Oracle Autonomous Data Warehouse

I used to use it in my previous role when I was working as a Spatial Data Analyst. I mostly used it to query and model GIS data and to write spatial queries to extract meaningful information. We used a GIS product that was only compatible with oracle and TOAD. I had some fun learning/experiences with it.

It's used by a department, it moves and transforms data from transactional financial database into warehouse database, so that end users can generate the reports out of it.

Oracle Autonomous Data Warehouse is a fully managed database that’s tuned and optimized for data warehouse workloads, so we decided to give it a try with a POC. It combines the market-leading performance of Oracle Database with the ease of Autonomous Database and is self-driving, self-securing, and self-repairing. Our objective was to spend our time focusing on delivering applications to solve our business problems. One of the hurdles was the time spent in maintaining--upgrading, patching, installing new services. Oracle Autonomous Data Warehouse gave us the confidence to delegate the operational well-being of our database instances to the experts who designed it from the ground up.

It is used in multiple departments to build in-house analytics tools where we use Oracle as our main database.
The current purpose of using Oracle in our team is just for storing less data but highly index database for frequent data fetch which enable our operation to resolve customer tickets/complaint within SLA. Our current system is small which may we scale in the future (10-20 million records in main and we also create SCD type 1,2 and 3 ETL flow using Oracle).

I'm using the a Free Tier [Oracle] Autonomous Data Warehouse in a PoC / Test environment to centralize information from a wide range of sources in order to allow quick Reporting and OLAP analysis of data coming from our suite of tolling software. I'm integrating information from more than 30 databases (20 SQL Server, 5 Oracle and other like Postgres). This POC will try to demonstrate the ease of use and maintenance of Autonomous databases so we can recommend them to our customers.

A manufacturing company recently asked my company to do a Business Intelligence project to improve and standardize the analysis of its users. Before the project, users used spreadsheets and local databases based on Access to perform management analysis.
For the project, we proposed them an Oracle cloud full-stack architecture based on:

• Object storage
• Oracle Autonomous Data Warehouse
• Oracle Analytics cloud

After a first as-is analysis, the project steps have been:

• Export of data from the company ERP on flat files
• Creation of a staging area layer
• Creation of PL/SQL procedures to import files on staging structures, performing formal checks, cleaning and standardization processes
• Creation of entity-relation models for some Data Marts
• Creation of ETL flows to load data from the staging area on the Data Marts
• Creation of a series of institutional reports, based on Data Marts
• Profiling of users to access to the reporting layer and for free ad hoc analysis

The project has recently been deployed and the architecture is currently being used by about fifty users.

We are using Oracle Autonomous Data Warehouse for storing our data from various fusion apps, marketing technology stack, and customer transaction data. We are stitching these disparate data sources/systems together so that eventually we can perform historical and OLAP analysis using Tableau.

We use the Oracle Autonomous Data Warehouse as my company central data warehouse where we store the different department's data in corresponding tables

We use ADW in conjunction with the Oracle Blockchain Platform, as it provides an easy way to inspect, analyze, and reuse information stored in a blockchain style. The ADW has basically the same functionality as the Oracle Autonomous Transaction Processing - and they have a massive toolset of useful things!
Let's start with the most engineer-y one: JSON or SQL? It does not matter. Basically, the DB hides the fact if data is in SQL or JSON structure and allows you to easily make queries independently of the actual data structure. This is extremely useful as in our case the Blockchain provides structures in JSON and we needed to digest the information without wanted to dump everything into a strict SQL table! And that works out of the box.

We've recently adopted Exadata as our primary DW solution. It allows us to quickly store and query lots of data. The data is generally written in overnight batches and is constantly queried 24x7. It even supports some online business decisions to our customers. We use it company-wide. I am an administrator of the database, as well as a SQL Programmer.

My company is a non-profit organization of a healthcare system. We adopted Oracle Data Warehouse for building a clinical data warehouse, to support quality improvement and clinical research. Our hospitals and clinics run electronic health record systems that capture clinical data through patient care; the Data Warehouse is used to store them in a permanent manner. Once stored, the data can be normalized and standardized to be used by clinical departments, clinical champions, and researchers. There were a number of use cases in the Data Warehouse, but the major two were monitoring of clinical operation and improvement of clinical workflow.

Oracle Data Warehouse were used for one of our clients to act as an analytics database that stores transactional data like Financial Transaction relation information, Customer Centric Information, Sales and Purchase related Information, Projects and Service related information. The information stored in the Oracle Data Warehouse would help the key business users to pull reports through data cubes which would help them in analyzing different aspects of business. So based on the data points received and the key indicators set, it would enable the business to make informed decisions to grow their business. One example would be to identify customers sales which could be seasonal in nature, so by understanding the patterns of their previous placed order the business can vouch for the customer next time when similar conducive scenario is in place.

Oracle Data Warehouse was used at a previous company to be the central data warehouse. Analysts connected to it to run queries on all types of different data like customer, transaction, and behavior data. It was also used to power reporting being done by the business.