Which data warehouse cloud platform is faster: Oracle Autonomous Data Warehouse or Snowflake Cloud Data Warehouse? The short answer: it depends. For a more detailed answer, read this blog post.
One of the extensions in Oracle 20c is the possiblity to use the In-Memory Database option for Partitioned External Tables and Hybrid Partitioned Tables. In my opinion, this opens up many possibilities to perform efficient ad-hoc queries on Data Lakes. That’s why I prepared a demo script for my DOAG presentation about SQL features in Oracle 20c. Unfortunately, it turned out differently than planned. A drama in four acts.
Oracle Autonomous Data Warehouse Cloud does not allow to create indexes. Is this a problem for star schemas because no Star Transformation can be used? Or are the required bitmap indexes automatically created? A look under the hood of ADWC.
The Autonomous Data Warehouse Cloud (ADWC) is a self-configuring, fast, secure and scalable platform for data warehouses. Does this mean we don’t have to take care anymore about performance of our ETL processes? Which performance tips are still important for us, and where can we hand over the responsibility to ADWC? A revised version of an old blog post, with regard to Oracle’s Data Warehouse Cloud solution.
Optimizer statistics are essential for good execution plans and fast performance of the SQL queries. Of course, this is also the case in the Autonomous Data Warehouse Cloud. But the handling of gathering statistics is slightly different from what we know from other Oracle databases.
In Oracle Autonomous Data Warehouse Cloud, External Tables can be used to read files from the cloud-based Object Storage. But take care to do it the official way, otherwise you will see a surprise, but no data.