Elasticsearch vs Oracle Data Warehouse

• Super-fast search on millions of documents. We've got over 2 billion documents in our index and the retrieve speeds are still in the < 1-second range.
• Analytics on top of your search. If you organize your data appropriately, Elasticsearch can serve as a distributed OLAP system
• Elasticsearch is great for geographic data as well, including searching and filtering with geojson, and a variety of geospatial algorithms.

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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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• Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
• The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
• This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.

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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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• Faster searches on our application have resulted in better usability and increased application use
• Analytics dashboard has given our managers a better understanding of day-to-day activities
• Being a backup data store, we need not touch SQL database while doing data dumps for local data science projects

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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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