![]() ![]() We learned from customers that they spend significant time and resources building and managing ETL pipelines between transactional databases and data warehouses. More recently, AWS introduced Amazon Aurora zero-ETL integration with Amazon Redshift at AWS re:Invent 2022. This provides a flexible way to ingest data while avoiding complex ETL pipelines. Data analysts and data engineers can use familiar SQL commands to join data across several data sources for quick analysis, and store the results in Amazon S3 for subsequent use. Using federated query in Amazon Redshift and Amazon Athena, organizations can run queries across data stored in their operational databases, data warehouses, and data lakes so that they can create insights from across multiple data sources with no data movement. QuickSight makes it incredibly simple and intuitive to get to answers with Amazon QuickSight Q, which allows users to ask business questions about their data in natural language and receive answers quickly through data visualizations.Īnother example of AWS’s investment in zero-ETL is providing the ability to query a variety of data sources without having to worry about data movement. After running analytics, the insights can be made available broadly across the organization with Amazon QuickSight, a cloud-native, serverless business intelligence service. They can connect to multiple data streams and pull data directly into Amazon Redshift without staging it in Amazon Simple Storage Service (Amazon S3). With Amazon Redshift Streaming Ingestion, organizations can configure Amazon Redshift to directly ingest high-throughput streaming data from Amazon Managed Streaming for Apache Kafka (Amazon MSK) or Amazon Kinesis Data Streams and make it available for near-real-time analytics in just a few seconds. For example, customers told us that they want to ingest streaming data into their data stores for doing analytics-all without delving into the complexities of ETL. ![]() We have been making steady progress towards bringing our zero-ETL vision to life. AWS is bringing its zero-ETL vision to life As a result, they can make data-driven predictions with more confidence, improve customer experiences, and promote data-driven insights across the business. On the flip side, when organizations can quickly and seamlessly integrate data that is stored and analyzed in different tools and systems, they get a better understanding of their customers and business. In these scenarios, the opportunity to improve customer experiences, address new business opportunities, or lower business risks can simply be lost. While all of this is happening-a process that can take days-data analysts can’t run interactive analysis or build dashboards, data scientists can’t build machine learning (ML) models or run predictions, and end-users can’t make data-driven decisions.įurthermore, the time required to build or change pipelines makes the data unfit for near-real-time use cases such as detecting fraudulent transactions, placing online ads, and tracking passenger train schedules. In case the data sources change, data engineers have to manually make changes in their code and deploy it again. Next, DevOps engineers have to deploy and manage the infrastructure to make sure the pipelines scale with the workload. Firstly, it invariably requires data engineers to create custom code. ETL can be challenging, time-consuming, and costly. What is ETL? Extract, Transform, Load is the process data engineers use to combine data from different sources. That’s why AWS is investing in a zero-ETL future so that builders can focus more on creating value from data, instead of preparing data for analysis. However, to realize this growth, managing and preparing the data for analysis has to get easier. According to Forrester, advanced insights-driven businesses are 8.5 times more likely than beginners to report at least 20% revenue growth. ![]() When data is used to improve customer experiences and drive innovation, it can lead to business growth. Data is at the center of every application, process, and business decision. ![]()
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