ETL and Business Intelligence: How Data Pipelines Support Informed Decisions in 2026

Your BI dashboards are only as good as the data feeding them. ETL is what makes that data usable. This article covers the three stages of ETL pipelines, compares ETL and ELT approaches for different organizational needs, and walks through best practices for building data workflows that power accurate, timely business intelligence.

Key takeaways

Here are the main points to keep in mind:

• ETL (Extract, Transform, Load) forms the foundation of effective business intelligence by converting raw data into analysis-ready insights
• Modern BI requires choosing between ETL and extract, load, transform (ELT) approaches based on your data volume, transformation complexity, and infrastructure
• The right ETL tool for your BI stack depends on your technical resources, data sources, and whether you need real-time or batch processing
• Following ETL best practices like data quality validation and automated monitoring prevents costly analytics errors
• Domo's Magic ETL provides drag-and-drop data transformation within a complete BI platform, eliminating the need for separate tools

To drive a data-driven business, you need to first understand how to gather and act on data you trust. The first step of any data process is called Extract, Transform, Load (ETL). This is the heart of data transformation, providing a process to combine and transform data from disparate sources. The result? Businesses make more informed decisions with less delay.

Traditional methods of data transformation no longer cut it. Data arrives at an ever-increasing rate and in formats that would have seemed exotic a decade ago. Volumes have reached a point where manual processing is not just inefficient. It is impossible. ETL provides the technical framework and necessary process to quickly and easily move this data to where it needs to be while transforming it into the correct format for downstream applications.

Data is increasingly becoming interconnected through your company's various systems. Valuable, yes. Sharing impactful data across business units opens new possibilities. But it also adds layers of complexity. This interconnectedness means your ETL processes need to focus on transforming all data, no matter the source, into a normalized format that can be combined with data from other sources. You get a comprehensive view into many different areas of your business that can overlap. By standardizing on common platforms and data models, companies gain a new level of insights and intelligence.

ETL plays a leading role in this transformation by providing the necessary capabilities for data ingestion, cleansing, transformation, and loading into target systems. It enables businesses to quickly get value from their data by helping to:

• Filter out noise and redundancy in data from multiple sources
• Create a unified view of data from different systems
• Integrate data from legacy applications

ETL bridges operational, transactional data sources with big data for analytics or business intelligence.

What is ETL?

Extract, Transform, and Load (ETL) is a process for extracting data from various sources, cleaning and transforming it into a unified format, and loading it into a target system. The ETL process can move data between different systems or load data into a data warehouse or big data platform.

Think of ETL as the data preparation backbone sitting between your source systems and your analytics tools. Without it, your BI dashboards would pull from messy, inconsistent, and siloed data that tells conflicting stories.

Here's how a typical ETL pipeline works in practice:

• Source systems: Salesforce (customer relationship management, or CRM, data), Stripe (payment transactions), and PostgreSQL (product database)
• : Pull customer records, payment history, and product usage data from each system
• : Deduplicate customer records across systems, standardize date formats to UTC, normalize currency fields to USD, and join customer IDs across sources
• : Write the cleaned, unified dataset to Snowflake or BigQuery
• BI output: A revenue dashboard showing customer lifetime value, churn risk scores, and product adoption metrics

The three main steps in the ETL process are:

• Extract: Extracting data from source systems into a staging area
• Transform: Cleaning and transforming the data into a unified format
• Load: Loading the data into a target system

Together, these three steps make up the core of an Extract-Transform-and-Load (ETL) solution.

The ETL process enables organizations to extract, cleanse, and load data across different systems. It uses a staging area as a "pre-production" environment where data is transformed and prepared for loading into a target system.

Extract: pulling data from source systems

The extraction phase captures data from your source systems and moves it into a staging area for processing. How you extract data depends on your sources, data volume, and freshness requirements.

Common extraction methods include:

• Full extraction: Pulls all data from the source system each time the pipeline runs. Simple but inefficient for large datasets.
• Incremental extraction: Captures only new or changed records since the last extraction, typically using timestamps or sequence numbers.
• Change data capture (CDC): Monitors database transaction logs to capture inserts, updates, and deletes in near-real-time.
• Application programming interface (API) polling: Periodically calls representational state transfer (REST) or GraphQL APIs to retrieve updated records from software as a service (SaaS) applications.
• Webhooks: Receives push notifications from source systems when data changes, eliminating the need for polling.

