According to Forrester research, compared with companies with immature data applications, those companies that effectively obtain business insights are as high as 8.5 times more likely to achieve at least 20% revenue growth. Achieving this growth, however, requires streamlining one process—managing and preparing data before it can be analyzed. That's why Amazon Cloud Technologies is building "the future of Zero ETL," so customers can focus more on creating value from data and less on preparing it.
ETL challenges
What is ETL? ETL is the process of extracting, transforming, and loading. It is also the process used by data engineers to integrate data from different sources. The ETL process can come with challenging, time-consuming, and costly issues. First, it requires data engineers to manually write custom code; next, DevOps engineers must deploy and manage the infrastructure to ensure data pipelines can scale with workloads. If the data source changes, the data engineer must manually change the code and deploy it again. This process may take several days, but at the same time, data analysts cannot conduct interactive analysis or build visual interface Kanban, data scientists cannot build machine learning (ML) models or make predictions, resulting in end users unable to make decisions based on Data decisions.
Additionally, the time required to build or change data pipelines can render the data unsuitable for near real-time scenarios, such as detecting fraudulent transactions, publishing online advertisements, and tracking passenger train schedules. In these cases, opportunities to improve customer experience, capture new business opportunities, or reduce business risk may simply be missed.
Conversely, when businesses can quickly and seamlessly integrate data from disparate sources, they gain a better understanding of their customers and their business, so businesses can more confidently make data-driven predictions that improve customer experience, And roll out data-driven insights across the business.
Amazon cloud technology is
The Vision of "Zero ETL" Becomes Reality
Amazon cloud technology has been making steady progress towards the goal of "Zero ETL". They heard feedback from their customers that they wanted to be able to ingest streaming data directly into their data store for analysis without having to delve into complex ETL processes.
Through the Amazon Redshift streaming data ingestion function, enterprises can configure Amazon Redshift to directly access high-throughput streaming data from the streaming service Amazon MSK (Managed Streaming for Apache Kafka) or Amazon Kinesis, and make them available within seconds. Perform near real-time analysis. Can connect to multiple data streams and inject data directly into Amazon Redshift without staging in Amazon Simple Storage Service (Amazon S3). After running analytics, the entire enterprise can benefit from business insights through Amazon QuickSight, a cloud-native, serverless business intelligence (BI) service. With Amazon QuickSight Q, users can easily and intuitively gain business insights. This feature enables users to ask business questions about their data using natural language and quickly obtain results through data visualization.
In the process of implementing Zero ETL, Amazon cloud technology also provides an important function, which is the ability to query various data sources without moving data. By using federated query in Amazon Redshift and Amazon Athena, enterprises can query data stored in their transactional databases, data warehouses and data lakes to gain insights from multiple data sources without moving data. Data analysts and data engineers can use their familiar SQL commands to connect to multiple data sources for quick analysis and store the results in Amazon S3 for later use. This flexible approach simplifies the data ingestion process and avoids complex ETL processes.
At the 2022 Amazon Cloud Technology re:Invent conference, Amazon Cloud Technology launched the Zero ETL integration of Amazon Aurora and Amazon Redshift.
Amazon Web Services has heard from customers that they spend a lot of time and resources building and managing ETL pipelines between transactional databases and data warehouses. As an example, imagine a global manufacturing company with factories in a dozen countries and using a series of Aurora database clusters to manage each country's order and inventory data. When company executives wanted to see all orders and inventory, data engineers had to build separate data pipelines for each Aurora cluster, consolidating the data into a central data warehouse so that data analysts could query the consolidated dataset. To achieve this, the data integration team had to write code to connect to 12 different clusters and manage and test 12 production pipelines separately. After the team deployed the code, the pipeline needed to be constantly monitored and tuned to optimize performance, and any changes needed to be updated in 12 different places. This is a very repetitive and tedious job.
Amazon Aurora and Amazon Redshift
Custom ETL pipelines are no longer required between
Aurora's Zero ETL integration with Amazon Redshift brings together Aurora's transactional data with Amazon Redshift's analytical capabilities. This reduces the effort to build and manage custom ETL pipelines between Aurora and Amazon Redshift.
Unlike "data islands" in traditional systems, users must make a trade-off between unified analysis and performance; now data engineers can copy data from multiple Aurora database clusters to the same or new Amazon Redshift instance, Get comprehensive insights across multiple applications or partitions. Updates in Aurora are automatically and continuously replicated to Amazon Redshift, so data engineers have access to the latest information in near real time. The entire system is serverless and can be dynamically expanded up and down according to the size of the data volume, so enterprises do not need to manage infrastructure. Enterprises can now truly achieve fast, scalable transactional analytics in Aurora and scalable analytics in Amazon Redshift, all in one seamless system. With near real-time access to transactional data, organizations can leverage Amazon Redshift's analytical capabilities such as machine learning, materialized views, data sharing, and federated access to multiple data stores and data lakes to gain insights from transactional and other data.
Continuously improving the performance of Zero ETL is an ongoing goal of Amazon Cloud Technology. For example, customers of Amazon Cloud Technology who used the preview version of Zero ETL in the early stage observed that their Amazon Aurora MySQL database generated hundreds of thousands of transactions per minute. These transactions Appears in their Amazon Redshift data warehouse in less than 10 seconds. Before this, their process of moving data from the ETL pipeline to Amazon Redshift required more than 2 hours of latency. With the Zero ETL integration between Aurora and Redshift, they can now achieve near real-time analytics.
Zero ETL enables data engineers to directly integrate services and query various data stores directly during use, allowing them to focus on creating value from data rather than spending time and resources building data pipelines. Amazon cloud technology will continue to be committed to building the future of Zero ETL, helping enterprises to move towards data-driven business growth.