Azure Blob Storage vs Oracle Data Warehouse

• Reduced cost. We were able to reduce our storage infrastructure by several hundred terabytes by consolidating redundant copies and dedupe/compression on files.
• Extremely high level of redundancy. We can replicate data in a variety of ways that we would never have been capable of on our own hardware.

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• Able to handle very large data sizes efficiently from a performance, high availability and manageability perspective. This is accomplished through the Oracle Partitioning functionality. Partitioning allows large segments (tables, IOT index-organized tables, indexes) to be broken into smaller segments at the physical layer but treated as a whole at the logical layer.
• Provides support for dual-format architecture through Oracle In-Memory functionality. Without any change to application code one can obtain in-memory performance. This functionality enables us to have the tables represented in both the row format and the column format using in-memory format. This is a huge boost for BI/analytic queries since the Oracle optimizer is able to intelligently choose the appropriate format.
• Provision to materialize a subset of table data or table joins. This is through materialized views and the optimizer will rewrite the query against the base tables to make use of this materialized view. This provides a huge performance boost and is critical in VLDBs as in a data warehouse. The query rewrite is fully transparent to users.
• Provides multiple compression capabilities. This is very useful not only for deducing the storage foot print but as well as increase performance at different layers of the infrastructure including query performance. The compression functionality can be applied against both structured and unstructured data.
• With the advent of Engineered Systems (Exadata, Database Machine, SuperCluster) there are specific features and functionalities that can further boost the Oracle data warehouse. These are related to consolidation, Smart Scan, Storage Indexes, EHCC (Exadata hybrid columnar compression) and much more.
• RAC - Real Application Clusters (with 2 or more nodes) provides functionality for high availability, performance and scaling as the work load increases. The parallelism is provided both within a node and as well as across nodes. If for any reason a node goes down the data warehouse is still available through other nodes and the running queries are transparently failed over to the surviving nodes.

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• One thing we wish was done better is more guidance for building it out from day one. We did not know what we were doing and until we hired an implementation person that understood what we wanted to do. We spent a lot of time and money doing things over several times.
• Help customers hook up with talented (local) implementation specialists would be great.

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• Customer support isn't the best out there. We usually have to wait about an hour to get some form of assistance.
• Pricing is a bit higher than many of its competitors such as AWS Redshift.
• Tweaking features requires dedicated staff. Software is fairly advanced. Would be difficult to use for newcomers.

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• Azure Blob Storage is just way cheaper than anything we could afford to do on-prem. Forecasting spend is way easier with predictable growth than it is with large capital expenditures every few years, and that ability to grow or shrink dynamically is simplifies things.

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• Very cost effective for large databases.
• Very fast results on simple queries.
• Analytical functionalities are of wide range, which makes it very cost effective.
• For smaller businesses, it might be a great asset.
• Some features are supposed to be bought separately, so one needs to consider this before licensing with Oracle DW.

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