For most BI use cases, incremental extraction or CDC provides the right balance between data freshness and system performance. Full extractions work well for small reference tables or initial data loads. Webhooks and API polling are essential for SaaS data sources that don't expose database-level access.

Transform: converting data into usable formats

This is where the magic happens. Raw data becomes analysis-ready. This phase handles everything from basic cleanup to complex business logic that shapes how metrics appear in your dashboards.

Without proper transformations, your BI reports will suffer from predictable problems. Here are common data issues and their ETL fixes:

Key transformation operations include cleansing (removing invalid records), deduplication (merging duplicate entries), normalization (standardizing formats and values), enrichment (adding calculated fields or lookup data), and aggregation (pre-computing summaries for shorter query times).

Load: delivering data to target destinations

The load phase writes transformed data to your target system, whether that's a cloud data warehouse, data lake, or operational database. Your loading strategy affects both pipeline performance and query speed in your BI tools.

Two primary loading approaches exist:

• Full load: Replaces the entire target table with fresh data. Simple to implement but slow for large tables and creates brief windows where data is unavailable.
• Incremental load: Appends new records and updates existing ones using merge or upsert operations. More complex but much more efficient for large datasets.

Most BI workloads target cloud data warehouses like Snowflake, BigQuery, or Amazon Redshift. These platforms handle the heavy lifting of query optimization, letting your dashboards run fast even against billions of rows. Data lakes (like Databricks or Amazon S3 with Athena) work well when you need to store raw data for future exploration or machine learning workloads alongside your BI use cases.

What is ETL in business intelligence?

ETL serves as the foundational framework for business intelligence (BI) by extracting raw data from diverse databases, transforming it into a standardized structure, and loading it into a data warehouse or data tool that lets your team visualize and interpret the data.

As you build out your ETL processes, keeping actionable insights for BI in mind ensures your ETL processes transform the data into usable information further down the decision-making process. When your ETL processes are built to support BI, your company can harness the full power of your data, uncovering trends, patterns, and relationships that drive strategic decision-making and ultimately contribute to the overall success of the organization.

Here's a quick summary of how ETL powers BI:

• Source systems: CRM (Salesforce, HubSpot), enterprise resource planning (ERP) systems like NetSuite and SAP, marketing platforms (Google Ads, Meta Ads), product databases
• Key transformations: Customer record deduplication, revenue attribution modeling, date standardization, currency conversion, metric calculations
• Destination: Cloud data warehouse (Snowflake, BigQuery, Redshift)
• BI output: Executive dashboards showing pipeline health, customer acquisition costs, revenue trends, and operational KPIs

Why ETL matters for business intelligence success

ETL enables companies to get more value out of the data assets they already have. It helps them integrate existing systems, enable analytics, and increase performance management capabilities by standardizing on common platforms and data models.

Filter out noise and redundancy across data sources

One of the main benefits of ETL is that it helps companies filter out noise and redundancy in data from multiple sources. This can be done by extracting data from source systems into a staging area to be cleaned and transformed.

Take a business-to-business (B2B) software company that wants to analyze customer health scores across their entire customer base. Multiple data sources are available for this purpose:

• CRM records: Salesforce contains account information, contract values, and renewal dates. But these records often have duplicates from merged accounts and inconsistent naming conventions.
• Product usage data: Application logs show feature adoption, login frequency, and support ticket volume. However, user IDs don't always match CRM contact records.
• Billing system: Stripe or Zuora contains payment history and subscription changes, but customer identifiers use different formats than the CRM.

The software company can extract data from all of these sources and cleanse and transform it into a unified format by using ETL. This helps them get a more complete view of customer health and make more informed decisions about retention and expansion efforts.

Create a unified view of customer and operational data

Another benefit of using modern data transformation (ETL) tools is that it helps to create a unified view of data from different systems. Extract data from source systems into a staging area, transform it, and load it into the target system.

For example, let's say a business wants to create a view of customer data that combines information from multiple sources. These sources may include transactional systems (such as ERP software), marketing automation systems, and other internal and external sources. By using ETL, the company gets the unified view of customer data they need while still allowing access to source data systems.

