Moving data out of Redshift and using separate services delays insights while increasing complexity, security risks, and costs.Ĭan the data warehouse scale without downtime?Īutonomous Data Warehouse can scale up to increase query performance and concurrent user throughput or scale down to minimize costs-both without downtime. Customers must use additional Amazon services, such as SageMaker Data Wrangler, SageMaker Studio, SageMaker Pipelines and other services to move and reformat data, develop/test algorithms, and generate predictive insights. Redshift lacks in-database advanced analytics and ML algorithms. Running inferences inside the data warehouse also eliminates the need to extract data, reduces time-to-insight, and data proliferation risks while lowering costs.
Building ML models inside Autonomous Data Warehouse eliminates the time-consuming requirement to extract, reformat, and move data to separate additional cost services, enabling users to create models without having to learn new tools. Data scientists and data science-savvy non-experts use these capabilities to accelerate modeling tasks, automate the discovery of new insights, and quickly generate predictions. These additional services add complexity to customers’ data marts, fragment data, introduce security risks, and require additional management, significantly increasing Redshift’s TCO.ĭoes the data warehouse include built-in machine learning capabilities for the training and deployment of ML models?Īutonomous Data Warehouse includes in-database ML algorithms and automated machine learning (AutoML) capabilities. Customers who want to use graph analytics need a separate specialty database, data scientists who want to use ML must export data to and reimport it from another AWS service, and data ingestion and transformation require separate AWS or third-party services. For instance, organizations must pay for and integrate Lambda, Kinesis, and S3 just to move data from Amazon Aurora to Redshift-or master Amazon Glue, which requires a DBA to define the ETL job whereupon Glue generates PySpark code, which often needs to be customized based on validation and transformation requirements. Customers can easily create self-service data marts using built-in capabilities for data ingest, transformation, ML, business analytics, graph analytics, geospatial analytics, and application development.Īmazon Redshift is missing an extensive set of data and analytical capabilities that are included in Autonomous Data Warehouse. Finally, Oracle makes it easy for customers to comply with data residency requirements.Ĭan customers build a self-service data mart for reporting without having to integrate multiple additional services?Īutonomous Data Warehouse includes comprehensive capabilities to address a broad set of analytical use cases.
It includes tools for self-service data loading, data transformations, business models, automatic insights, and built-in converged database capabilities that enable simpler queries across multiple data types and machine learning analysis. It automates provisioning, configuring, securing, tuning, scaling, patching, backing up, and repairing of the data warehouse. Customers choose Oracle Autonomous Data Warehouse vs Amazon Redshift, a cloud data warehouse service that eliminates all the complexities of operating a data warehouse, securing data, and developing data-driven applications.
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