What is Zero ETL?
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.
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.
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.
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.
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 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.
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.
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.
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 Athena is a serverless, interactive analytics service built on open-source frameworks, supporting open-table and file formats. Athena provides a simplified, flexible way to analyze petabytes of data where it lives. You can analyze data or build applications from an Amazon Simple Storage Service (S3) data lake and 30 data sources, including on-premises data sources or other cloud systems, using SQL or Python. Athena is built on open-source Trino and Presto engines and Apache Spark frameworks, with no provisioning or configuration effort required.
Amazon Redshift Streaming Ingestion ingests hundreds of megabytes of data per second from Amazon Kinesis Data Streams or Amazon MSK. Define a schema or choose to ingest semi-structured data with SUPER data type to query data in real-time.
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 Redshift Auto-copy from S3 simplifies and automates file ingestion into Amazon Redshift. This capability continuously ingests data as soon as new files are created in S3 with no custom coding or manual ingestion activities.
Data Sharing Access Control with AWS Lake Formation centrally manages granular access to data shared across your organization. You can define, modify and audit permissions on tables, columns, and rows within Amazon Redshift.
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