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ETL vs ELT Cage Fight: Combining Rudderstack & ClickHouse to Build Real-Time Data Pipelines

In the realm of data engineering, the choice between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) has been a topic of heated debate for years. In this article, we'll delve into the nuances of these approaches and explore how the combination of Rudderstack and ClickHouse can empower developers and data engineers to build robust real-time data pipelines.

Understanding the ETL and ELT Paradigms

ETL: Extract, Transform, Load

The ETL approach has been a staple in data pipeline architecture for decades. It involves extracting data from source systems, applying transformations to cleanse and structure it, and then loading the processed data into a data warehouse for further analysis. This method is particularly suitable for structured data that requires extensive transformations before reaching the data warehouse.

ELT: Extract, Load, Transform

In contrast, the ELT approach flips the order of operations (well, sort of, except the extract phase is still the same). Data is first extracted from source systems and loaded directly into the data warehouse. Transformations are then performed within the data warehouse, taking advantage of its powerful processing capabilities. ELT is advantageous for scenarios involving semi-structured or unstructured data and situations where maintaining an original copy of the data is important.

Introducing Rudderstack and ClickHouse

Rudderstack: A Customer Data Platform

Rudderstack is a powerful customer data platform designed to streamline data collection and integration processes. It provides standardized event schemas and offers software development kits (SDKs) for web, mobile, and server-side applications. With over 200 out-of-the-box integrations, Rudderstack enables seamless data delivery to various downstream tools, including marketing automation and analytics platforms.

ClickHouse: A Versatile Data Warehouse

ClickHouse is an open-source enterprise-grade data store designed for analytics and data warehousing. It offers unique deployment flexibility, allowing you to run ClickHouse clusters on-premises, in the cloud such as the Altinity.Cloud platform, or even on Kubernetes. ClickHouse excels at efficient data storage and enables rapid query performance, making it ideal for data analytics. Altinity is the leading service and software provider for ClickHouse for managing and analyzing extremely large volumes of data. Altinity helps companies build and maintain systems extracting insight from data at an unmatched performance.

Combining Forces: ETL and ELT for Real-Time Data Pipelines

Now, back to the cagefight. ETL vs ETL, which one should you be using? We say both. Although each has it’s own use cases, we like to think ETL and ELT are not competing paradigms but complementary strategies that can work together harmoniously to optimize data pipelines. Here's how this combination can empower developers and data engineers:

1. Flexible Transformations

Rudderstack provides real-time event streaming, ETL pipelines, and reverse ETL pipelines, catering to a variety of transformation needs.

ClickHouse's powerful querying capabilities within the data warehouse allow developers to perform complex transformations on the data, unlocking new insights.

2. Standardized Event Collection

Rudderstack's standardized event schemas streamline data collection, making it easier to integrate with downstream tools and data warehouses.

3. Efficient Data Storage and Analytics

ClickHouse's cloud-native and on-premises deployment options ensure versatile and efficient data storage, while its performance ensures rapid analytics.

4. Original Data Copy

ELT's emphasis on loading raw data into the data warehouse preserves the original data, enabling developers to experiment with various transformations without losing valuable information.

5. Scenario-Based Optimization

ETL is well-suited for structured data that requires substantial transformation before loading.
ELT shines when dealing with semi-structured or unstructured data and situations where data preservation is crucial.

Real-World Example: Funnel Analysis

To put these concepts into context, let's consider a real-world scenario: funnel analysis for an e-commerce website. Using a combination of ETL and ELT, developers can collect user behavior data using Rudderstack's SDKs, standardize the event schema, and load it into ClickHouse. From there, ClickHouse's powerful capabilities allow developers to perform transformations that enable insightful funnel analysis, tracking users' journeys from website visits to conversions.

If you're interested in learning how to efficiently combine RudderStack and ClickHouse to build effective data pipelines, we have an excellent hands-on demo for you straight from the experts. A webinar, previously hosted by Altinity and Rudderstack, delved into defining standard transformations, and demonstrated how to implement them in both RudderStack and ClickHouse. The recording of the webinar is available on-demand and you can access it here: Altinity and Rudderstack Joint Webinar Recording or watch more webinars like that on our YouTube channel!


The ETL vs ELT debate need not be a divisive one; rather, it's an opportunity to harness the strengths of both approaches. The combination of Rudderstack's streamlined data collection and integration with ClickHouse's versatile data warehousing capabilities empowers developers and data engineers to create efficient, real-time data pipelines that drive actionable insights. Whether you're dealing with structured, semi-structured, or unstructured data, the harmony of ETL and ELT can pave the way for powerful data analytics and informed decision-making.

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