Enable self-service analytics and timelier reporting

ETL enables people to more easily perform their own analytics on the data they have access to, whether or not it is stored in a centralized location. ETL provides a more unified view of data from multiple systems and sources that can be transformed into a format that is easier for people to understand.

When analysts don't have to wait for IT to prepare data or manually combine spreadsheets, they can answer business questions in hours instead of weeks. This shift from IT-dependent reporting to self-service analytics removes bottlenecks and lets teams move at the speed of business.

ETL also supports business intelligence (BI) visualizations. A leading BI application like Domo allows people to connect, transform, and visualize data (and so much more) all within a single product. Using a BI application that lets you do all of this in one system allows businesses to get more value from their data and make more informed decisions based on that data.

Support performance tracking and benchmarking

ETL can also help businesses track and improve the performance of their business processes. Standardizing a common data model across different systems makes this possible.

By using ETL, businesses can create benchmark metrics within their datasets that compare actual performance against desired performance. For example, you might track metrics like customer acquisition cost against industry benchmarks, sales cycle length compared to historical averages, or support ticket resolution time versus service-level agreement (SLA) targets.

ETL vs ELT: choosing the right approach for your BI strategy

The traditional ETL approach transforms data before loading it into your warehouse. ELT (Extract, Load, Transform) flips this sequence, loading raw data first and transforming it inside the warehouse using structured query language (SQL). But there's also a third option that many teams overlook: BI-native data preparation tools like Power Query, Tableau Prep, and Domo Magic ETL.

Each approach fits different scenarios. Choosing wrong can create technical debt that slows your team down for years.

The right choice often depends on your organization's maturity stage:

• Startup/prototype stage: BI-native prep tools let you move fast without infrastructure overhead. Power Query in Power BI or Magic ETL in Domo can handle most early-stage transformation needs. Build complex, multi-step transformations outside your BI tool if you plan to scale.
• Scale-up stage: ELT in a cloud warehouse (Snowflake, BigQuery, Redshift) with dbt for transformation logic gives you the flexibility to handle growing data volumes. Keep documentation and testing in place even when you're moving quickly.
• Enterprise stage: A governed approach combining dedicated ETL/ELT tools with a semantic layer ensures consistent metrics across teams. Set central oversight so each team builds pipelines within shared standards.

Many organizations use a hybrid approach: ELT for heavy data processing, with BI-native prep for last-mile transformations that people in the business can manage themselves. And honestly, assuming you must pick just one approach is where most teams trip up. Mature data teams blend methods based on the specific use case.

Common data sources for ETL and BI integration

ETL pipelines need to handle data from wherever your business generates it. Modern organizations typically integrate data from multiple sources, often dozens or even hundreds, each with its own format, API, and update frequency.

Databases and transactional systems

Your core business systems generate the most critical data for BI:

• Relational databases: PostgreSQL, MySQL, SQL Server, and Oracle contain transactional data from custom applications
• ERP systems: NetSuite, SAP, and Microsoft Dynamics hold financial, inventory, and operational data
• CRM platforms: Salesforce, HubSpot, and Microsoft Dynamics 365 track customer relationships and sales pipelines
• E-commerce platforms: Shopify, Magento, and WooCommerce store order and product data

These systems typically support CDC or incremental extraction.

Cloud applications and APIs

SaaS applications have become primary data sources for most organizations:

• Marketing platforms: Google Ads, Meta Ads, LinkedIn Ads, and marketing automation tools like Marketo or Pardot
• Product analytics: Amplitude, Mixpanel, Segment, and custom event tracking systems
• Support systems: Zendesk, Intercom, and Freshdesk for customer service metrics
• HR and finance: Workday, ADP, and expense management tools

Most SaaS applications expose REST APIs for data extraction. Some offer native integrations with popular ETL tools, while others require custom API connectors. Webhooks provide real-time updates for platforms that support them, reducing the lag between when something happens and when it appears in your dashboards.

Best practices for ETL in business intelligence

Building ETL pipelines that reliably power BI dashboards requires more than just moving data from point A to point B. These practices help ensure your data stays accurate, your pipelines stay healthy, and your stakeholders trust what they see.

