Getting started with an ELT pipeline

As businesses grow, they get insights from a range of data sources to make informed decisions. Ad hoc reports, in-app reporting, and spreadsheets can get at pieces of the data. But often, you need to bring data together from multiple sources and reshape it to yield true business insights.

An extract, load, transform (ELT) data pipeline combines data from multiple sources to provide accurate and reliable business insights. Transforming large volumes of unprocessed data into a format that’s suitable for analytics isn’t easy, though. It requires breaking down siloed transformation workflows and a high degree of visibility into the data transformation processes.

This article explores ELT pipelines and how to design one that scales efficiently. You’ll also learn how dbt drives modern ELT pipelines, simplifying collaboration and data transformation.

What is an ELT pipeline?

An ELT pipeline is a modern data integration process following the Extract, Load, Transform sequence. Analysts first extract data from various sources and load it into a centralized data warehouse. After that, they transform the data within the data warehouse to extract insights.

ELT pipelines consist of four core components:

1. A cloud data warehouse for storing and transforming data.
2. A data integration tool for extracting and loading data.
3. A transformation framework for data modeling and preparation for analysis.
4. A business intelligence (BI) tool for insights and visualization.

The choice of tools determines how efficiently data moves and transforms in your ELT pipeline.

Why modern data teams choose ELT over ETL

Extract, transform, load (ETL) pipelines transform data before loading it into the warehouse. This requires analysts to anticipate data models and reporting needs in advance. Any change in business requirements often means redesigning complex workflows and schema mappings.

ETL was all the rage back when compute and storage cost multiples of what they do today. While ETL works, it’s generally an inflexible approach that doesn’t scale to meet today’s data transformation demands.

ELT flips this process. It uses the scalability of cloud compute and storage to enable faster, more flexible transformations. Data teams ensure consistent access and simplify governance by centralizing data in a warehouse. Automated tools for extraction and loading keep pipelines updated and resilient against schema changes.

In ETL, transformation happens before analysts even touch the data. In ELT, it occurs closer to the data so analysts can quickly iterate and build accurate models for analysis.

How to design your ELT pipeline

Designing an effective ELT pipeline

Designing an ELT pipeline begins with a scalable data warehouse that acts as the main hub for all your data.

When planning the warehouse:

• Focus on scalability and elasticity to handle changing workloads.
• Ensure governance to stay compliant and keep access secure.
• Use cloud storage for better flexibility and performance.

The next step is to choose a data integration tool that automates data extraction and loading. Fivetran and similar tools set up connectors automatically and manage scheduling in the background. Look for integration features such as:

• Automated schema updates and error handling.
• Broad support for data sources and destinations.
• Incremental syncs for faster and efficient updates.

A transformation framework turns raw data into structured models for analysis within the warehouse. Key features to consider include:

• Modular SQL-based development. Build reusable and maintainable data models using SQL.
• Automated testing. Validate data models to ensure their accuracy and reliability.
• Version control integration. Facilitate collaboration and enable tracking of changes in data models.
• Built-in documentation. Provide clear explanations of data models to enhance understanding and usability.
• Data lineage visualization. Show how data moves and transforms across models, allowing data consumers to show where data came from so they can validate its authenticity.

Modern Business Intelligence (BI) tools help teams share real-time insights on business performance. Stakeholders use shared dashboards to make decisions using the updated data.

Common use cases of ELT pipelines

ELT pipelines enable teams to process and transform massive data streams directly within cloud data warehouses. They support transformations on demand, real-time analytics, and machine learning (ML) on continuously updated datasets.

Real-time analytics and reporting

Companies stream raw data from apps and IoT devices into data warehouses like Snowflake. ELT transformations then clean and model this data to power live dashboards and operational metrics.

Retail and e-commerce optimization

E-commerce platforms use ELT pipelines to combine clickstream, sales, and inventory data from multiple systems. The pipeline then applies transformations to help identify buying patterns and enhance personalized recommendations.

