Zero-ETL: Hype or Future of Data Integration?

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Nowadays every new technology is announced like a Game Changer, X killer, etc. I am always sceptical about new technologies and marketing hype around them. I try to collect every single bit of information before I make my move. It is not different for for Zero-ETL.

It was around 2 years ago I first heard about Zero-ETL. It was at a Customer Data Platform (CDP) vendor meeting. The sales engineer guy was talking about Zero-ETL, how it is going to end the ETL and ELT operations in the next 6–12 months, we don’t need to worry about data movement and transformation operations, they were going to build the infrastructure in soon…

During the last 2 years Generative AI, Chat GPT, and LLMs dominated the data domain. ETL, ELT and data transformation tools and companies continued to grow and extend their user base. So what happened to Zero-ETL?

Let’s take a look into it! Before diving into Zero-ETL, let’s deep dive a little bit into ETL and its challenges.

ETL

In traditional Extract, Transform, and Load (ETL), data is first extracted from various sources, then transformed to fit the destination schema or requirements, and finally loaded into the target database or data warehouse.

Challenges of Traditional ETL

The traditional ETL process has been a reliable method over the last decades when we need to move data between different sources. Even though it solved many problems it also created different issues for us.

Speed

ETL is a slow task. No matter how we scale up our services, instances, or introduce new technologies, moving data from A to B and applying different types of transformations take time. Especially in the need of heavy data transformations and high volume of data movement, we usually prefer daily batch operations where the speed is the least important factor to consider.

Data Integrity and Inconsistency

The purpose of the ETL process is to move data between different locations and applying various types of transformations. During this process we can encounter numerous types of problems; e.g. column format change, column deletions, row filtering, column name changes, etc. These problems make the data inconsistent and break data integrity.

Tech Debt

Tech debt is the curse of engineering. The more we develop the more tech debt we generate. Each ETL task is a natural tech debt for the engineering teams. We need to maintain the code, monitor it, improve its performance, scale it up to new requirements and then maybe split it into smaller ETL jobs to manage it and introduce new tech debt.

From ETL to ELT

What is Zero-ETL?

Zero-ETL is a data management approach where data is analysed and processed in real-time or near-real-time without the need for traditional ETL processes.

Zero-ETL aims to eliminate or minimize the need for the batch-oriented ETL processes by processing data as it arrives, often using techniques like event streaming, change data capture, and real-time data integration. This approach enables organizations to query and analyze data directly from its source, and act on data more quickly, as there is less latency between data generation and its availability for analysis.

Zero-ETL is relevant for organizations which seek real-time or near real-time data to support instant decision-making, analytics and machine learning processes at domains such as finance, healthcare, cybersecurity, etc.

What are the Zero-ETL Use Cases?

According to AWS there are three main use cases for zero-ETL;

Federated querying
Federated querying technologies provide the ability to query a variety of data sources without having to worry about data movement. You can use familiar SQL commands to run queries and join data across several sources like operational databases, data warehouses, and data lakes.

Streaming ingestion
Data streaming and message queuing platforms stream real-time data from several sources. A zero-ETL integration with a data warehouse lets you ingest data from multiple such streams and present it for analytics almost instantly.

Instant replication
Zero-ETL can act as a data replication tool, instantly duplicating data from the transactional database to the data warehouse. The duplication mechanism uses change data capture (CDC) techniques and may be built into the data warehouse.

Advantages and Disadvantages of Zero-ETL

Zero ETL offers several advantages, but it also comes with its own set of challenges. Let’s explore both:

Advantages:

1. Real-time Insights: By processing data as it arrives, Zero ETL enables organizations to gain real-time insights into their data. This allows for quicker decision-making and the ability to respond rapidly to changing conditions or events.
2. Reduced Latency: Zero ETL eliminates the need for batch processing, which reduces the latency between data generation and analysis. This can be particularly beneficial in use cases where timely insights are critical, such as fraud detection, real-time monitoring, and personalized recommendations.
3. Simplified Architecture: Zero ETL simplifies data architectures by eliminating the need for complex ETL processes. This can reduce the overall complexity of data pipelines and make them easier to manage and maintain.

Disadvantages:

1. Complexity of Real-time Processing: Implementing real-time data processing can be more complex compared to batch processing. Developers need to consider factors such as event ordering, event time processing, exactly-once semantics, and handling late or out-of-order events.
2. Data Consistency and Integrity: Ensuring data consistency and integrity in real-time processing can be challenging, especially in distributed systems. Organizations need to implement mechanisms for handling duplicates, ensuring data completeness, and maintaining data quality throughout the pipeline.
3. Operational Overhead: Managing and monitoring real-time data pipelines can require additional operational overhead compared to batch-oriented ETL processes. Organizations need to invest in tools for monitoring data pipelines, troubleshooting issues, and ensuring high availability and reliability.
4. Performance Considerations: Real-time data processing can introduce performance considerations, particularly when dealing with large volumes of data or complex processing logic. Organizations need to carefully optimize their data pipelines to ensure efficient resource utilization and minimize processing latency.
5. Cost: Implementing and maintaining real-time data processing infrastructure can be costly, especially when using managed services or cloud-based platforms. Organizations need to carefully consider the cost implications and ensure that the benefits of real-time processing justify the investment.

Conclusion

For me Zero-ETL is yet another Buzz-Word for data industry. If we pull ourselves from the Buzz-Word hype and focus on the real use case, there might be some opportunities for the future. For me the real use case and the problem needs a solution is to store and analyse transactions in the same instance. If we consider this requirement as a form of “Zero ETL” such as Snowflake Unistore, AWS Aurora and Redshift integration, AWS Big Table and Big Query integration.

Only thing we need to pay attention is the use case. Unless we don’t have a near realtime analytics or machine learning use case, Zero-ETL is going to be a luxury for the organizations due to disadvantages I highlighed before.

Thanks a lot for reading 🙏

If you are interested in Data Engineering, don’t forget to check out my new article series.

From ETL to ELT

Reverse ETL