1. Validate data quality at every stage through rigorous ETL testing. Assume source data needs validation. Build checks into your pipeline that catch problems before they reach your dashboards.
2. Design for idempotency. Your pipeline should produce the same result whether it runs once or ten times. Use deduplication keys and upsert/merge patterns so that reprocessing historical data or recovering from failures does not create duplicate records. Teams often overlook this until a failed job runs twice and suddenly customer counts double overnight.
3. Document transformation logic. When someone asks why a number looks wrong, you need to trace it back to the source.
4. Monitor pipeline health proactively. Set alerts so you can catch a broken dashboard before a stakeholder does.
5. Version control your transformations. Treat your ETL code like application code. Use git, write tests, and review changes before deploying to production.
6. Start simple and iterate.

Here's a data quality checklist to build into your pipelines:

Real-time ETL and modern BI architectures

Before diving into architecture patterns, it's worth clarifying what "real-time" actually means. The term gets thrown around loosely, but the distinction matters for both cost and complexity.

True real-time means sub-second latency, typically achieved through event-driven architectures with stream processors like Apache Flink or Kafka Streams. Expensive to build and operate. Most BI use cases don't actually need it.

Near-real-time means data freshness measured in minutes, typically refresh windows of one to 15 minutes. This is achievable with micro-batch ELT in cloud warehouses and satisfies the vast majority of BI dashboard requirements at a fraction of the cost.

The question to ask: Would your business decisions change if data was five minutes old instead of five seconds old? For most dashboards, the answer is no.

Three reference architecture patterns dominate modern BI implementations:

• CDC to warehouse to ELT: Change data capture tools (Fivetran, Airbyte, Debezium) stream changes to your cloud warehouse, where scheduled dbt jobs transform data every five to 15 minutes. Best for teams with existing warehouse infrastructure who want improved freshness without streaming complexity.
• Event streaming to stream processor to online analytical processing (OLAP) serving layer: Kafka or similar captures events, Flink or Kafka Streams processes them in flight, and results land in a real-time OLAP database (ClickHouse, Apache Druid, Apache Pinot). Best for sub-second KPI tiles and high-concurrency dashboards where thousands of people hit the same metrics simultaneously.
• Micro-batch ELT with orchestration: Airflow, Dagster, or Prefect schedules frequent incremental loads (every five to 30 minutes) with dbt transformations. Best for teams that want freshness improvements without the operational burden of true streaming.

One architectural component that often gets overlooked is the semantic layer. ETL gets your data into the warehouse, but it does not guarantee that everyone calculates "revenue" or "active customer" the same way. A semantic layer (dbt metrics, Looker LookML, Cube, or similar) sits between your warehouse and BI tools, enforcing consistent metric definitions across all dashboards and preventing the metric drift that plagues organizations with multiple BI tools or teams.

Integrating ETL into your business

To maximize the benefits of using ETL, it's essential to find a modern BI tool that allows you to standardize on a common platform and data model for all of your internal and external systems.

Some BI tools include ETL capabilities, while others keep ETL separate. But ETL within your BI tool is critical for modern businesses. ETL tools enable businesses to benefit from their data in a number of ways: they can integrate data from different systems into a single view, help people perform analytics and BI on the data available to them, and enable businesses to track and improve performance.

Beyond tool selection, successful ETL implementation requires thinking about data contracts and schema change handling. Data contracts define the expectations between data producers and consumers: what fields are guaranteed to exist, what data types are expected, and what happens when a source schema changes. Without these agreements, a well-meaning change to your CRM's custom fields can silently break downstream BI reports.

Build schema change detection into your pipeline so that source-side updates trigger alerts rather than silent failures. Tools like Fivetran and Airbyte can auto-detect schema changes and notify your team before they cause problems.

Top ETL tools for business intelligence in 2026

There are any number of ways your company can incorporate ETL into your organization. While building an ETL pipeline is always something you can consider, there are a number of tools already available that will help you manage the process efficiently and get you up and running with your data quickly.

