What is zero-ETL?
Zero-ETL is a set of integrations that 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.
Faster time to 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.
Rapid data ingestion
Enterprises need to quickly ingest and analyze different types of data for real-time decision-making. Zero-ETL provides a flexible approach to rapidly ingest data directly into data warehouses and data lakehouses. This removes the need for traditional ETL pipelines, allowing organizations to adapt to changing business requirements with ease.
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, as these platforms also offer rich transformations and analytics while the data is in motion.
Instant replication
Traditionally, moving data from an operational and transactional database into a central data warehouse and a data lakehouse always required a complex ETL solution. These days, zero-ETL can act as a data replication tool, instantly duplicating data from the operational database, transactional database, and applications to the data warehouse and data lakehouse. The duplication mechanism uses change data capture (CDC) techniques and may be built into the data warehouse and data lakehouse. The duplication is invisible to users—applications store data in the transactional database and analysts query the data from the warehouse seamlessly.
How can AWS support your zero-ETL efforts?
AWS is investing in a zero-ETL future. Here are examples of services that offer built-in support for zero-ETL.
Amazon SageMaker Lakehouse and Amazon Redshift support for zero-ETL integrations from applications, which automates the extracting and loading of data from applications into Amazon SageMaker Lakehouse and Amazon Redshift.
Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakhouse automates the extracting and loading of data from Amazon DynamoDB into Amazon SageMaker Lakehouse, a transactional data lake built on Amazon S3.
Amazon OpenSearch Service zero-ETL integration with Amazon CloudWatch Logs enables direct querying and visualization of log data in near real-time, centralizing log management without complex pipelines or pre-processing.
Amazon OpenSearch Service zero-ETL integration with Amazon Security Lake enables direct searching and analysis of security data, eliminating data integration challenges while reducing complexity, operational overhead, and costs through on-demand data acceleration and rich analytical capabilities.
Amazon Aurora zero-ETL integration with Amazon Redshift enables near-real-time analytics and machine learning (ML). It uses Amazon Redshift for analytics workloads on petabytes of transactional data from Aurora. It's a fully managed solution for making transactional data available in Amazon Redshift after it's written to an Aurora DB cluster.
Amazon RDS for MySQL zero-ETL integration with Amazon Redshift helps derive holistic insights across many applications and break data silos in your organization, making it simpler to analyze data from one or multiple Amazon RDS for MySQL instances in Amazon Redshift.
Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service provides customers advanced search capabilities, such as full-text and vector search, on their Amazon DynamoDB data.
Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service provides customers advanced search capabilities, such as fuzzy search, cross-collection search and multilingual search, on their Amazon DocumentDB documents using the OpenSearch API.
Amazon OpenSearch Service zero-ETL integration with Amazon S3, a new efficient way for customers to query operational logs in Amazon S3 data lakes removing the need to switch between tools to analyze data.
Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift enables near real-time analytics and machine learning (ML) using Amazon Redshift to analyze petabytes of transactional data from Aurora.
Amazon DynamoDB zero-ETL integration with Amazon Redshift enables customers to run high-performance analytics on their DynamoDB data in Amazon Redshift with no impact on production workloads running on DynamoDB.
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