Sentiment analysis and marketing intelligence

Marketing teams pull campaign, CRM, and social data into a data warehouse using ELT automation. They transform it to analyze sentiment trends and measure cross-channel campaign effectiveness in real time.

Healthcare and patient outcomes

Hospitals stream patient vitals and historical records into a data warehouse through ELT pipelines. Data is then standardized to enable predictive models that improve diagnostic speed and patient care outcomes.

Key challenges in implementing ELT pipelines

Scalable ELT pipelines demand tight control over security, compliance, and cost to ensure reliability and efficiency. Here are some of the challenges in implementing ELT pipelines:

Data quality and consistency. Loading raw data without validation can introduce duplicates, missing values, or inconsistent formats. Teams need to implement validation and anomaly detection during the loading and transformation phases. Automated testing and data lineage tracking help identify errors early and maintain consistent data quality.

Security and access control. Transferring petabytes of data between applications and warehouses can expose pipelines to corruption or unauthorized access. Implementing encryption and role-based access control (RBAC) limit who can view sensitive datasets. Integrating security measures at each pipeline stage ensures data remains protected across the warehouse.

Regulatory compliance. Regulations like HIPAA and GDPR require ongoing audits and privacy by design transformations. Compliance should be integrated into ELT design with logging and documentation tools. Regular monitoring of data processes helps teams remain audit-ready and ensures clear governance.

Resource bloating. Cloud data warehouses expand endlessly without retention strategies, which drives up compute and storage costs. Teams should implement data lifecycle policies to regularly archive or delete unused data. Automatic partitioning and tiered storage can help control costs and improve query performance.

Integration complexity. Diverse data formats and systems require adaptable connectors and consistent schema management. Tools with broad connector support, such as Fivetran or Rivery, simplify multi-source integration. Modular transformations make pipelines easier to manage and scale over time.

How dbt powers ELT pipelines

dbt functions as the transformation layer in an ELT pipeline, orchestrating transformations within the data warehouse. dbt serves as a team’s data control plane, using modular SQL models with testing, documentation, and version control to manage data in the organization in a flexible, cross-platform manner.

dbt handles the following ELT tasks:

Transforms data directly within your data warehouse. dbt runs transformations within cloud warehouses, keeping data in place within the ELT pipeline. dbt compiles SQL models into warehouse-native queries rather than exporting data for transformation. This approach enhances both performance and scalability.

Brings software engineering discipline to analytics. Transformations are maintained in version-controlled repositories. Teams branch, review, and merge code just as they would with an application. This approach ensures reproducibility because everyone knows precisely which logic created a table and why.

Automates testing and quality checks. dbt supports creating data tests, with built-in support for common data quality checks such as null values, uniqueness, and relationships. Teams can define custom tests using YAML, and dbt runs them automatically after every model build. This tightens the feedback loop and adds trust in the downstream data pipeline by flagging errors immediately.

Builds documentation and lineage as you go. Every dbt model automatically generates documentation and dependency graphs. You don’t need separate data catalog tools to trace the origin of your data. Data lineage becomes an integral part of your transformation process.

Schedules and orchestrates transformations reliably. dbt Cloud or dbt Core integrations with tools like Airflow and Fivetran handle workflow orchestration. Data pipelines can refresh incrementally or run on a set schedule. This ensures transformed models remain synchronized across dashboards and ML workflows within the ELT pipeline.

Conclusion

ELT pipelines have moved beyond mere technical workflows. They function as data systems that accelerate business growth. This means that business leaders, IT professionals, and sales teams are influenced by ELT pipelines and other types of data pipelines. These pipelines influence decision-making across the entire organization, for better or worse.

In big data environments, the key advantage lies in how efficiently organizations model and transform data. dbt provides a framework for building reliable, scalable data models. Designing your ELT pipeline with the right tools ensures that your organization can make informed decisions.

Start managing your data transformations effectively and take control of your ELT pipeline with dbt.