![]() 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.Fourth, Autonomous Data Warehouse reduces risk with production-hardened data security and privacy.Third, Autonomous Data Warehouse delivers consistent performance as workloads change by automatically scaling resources and tuning the database. ![]()
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