Before evaluating individual tools, it helps to understand the categories:

• BI platforms with built-in ETL/prep: Domo, Power BI (Power Query), Tableau (Tableau Prep). Best for teams that want an integrated experience without managing separate tools.
• Dedicated ETL/ELT tools: Fivetran, Airbyte, Matillion, and Informatica handle complex data integration needs, but they add another tool to manage, so teams that want ETL inside their BI platform may prefer Domo.
• Reverse ETL tools: Census, Hightouch. Best for pushing warehouse data back to operational tools like CRMs and marketing platforms.
• Integration platform as a service (iPaaS) connectors: MuleSoft, Boomi, Workato. Best for application integration beyond just analytics use cases.

Here are some effective ETL tools your company can consider to help you build an ETL process:

Domo

Domo is a cloud-based business intelligence and data integration platform that streamlines ETL processes to deliver real-time insights and data visualization, offering an interface that works for both novices and technical people. Domo's drag-and-drop Magic ETL tool is a no-code transformation layer that operates natively within the BI platform, so teams can build and manage data pipelines without leaving their analytics environment or standing up a separate pipeline tool.

Magic ETL works for people without advanced SQL expertise. But it also includes advanced features that help technical people handle more advanced data transformation.

Domo's key strengths lie in its pre-built data connectors, simplifying the ETL process, and its support for the entire data lifecycle, from connection to sharing insights.

Because Domo provides a comprehensive end-to-end business intelligence solution, its extensive capabilities may exceed the needs of companies solely focused on ETL.

Apache NiFi

Apache NiFi supports automated data flows between systems, but it requires technical setup and has limited built-in transformation, so Domo may be a simpler fit for teams that want ETL and BI together. It emphasizes secure data governance, offering routing, transformation, and connectivity to diverse data sources, supporting various formats. While free, scalable, and extensible, it requires some technical expertise for setup and configuration.

Talend

Talend, a Qlik company, offers a broad suite for data integration, transformation, and quality, but it often requires custom pipeline work, so Domo may be easier for teams that want a more integrated BI and ETL workflow. People need to build custom data pipelines for each source, but once they create them, the platform offers flexible data use. Key features include a wide range of connection abilities, data transformation capabilities, and a user-friendly graphical interface. Pros include strong community support, a rich feature set, and compatibility with big data and cloud integration. Some advanced features may require a paid version, and managing complex workflows can be challenging.

Alteryx

Alteryx simplifies data blending and analysis through a drag-and-drop interface, but advanced features can take time to learn and may require premium licensing, so Domo may be simpler for teams that want ETL inside the BI platform. It offers a suite for data manipulation, predictive analytics, and integration with popular visualization tools. While known for its accessibility and thriving user community, there's a learning curve for advanced features, and some functionalities may require a premium version.

Informatica

Informatica offers enterprise-grade data integration, quality, and governance, but its cost and complexity can be high for smaller teams, so Domo may be a more practical fit when you want BI and ETL in one platform. Key features include a comprehensive suite supporting cloud and on-premises deployments. Pros encompass enterprise-grade capabilities, data quality features, cloud integration, and scalability. However, enterprise-level tools entail a higher cost, and the product may be complex for smaller organizations.

Microsoft SQL Server Integration Services (SSIS)

Microsoft SQL Server Integration Services integrates closely with SQL Server for data integration and transformation, but it is Windows-centric and may require SQL Server licensing, so Domo may be easier for teams that want a cloud-based BI and ETL platform. Key features include tight integration with the Microsoft ecosystem, a visual design interface, and support for various data sources. Pros include inclusion with SQL Server, user-friendliness for Microsoft people, and data transformation capabilities. However, it is Windows-centric, and licensing for SQL Server may be required.

No matter the tool you decide to use, having ETL processes in place ensures your company can build and act on your data.

How to measure ETL success in your BI environment

Building ETL pipelines is only half the battle. You also need to know whether they're working well. These metrics help you track pipeline health and demonstrate value to stakeholders.

Beyond these operational metrics, track business impact: How many hours per week do analysts save? How quickly can the business answer new questions? Are stakeholders actually using the dashboards you've built?

Set up automated monitoring for freshness, error rates, and volume anomalies. Tools like Monte Carlo, Datadog, or built-in warehouse monitoring can alert you when something goes wrong before a stakeholder notices a broken dashboard.