What Is an ETL Pipeline?

What Is an ETL Pipeline?

Today's businesses are often rich in data but lack the ability to share trusted data among business teams across their organizations. Valuable information sits siloed in various tools, which means many teams are working with an incomplete or inaccurate picture of their customers. This situation leaves them without access to the actionable insights necessary to make well-informed decisions and gain a competitive advantage. 

The paradox stems from both the sheer volume of available data and the complexity of putting it to use. ETL pipelines offer a potential solution to bridge this gap, but can they deliver the results you need? So, what is an ETL pipeline? ETL stands for Extract, Transform, and Load and the pipeline is a process that gets your valuable data back out of all those silos and moves it into the systems that your employees use every day to drive your business forward. Read on to find out more about ETL pipelines, discover how they differ from reverse ETL, and explore how you can activate your data for maximum impact.

What Is an ETL Pipeline?

An ETL pipeline is the name for a series of processes that can efficiently move your data from point A to point B. ETL prepares raw, unstructured data and makes it available for comprehensive analysis and reporting. This refinement enables your business teams to make data-driven decisions based on the most up-to-date and accurate information. ETL pipelines are highly versatile and can extract data from a wide array of sources, including CRMs, transaction databases, and APIs. The extracted data is transformed into a standardized format and then loaded into a target system designed for storing vast amounts of information, such as a data warehouse. 

How Does an ETL Pipeline Work?

ETL Pipelines seamlessly extract data, transform it to align with business requirements, and load it for analysis and decision-making. 

ETL pipelines consist of three main processes:

Extract: 

Raw data is extracted from the source systems, including databases, applications, files, APIs, websites, social media platforms, data lakes, and more. 

Transform: 

After extraction, the raw data undergoes transformation. This can include cleaning, filtering, correcting errors, removing duplicates, and converting data into a consistent format suitable for downstream applications. This ensures that it’s consistent, accurate, and compatible with the target system's requirements. 

Load: 

During the loading phase, the transformed data is delivered to the target systems using various methods. Bulk loading is used for large volumes of data, while incremental loading processes only new or updated data. Real-time data streaming can continuously push data to the target system, and API-based loading uses application programming interfaces to send data. Batch processing is employed to load data at scheduled intervals.

Target systems can include:

• Data Warehouses
• Data Lakes
• Databases
• Cloud Storage
• Analytics Platforms
• Machine Learning Platforms
• Marketing Automation Systems
• Customer Relationship Management (CRM) Systems
• Enterprise Resource Planning (ERP) Systems
• Flat Files

The data is then indexed, optimized for queries, and made accessible for analysis and decision-making.

What Are the Benefits of ETL Pipelines?

Many critical business systems are designed for efficient and accurate data entry, but not for analysis. So, if you require deeper insights or effective data integration, it’s essential to be able to move this data into other systems. This is where solutions like ETL pipelines can offer numerous benefits to your organization. 

Data integration: 

By combining diverse sources, ETL pipelines allow your data warehouse to become a single source of truth. This centralization and integration means no more valuable data locked up in silos. You can be sure that accurate, unified data is at the fingertips of every team and available when and where they need it to get the holistic view that enables better decision-making.

Improved data quality: 

Transforming data through cleaning, deduplicating, validating, and standardizing before loading ensures that only accurate and high-quality data arrives in the target systems. By eliminating inconsistencies and errors, ETL pipelines ensure you can trust your data and give you the confidence to make more informed decisions. What's more, data from one system can be used to enrich data from another source, enhancing the quality and completeness of information.

Higher efficiency: 

ETL pipelines streamline the data processing workflow, making it more efficient and less error-prone. Automated data movement and the ability to schedule data eliminates manual intervention which increases efficiency, reduces the risk of human errors, and ensures consistent and reliable data transfers. By scheduling data movement you gain flexibility to accommodate different data update frequencies and requirements. It also lets you optimize resource usage and maintain the freshness of data. 

Analytics for decision-making: 

By extracting raw data from multiple inputs and transforming it, ETL pipelines provide a firm foundation for reliable business intelligence and reporting. With integrated and quality-assured data, businesses can generate powerful analytics. Instead of manually handling data, your analysts can focus on deriving insights and making data-driven decisions.

Scalability: 

As your business grows and your data volume increases, ETL pipelines can scale to accommodate your expanding needs. By accommodating additional sources without sacrificing performance, they minimize silos and ensure your data warehouse remains always up-to-date with accurate and unified data that is ready for analysis.

How to Build an ETL Pipeline

There are various approaches to building an ETL pipeline and which one you choose depends on your unique requirements. In general, the process involves the following key steps:

Create reference data: 

To ensure the consistency and accuracy of data across the pipeline, start by establishing your reference data. This is a standardized set of values or codes that the ETL system will use to classify or categorize other data and confirm its integrity.

