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Oracle Autonomous Data Warehouse

Score10 out of 10

241 Reviews and Ratings

What is Oracle Autonomous Data Warehouse?

Oracle Autonomous Data Warehouse is optimized for analytic workloads, including data marts, data warehouses, data lakes, and data lakehouses. With Autonomous Data Warehouse, data scientists, business analysts, and nonexperts can discover business insights using data of any size and type. The solution is built for the cloud and optimized using Oracle Exadata.

Categories & Use Cases

A quick way to analyze your data

Use Cases and Deployment Scope

Oracle Autonomous Data Warehouse is being used by our organization to help our clients get the best out of their data which was earlier analyzed using Microsoft Excel. It is currently being used by one department of the company which caters to an Australian insurance giant. The clients want to better utilize the claims data in order to reduce the pathing cost and increase income from recoveries.

Pros

  • Fast performance
  • User friendly
  • Fully managed
  • Data security

Cons

  • More data connection to different applications can be introduced
  • Pricing
  • User support service

Return on Investment

  • It has helped generate key insights, reducing the cost of company per claim
  • It has helped gather average recovery information from each claim
  • It helped streamline the process of data analysis

Other Software Used

Jira Software, Atlassian Confluence, Microsoft Powerpoint

Oracle ADWH for manufacturing company

Use Cases and Deployment Scope

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:

<ul><li>Object storage</li><li>Oracle Autonomous Data Warehouse</li><li>Oracle Analytics cloud</li></ul>After a first as-is analysis, the project steps have been:

<ul><li>Export of data from the company ERP on flat files</li><li>Creation of a staging area layer</li><li>Creation of PL/SQL procedures to import files on staging structures, performing formal checks, cleaning and standardization processes</li><li>Creation of entity-relation models for some Data Marts</li><li>Creation of ETL flows to load data from the staging area on the Data Marts</li><li>Creation of a series of institutional reports, based on Data Marts</li><li>Profiling of users to access to the reporting layer and for free ad hoc analysis</li></ul>The project has recently been deployed and the architecture is currently being used by about fifty users.

Pros

  • It's really fast to set up (like 2 minutes to create a new database)
  • It's cheap, and its costs are based on dedicated resources (RAM and CPUs), and it can eventually be turned off
  • Resources (RAM and CPUs) can be increased or decreased at run-time
  • Patching and release upgrades are automatically performed by Oracle at scheduled times
  • It's secure, without the need to implement a VPN: it provides a wallet that includes encryption methods for authentication
  • It automatically extracts statistics (needed by Oracle database engine to improve performances) on its structures
  • Backups are automatically performed and very easy to restore
  • Disaster recovery is granted thanks to fault domains provided by Oracle

Cons

  • It's really limited from a DBA point of view
  • There is only 1 tablespace associated to all the users you create on the database
  • The cost (license and monthly fees) are not always very clear
  • The loading of data on the cloud is subjected to network speed, so huge amounts of data may take a lot of time to be loaded on the database

Return on Investment

  • No costs for hardware or server rooms
  • Cost reduction, turning off or decreasing resources (RAM or CPUs) for the database when it's not needed
  • No costs for maintenance of the database (i.e.: patching or resources monitoring)
  • No costs for backups and disaster recovery

Awesome in-house solution!

Use Cases and Deployment Scope

The blockchain style for data storage and analysis. We have many different data formats from XML, HTML, JSON and this tool is very useful in organizing and querying all types of data at once. Getting all of our data centralized in one place and then only using one tool to look at all of it is hugely beneficial.

Pros

  • Integration with other Oracle products is very easy.
  • Centralizing different data formats.
  • Smooth implementation.

Cons

  • Learning curve can be high if the user is inexperienced.
  • Can get expensive with options.

Return on Investment

  • Faster reports
  • Faster queries
  • In-house solution

Oracle Autonomous Data Warehouse + Oracle Analytics Cloud + Oracle Blockchain Platform = 🥰

Use Cases and Deployment Scope

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.

Pros

  • Connecting to Oracle Blockchain Platform out of the box
  • JSON or SQL? ADW makes handling both as if they are the same!

Cons

  • The basic setup comes with quite some power. The power is often too much for a data warehouse which is used to aggregate data just a dozen times per day and is due to caching not queries for the data sets that often. A smaller shape - yet bigger than the always free - would be great!

Return on Investment

  • Oracle Autonomous Data Warehouse is approx 100 times faster spin up than manual setup of an Oracle DB.
  • 3h annual maintenance for big version releases (18c to 19c) compared to est. 500h annual maintenance with manual database updates.

The Oracle Autonomous Data Warehouse - My dream come true

Use Cases and Deployment Scope

We use Oracle Autonomous Data Warehouse for one specific application that store[s] alarm[s] and incidents as an historical database. It address business problem[s] with regard to preventive maintenance that analysts can query more than a billion rows very fast, to identify certain patterns. This historical database was migrated from an older Oracle database into the Autonomous Data Warehouse and now we don't need to think about upgrading the database and having machine/database available. Now the database is always available and if it is not need[ed] for some time, we just stop it, and when there is need to query the data I start it up again.

Pros

  • Very easy and fast to load data into the Oracle Autonomous Data Warehouse
  • Exceptionally fast retrieval of data joining 100 million row table with a billion row table plus the size of the database was reduced by a factor of 10 due to how Oracle store[s] and organise[s] data and indexes.
  • Flexibility with scaling up and down CPU on the fly when needed, and just stop it when not needed so you don't get charged when it is not running.
  • It is always patched and always available and you can add storage dynamically as you need it.

Cons

  • Not sure what these can be

Return on Investment

  • Just positive as we wanted to decommission an old application/database and now we have all the historical data - always available and it hardly cost us anything to store it on the Oracle Autonomous Data Warehouse.