT solution: smarter supply chain and inventory optimization
ELT enables the creation of an efficient supply chain control tower in a cloud data platform. Orgs can extract data from multiple systems including supplier databases, then load it all into a centralized cloud warehouse or lake.
Once loaded, BI teams can merge and cleanse these datasets. For example, they might align product codes between a procurement system and a sales system, or calculate key metrics such as inventory turnover, in-transit times, or forecast vs actual demand.
Because ELT can handle raw data in vastly different formats, you can aggregate supply chain data which is often heterogeneous. A global shipper, for example, might combine IoT container tracking data with weather and port data to optimize routing and delivery efficiency.
In addition, ELT makes it easier to share raw data with suppliers or partners so that everyone operates on the same facts.
6. Healthcare patient data integration & analytics
ELT use case #6: healthcare data
Healthcare providers and hospital systems need to improve patient outcomes and operational efficiency.
Data can help, but patient data is spread across EHR/EMR databases, lab systems, insurance claims, patient portals, and even wearable/remote monitoring devices. Of course, all these systems use different formats, so integrating and analyzing this data for insights is hard.
The ELT solution: integrated healthcare data for better patient outcomes
ELT can be transformative in healthcare thanks to its ability to securely combine structured and unstructured data at scale.
That means you can extract data from clinical systems, billing and claims systems, and newer sources like health apps or even IoT health devices, and then load it into a central database. There you can perform transformations to normalize medical codes, aggregate patient records, and de-identify data for privacy.
The result: raw health data transforms into analytics-ready datasets.
ELT can be invaluable for patient care coordination and analytics, enabling faster and more holistic insights. That means doctors can get near real-time insights on patients, leading to better care decisions.
Note:
Cloud providers often offer healthcare-specific data services like Google Healthcare Data Engine or Snowflake Healthcare & Life Sciences Data Cloud that leverage ELT to integrate data while maintaining HIPAA compliance.
7. Product usage analytics & SaaS metrics
ELT use case #7: product analytics
Tech companies, especially software as a service companies, need to understand how customers are using their software products. Detailed usage data like clicks, usage, feature interactions, and errors or crashes are essential for improving software, reducing churn, and understanding where to develop more features.
This telemetry data, however, is often large-scale, semi-structured, and separate from business data like customer info, payments, or support tickets. Relying solely on third-party product analytics tools will create silos and limit your ability to not just analyze but also contextualize issues and opportunities.
The ELT solution: unified product usage and SaaS metrics
The good news is that ELT tools like Extract can help companies build a client health dashboard platform using their own product analytics and CRM data. They can extract event data, get CRM information, add any sales notes, and also fetch records of support tickets.
Then, inside their data warehouse, orgs can transform and join these data sources to derive valuable SaaS metrics like feature adoption rates, user cohorts, conversion funnels, and usage patterns.
Because ELT gives you the raw event data, analysts can create new metrics when needed,or run ad-hoc analysis if problems arise.
We’re seeing more modern SaaS companies use ELT with tools like Extract to pull in Salesforce and Zendesk, for example, then dbt to transform and model the data. Now you have a unified view of product and customer data in one place, avoiding data silos and ensuring consistent KPIs across teams.
You can also reverse-ETL to feed any insights back into your CRM system, alerting sales or support staff to address any impending issues … or highlight which customers are ready for an upgrade.
8. AI and machine learning data pipelines
ELT use case #8: AI
Pretty much everyone in all industries is investing in machine learning and AI for their companies. That includes predictive models, marketing software, product instrumentation, data insights … the full gamut.
A major hurdle, however, is getting all the data.
Enter ELT and Extract.
With an ELT tool like Extract, you can prepare the huge amount of high-quality data your models will require. You can get it from whatever databases, data lakes, and external sources it might currently be in, bring it all together, and then clean it and put it to use.
The ELT solution: high-quality data for AI and machine learning pipelines
ELT is now a cornerstone for ML data pipelines.
Teams can centralize all their raw data and then put it to work building their models and refining their machine learning services. First, they’re loading it into a centralized platform, and then transforming it: joining data to create a training dataset, handling missing values, standardizing formats, and finding patterns
Of course, ELT enables maintaining historical raw data, which can be useful for training time-travel, meaning that models can be trained on various time slices of data without re-extracting data from your sources all over again. Machine learning models are only as good as the data they feed on, and ETL/ELT processes can help in the collection and standardization process.
9. Real-time analytics & operational dashboards
ELT use case #9: dashboards
This isn’t news: many if not most business decisions need to be made in real time or near-real-time.
To enable that, it’s helpful to have live dashboards and alerts. That might be for website performance, sales, or real-time stock level updates.
Here, ELT can do what older batch-oriented data integration pipelines can’t: deliver up-to-the-minute data to your BI tools. Without real-time data, you’re going to react slower to issues or opportunities.
The ELT solution: real-time analytics and operational dashboards
Modern ELT pipelines enable real-time or micro-batch data flow into analytics systems, giving you faster data. That means more response time, and generally better business outcomes.
For example, Extract can continuously load from multiple sources into a data warehouse or lake. Once inside, the transformations can run frequently or even essentially all the time, leveraging the warehouse’s compute to aggregate, filter, and make sense of your data with minimal latency.
The combination of ELT with streaming means companies can maintain live metrics like current inventory, live game player counts, or instant fraud alerts. Plus, operational dashboards and live business metrics enable dynamic decision-making. That means you can drive dynamic pricing based on demand, competitor prices, environmental conditions, or even buyer details.
10. Cloud data warehouse modernization & data consolidation
ELT use case #10: modernization
Not everyone is always on the cutting edge. That means that at any given time, many businesses are modernizing legacy data infrastructure. That might include on-prem data warehouses or siloed databases.
The business problem is dual: consolidating data from acquisitions or multiple systems, as well as upgrading to a more agile analytics environment. ELT can help modernize a company’s data stack, reducing cost and speeding response times.
The ELT solution: modernizing legacy systems and consolidating data
Data migration and consolidation into cloud data warehouses or lake houses is a perfect ELT task. It’s fast to simply extract data from source systems and load it into modern cloud repositories like Snowflake, Amazon Redshift, or Databricks Lakehouse, rather than trying to transform it where it is, or mid-journey.
Once all your data lands in the cloud, your BI team can perform any necessary transformations to reconcile schemas, cleanse data, and merge columns.
Enterprises across multiple sectors use ELT-based consolidation to create a single source of truth. The result is a scalable, low-maintenance environment that supports advanced analytics on previously siloed data. Now you can proceed quickly with other business requirements, including using your data for training AI systems.
Try Extract: the modern ELT platform
Extract is a super-modern ELT platform that is orders of magnitude faster and cheaper than existing legacy alternatives.
It’s also free to try, and free to use for unlimited sources and up to 1 million monthly rows. And you can get up to 100 million month rows starting at just $15/month.