Today, data is at the heart of every application, system and decision made by organizations, and its volume and applications are becoming more and more extensive. From a data engineering point of view, this means more work, due to more time spent developing new ETL pipelines, more diversification of sources from which to extract data, and more time spent developing new ETL pipelines.

Understanding ETL

Extract, Transform, Load (ETL) is a vital process responsible for gathering, moving, combining, cleaning, and normalizing data from various sources, preparing it for analytical workloads. This process plays a pivotal role in business intelligence, addressing specific needs like predicting outcomes and generating reports.

Traditional ETL processes, though effective, bring forth challenges such as complex configurations, additional costs, and delayed time to analytics. Handling inconsistencies and ensuring data security add further complexity. Moreover, as data volumes grow, the costs associated with ETL pipelines can escalate, necessitating costly infrastructure upgrades and maintenance efforts.

What is Zero ETL integration

Zero-ETL integration is a type of data integration that doesn’t involve the use of the conventional extract, transform and load (ETL) processes. In a zero-ETL setup, data is transferred directly from one system to another without the need for any intermediary steps to transform or clean the data. This approach can be useful in situations where data needs to be transferred quickly and efficiently between systems, without the need for complex data transformation or manipulation.

A data replication tool can also be called a zero-ETL tool. A replication tool will transfer data in near-real-time without requiring any intermediate processing or manipulation.

The Good and Bad of Zero-ETL

Now that we’ve established that zero-ETL is having a moment among industry leaders, let’s discuss its benefits and drawbacks.
The Good:
  • Speed: Since zero-ETL integration does not involve any data transformation or manipulation, it can be faster than traditional ETL processes. This can be especially useful in situations where real-time data transfer is important.
  • Simplicity: Zero-ETL integration is often simpler to implement and maintain than traditional ETL processes. This is because it does not require any complex data transformation or manipulation and can be set up quickly and easily.
  • Cost savings: Because zero-ETL integration is typically faster and simpler to implement than traditional ETL processes, it can help to reduce the overall cost of data integration. This can be especially important for organizations with limited budgets or resources.
  • Improved data quality: By eliminating the need for data transformation and manipulation, zero-ETL integration can help to improve the overall quality of the transferred data. This can be a big plus in situations where data accuracy is critical.
The Bad:
  • Limited data transformation capabilities: Because zero-ETL integration involves transferring data directly from one system to another without any intermediate steps, it can be difficult to perform complex data transformations. This can be a problem in situations where the data needs to be cleaned, standardized or otherwise manipulated before it gets transferred.
  • Lack of data governance: Traditional ETL processes often include built-in controls and safeguards to ensure the quality and integrity of the data being transferred. Zero-ETL integration, on the other hand, relies on the systems involved in the transfer to handle these tasks. This can make it more difficult to ensure that the transferred data is accurate and reliable.
  • Inability to integrate with other systems: Because zero-ETL integration relies on direct data transfer, it’s not possible to use it to integrate with other systems outside the ecosystem. This limits the flexibility and versatility of the integration process.
Increased agility
Zero-ETL simplifies data architecture and reduces data engineering efforts. It enables the inclusion of new data sources without the need to reprocess large amounts of data. This flexibility improves agility, supports data-driven decision making and rapid innovation.
Zero-ETL uses data integration technologies that are scalable and cloud-native, enabling enterprises to optimize costs based on actual usage and data processing needs. Organizations reduce infrastructure costs, development efforts and maintenance overhead.
Real-time information
Traditional ETL processes typically involve periodic batch updates, resulting in a delay in data availability. Zero-ETL, on the other hand, provides real-time or near real-time access to data, ensuring more up-to-date data for analytics, artificial intelligence and machine learning and reporting. You get more accurate and timely information for use cases such as real-time dashboards, optimized gaming experience, data quality monitoring and customer behavior analysis. Organizations make data-driven predictions with more confidence, improve customer experience and promote data-driven insights across the enterprise.
  • The source DB cluster must be in the same Region as the target Amazon Redshift data warehouse.
  • You can’t modify an integration after you create it. If you need to change certain settings, you must delete and recreate the integration.
  • You can’t rename a DB cluster or any of its instances if it has existing integrations.
  • You can’t delete a DB cluster that has existing integrations. You must delete all associated integrations first.
  • If you stop the source DB cluster, the last few transactions might not be replicated to the target data warehouse until you resume the cluster.
  • Zero-ETL integrations don’t currently support data filtering.
  • If your cluster is the source of a blue/green deployment, the blue and green environments can’t have existing zero-ETL integrations during switchover. You must delete the integration first and switch over, then recreate it.
  • If your source cluster is the primary DB cluster in an Aurora global database and it fails over to one of its secondary clusters, the integration becomes inactive. You must delete and recreate the integration.
  • When you initially create an integration, or when a table is being resynchronized, data seeding from the source to the target can take 20-25 minutes or more depending on the size of the source database. This delay can lead to increased replica lag.
  • Some data types aren’t supported. For a list of supported data types.Foreign key references with predefined table updates aren’t supported. Specifically, ON DELETE and ON UPDATE rules aren’t supported with CASCADE, SET NULL, and SET DEFAULT actions. Attempting to create or update a table with such references to another table will put the table into a failed state.
  • XA transaction aren’t supported.
  • Object identifiers (including database name, table name, column names, and others) can contain only alphanumeric characters, numbers, $, and _ (underscore).


Zero-ETL Integration Test Case

To demonstrate the effectiveness of Zero-ETL integration, a test case was created using Aurora MySQL database as the source and Redshift as the target for the data pipeline.

The source database, Aurora MySQL version 8.0.mysql_aurora.3.05.1, utilized a dedicated parameter group, as Zero-ETL automatically applies changes to certain database parameters. The target warehouse, Redshift a one node type of ra3.xlplus, encrypted with a dedicated parameter group.

A schema named ‘classicmodels’ with data in the Aurora MySQL database was configured for Zero-ETL integration to initiate online replication of the schema. Below are some screenshots showcasing the key aspects of this test case, illustrating the simplicity and efficiency of Zero-ETL integration

1. RDS console -> go to Zero-ETL Integration tab
2. For Source database we choose the Aurora Mysql database that we created for the test case
3. If the source database parameter doesn’t have the needed values for some parameters then you can check the option “ fix it for me (require reboot) “. Then new parameter group will be created with required value and will be attached to the Aurora and reboot will be performed to reflect the changes to the DB
4. Choose the target as redshift and also you can check the fix for me option
5. Click next and create
After sometime the Integration will be created.
6. Next you need to create database in the Redshift so the Zero-ETL integration can replicate the Data to it as it will not auto create new database for that
7. Once the database is created the replication will start and you can start querying and transforming the data in your warehouse to suit your business need


In conclusion, the emergence of Amazon Web Services (AWS) Zero-ETL integration represents a transformative shift in the domain of data integration. This innovative approach simplifies the Extract, Transform, Load (ETL) process, minimizing complexities which will make AWS Zero-ETL stands as a powerful enabler, unlocking the full potential of data for strategic decision-making and driving innovation in the digital era. By eliminating the barriers associated with traditional ETL, AWS continues to lead the charge towards a more agile, cost-efficient, and data-driven future for organizations worldwide.