Extract and standardize  data: 

The next step is to identify your data sources, such as a CRM, connect to them using APIs or connectors, and then collect the raw data before standardizing it into a single format.

Validate data: 

In validation, the standardized data will be checked for accuracy and completeness, including problems such as missing values, duplicates, and outliers. This step ensures that the data is reliable and fit for purpose before processing.

Transform data: 

At this point, a series of rules will be applied to the extracted data to clean, aggregate, deduplicate, and restructure it into a format suitable for analysis or business needs. This step enhances data quality and consistency and is crucial for ensuring that it’s in a standardized and easily understandable format for target systems.

Stage data: 

After transforming the data, it will be temporarily stored in a staging area where an additional quality audit can be carried out to diagnose and repair any potential problems before the data is loaded into the data warehouse.

Load data: 

Following the quality audit, your data will move from the staging area into your final target, often a data warehouse or data lake, and ensure that it is properly indexed and optimized for queries.

Schedule future processing: 

You can automate your ETL pipeline to run at specific intervals and keep your data storage destination constantly up-to-date.

How Are ETL Pipelines Different from Other Solutions?

While ETL pipelines are a popular choice for data integration and processing, there are other solutions available, from ELT to Reverse ETL. Let’s look at how ETL pipelines differ from the alternatives:

ETL pipelines vs data pipelines

Although ETL pipelines and data pipelines are sometimes used interchangeably, there are some key differences between the two:

Data pipelines: 

Data pipelines are a broad concept that encompasses various types of data movement and processing — this could include uploading a spreadsheet into your CRM. ETL is just one particular form of data pipeline. 

ETL pipelines: 

ETL pipelines specifically focus on extracting data, transforming it for analysis, and loading it into a target system. It prioritizes preparing data for data warehousing and business intelligence.

ETL vs ELT

Another related concept is ELT (Extract, Load, Transform), which differs from ETL in the order of the processing steps.

ETL prioritizes data quality by thoroughly cleaning and transforming the data before loading it into a data warehouse. This approach ensures that the data is ready for analysis, but this means it can be slower for large datasets.

On the other hand, ELT loads the raw data into the warehouse first and then transforms it as needed for specific use cases. ELT is often used when dealing with large volumes of data or when flexibility is required in the transformation process. This approach is faster but may require additional processing during analysis.

The choice between ETL and ELT depends on factors such as data volume, data structure, and the specific analytical requirements of your organization.

ETL vs Reverse ETL

While traditional ETL pipelines are essential for getting data into your warehouse, Reverse ETL allows you to move valuable data like customer or business intelligence from your warehouse back into the operational systems your teams use every day, where it can be put to work. From Amazon Ads to Zendesk, Reverse ETL enhances all your other tools by transforming your data warehouse into a single source of truth. It makes accurate and high-quality data available across your systems from CRMs and marketing automation platforms to customer support software. 

Here’s a closer look at some of the benefits offered by Reverse ETL:

Allows personalization at scale: 

Reverse ETL allows you to activate the rich customer data in your warehouse to personalize experiences across multiple touchpoints. For example, you can sync customer segments and attributes to your marketing automation platform to deliver targeted campaigns or to your customer support tool to provide personalized service.

Boosts operational efficiency: 

Reverse ETL eliminates the need for manual data exports and imports by automating the flow of data from your warehouse to operational systems. Manual intervention is prone to human errors such as incorrect file transfers, inconsistent data formatting, or missed updates. With predefined rules and workflows, Reverse ETL ensures that data is consistently and accurately transferred to your target systems, saving time and allowing your teams to focus on high-value activities.

Enables real-time decision-making: 

Reverse ETL enables real-time data synchronization, ensuring that your operational systems always have access to the most up-to-date information. This empowers your business teams to make decisions based on the latest insights, rather than relying on stale data.

Canva uses Census to activate Snowflake and send more data into marketing tools like Braze, cutting costs, slashing months of work to minutes, and enabling its lifecycle marketing team to personalize messages to over 170M users. Get the case study

Census for Your Data Activation Needs

In the drive to unlock the full potential of your data and grow your business, traditional ETL is just the beginning. By empowering your teams with fresh and reliable data, reverse ETL enables personalization at scale, provides a single source of customer truth, improves operational efficiency, and supports real-time decision-making.

If you're looking to activate your valuable customer data and put it to work across your organization, Census can help. 

With Census, you can:

• Use your data warehouse for campaigns, personalizing emails, and real-time customer targeting without coding.
• Sync customer data across 200+ business tools with Reverse ETL connectors.
• Empower your marketing and data teams with a no-code segment builder and dynamic audiences.
• Transform your data into actionable insights and drive revenue growth. 

Learn more about how Reverse ETL can help you activate your data and take your business to the next level.