Zero-ETL is a set of integrations that eliminates or minimizes the need to build ETL data pipelines. Extract, transform, and load (ETL) is the process of combining, cleaning, and normalizing data from different sources to get it ready for analytics, artificial intelligence (AI) and machine learning (ML) workloads. Traditional ETL processes are time-consuming and complex to develop, maintain, and scale. Instead, zero-ETL integrations facilitate point-to-point data movement without the need to create ETL data pipelines. Zero-ETL can also enable querying across data silos without the need for data movement.
What ETL challenges does zero-ETL integration solve?
The zero-ETL integrations solve many of the existing data movement challenges in traditional ETL processes.
Increased system complexity
ETL data pipelines add an additional layer of complexity to your data integration efforts. Mapping data to match the desired target schema involves intricate data mapping rules, and requires the handling of data inconsistencies and conflicts. You have to implement effective error handling, logging, and notification mechanisms to diagnose issues. Data security requirements further increase constraints on the system.
Additional costs
ETL pipelines are expensive to begin with, but costs can spiral as data volume grows. Duplicate data storage between systems may not be affordable for large volumes of data. Additionally, scaling ETL processes often requires costly infrastructure upgrades, query performance optimization, and parallel processing techniques. If requirements change, data engineering has to constantly monitor and test the pipeline during the update process, adding to maintenance costs.
Delayed time to analytics, AI and ML
ETL typically requires data engineers to create custom code, as well as DevOps engineers to deploy and manage the infrastructure required to scale the workload. In case of changes to the data sources, data engineers have to manually modify their code and deploy it again. The process can take weeks—causing delays in running analytics, artificial intelligence, and machine learning workloads. Furthermore, the time needed to build and deploy ETL data pipelines makes the data unfit for near-real-time use cases such as placing online ads, detecting fraudulent transactions, or real-time supply chain analysis. In these scenarios, the opportunity to improve customer experiences, address new business opportunities, or lower business risks is lost.
What are the benefits of zero-ETL?
Zero-ETL offers several benefits to an organization’s data strategy.
Increased agility
Zero-ETL simplifies data architecture and reduces data engineering efforts. It allows for the inclusion of new data sources without the need to reprocess large amounts of data. This flexibility enhances agility, supporting data-driven decision making and rapid innovation.
Cost efficiency
Zero-ETL utilizes data integration technologies that are cloud-native and scalable, allowing businesses to optimize costs based on actual usage and data processing needs. Organizations reduce infrastructure costs, development efforts, and maintenance overheads.
Real-time insights
Traditional ETL processes often involve periodic batch updates, resulting in delayed data availability. Zero-ETL, on the other hand, provides real-time or near-real-time data access, ensuring fresher data for analytics, AI/ML, and reporting. You get more accurate and timely insights for use cases like real-time dashboards, optimized gaming experience, data quality monitoring, and customer behavior analysis. Organizations make data-driven predictions with more confidence, improve customer experiences, and promote data-driven insights across the business.
What are the different use cases for zero-ETL?
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. In-Memory Data Grids (IMDG) store data in memory to be cached and processed, so you can reap the benefits of immediate analysis and query response times. You can then store the join results in a data store for further analysis and subsequent use.
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. There is no requirement to stage the streaming data for transformation on any other storage service.
Instant replication
Traditionally, moving data from a transactional database into a central data warehouse always required a complex ETL solution. These days, 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. The duplication is invisible to users—applications store data in the transactional database and analysts query the data from the warehouse seamlessly.
