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Overview
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.…
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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…
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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.
Oracle Autonomous Data Warehouse Competitors
- Amazon Redshift
- Microsoft SQL Server
- SAP Business Warehouse (SAP BW), formerly SAP NetWeaver Business Warehouse
Oracle Autonomous Data Warehouse Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Frequently Asked Questions
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.
Amazon Redshift and Microsoft SQL Server are common alternatives for Oracle Autonomous Data Warehouse.
The most common users of Oracle Autonomous Data Warehouse are from Enterprises (1,001+ employees).
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October 07, 2021
A quick way to analyze your data
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.
- Fast performance
- User friendly
- Fully managed
- Data security
- More data connection to different applications can be introduced
- Pricing
- User support service
April 12, 2021
New era of data warehouse
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.
- Excellent performance, ease of use, great scalability, and most importantly excellent integration with Oracle GoldenGate for real-time data view
- Fully managed enterprise class, full-featured relational database that brings the power of Oracle technology to managed Cloud; support for private endpoints to keep data private
- Multi-user, high concurrency real-time reporting from across several data sources
- Level of integration or compatibility to connect it to different applications can be improved
- The support service is slow
- The issue is with the record number limitation of not being able to bring back more than one million records or not being able to export larger datasets to Excel
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).
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).
- To work in SQL and PL SQL and create high index database. Its user base is very huge so it is used in most of the company which helps in building profile.
- Easy integration in application development, I have used in python currently.
- Developers must know the backend as well so that they can build a scalable product.
- If you learn SQL using Oracle, which cover most of the syntax, then you will be proficient in SQL and can easily work on other tools too (for ex, MySQL and PostgreSQL).
- In Oracle Data Warehouse I used to build Type 1 and Type 2 Load frequently, where I did not face any issue, so nothing to improve from a product functionality point of view.
- If UI is more interactive as in Informatica, then maybe more users can start using this
- Blog link must be there on one site for solving user issues.
December 14, 2020
Oracle Autonomous Data Warehouse An Awesome Tool That Gets Better The More Your Organization Uses It
Oracle Autonomous Data Warehouse is being used by our company to full
manage our warehouse inventory. Time that was wasted previously doing
repetitive tasks is now delegated and we did not have to worry about any
database management.
- fully autonomous
- Easy to use
- Fast query performance
- Complicated and time consuming setup
- Several discovery calls are needed in order to begin process
- Oracle customer service is not always responsive
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.
- Simple and quick provisioning and configuration
- Really good and fast ETL features allow quick data load
- Always pached, always available. Foerget about many tedious admin tasks
- Pricing may we high when using all features
December 13, 2020
First hand with Oracle Autonomous Data Warehouse
<h3><span style="font-size: 15px; color: rgb(21, 21, 33); font-family: "Source Sans Pro", sans-serif; font-weight: 500;">One of the biggest challenge is the scaling and provisioning of hardware & software for any data ware house solution and also the associate problems like configuration, security and importantly tuning of the storing and fetching/querying data. For the same reason Oracle Autonomous Data Warehouse tool used for one of the project for the customer. It help overcome some of critical, complex, manual and time consuming tasks like hardware procurement, configuration, security, high availability and high performance all this with reducing administrative cost and no human error.</span></h3>
- Scale Up & Down as you need to reduce the cost immediately.
- Save lots of time and costs, helped project delivery in less time which is a big plus for customer and vendor. Also, saving administrative cost.
- Performance Tuning, security, backup and high availability.
- Combine the abilities of a data lake and a data warehouse to manage any data type for business analysis.
- The analytics come with it to code collaboratively with the rest of your team, not the classic data miner GUI interface.
- Due to security limitations, neither the powerful GUI development environment Application Express (yet?) nor Oracle R Enterprise are not available.
December 11, 2020
New-generation data warehousing and analytics with Oracle Autonomous Data Warehouse
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.
- Extremely fast query execution for large volumes of data
- Very rich library of statistical and aggregation functions
- We can access the underlying data objects from Multiple IDEs such as SQL developer
- Highly granular and robust access control on data warehouse objects
- Sometimes when we run queries the error codes/details are not detailed or very helpful
- We need a built-in easy-to-use data pumping tool
- I get confused sometimes between the schema vs user in Oracle Autonomous Data Warehouse (it is the same)
December 10, 2020
ADW review
We use the Oracle Autonomous Data Warehouse as my company central data warehouse where we store the different department's data in corresponding tables
- This eliminates nearly all the manual and complex tasks that can introduce human error
- Database providing built-in support for multi-model data and multiple workloads such as analytical SQL, machine learning, graph, and spatial.
- I need to spend lot a time to find the appropriate technical document
- Pricing
Oracle Autonomous Data Warehouse is used in automation of provisioning and configuring a data warehouse. It is also used to tune and scale the data warehouse as needed. It is used across the whole organization.
Business problems that it addresses: Since the manual work is almost completely eliminated, the cost of administering a data warehouse is reduced significantly. Also, this is highly secured and reliable.
Business problems that it addresses: Since the manual work is almost completely eliminated, the cost of administering a data warehouse is reduced significantly. Also, this is highly secured and reliable.
- High performance using continuous query optimization, table indexing, data summaries, and auto-tuning
- Autonomous data encryption and security patch application
- Different deployment models--shared, dedicated, and cloud@customer
- Built in analytics--this makes data loading, indexing, and building good data visualization models easier
- Improved machine learning capabilities
- I find it to be the best autonomous solution out there with high scalability and reliability
- More capabilities of Analytics Cloud
November 26, 2019
ADW is a fantastic cloud data warehouse
Oracle Autonomous Data Warehouse is the data warehousing tool for our company in cloud. It is currently being utilized by the transportation department, but we are in the process of expanding its adoption to other departments once the single sign-on issues with the connection to our core Oracle ERP Cloud is resolved. We have a need for many summary reports which could be a challenge from OTBI, hence we are planning to do summarization at the ADW level and then report out of ADW using Oracle Analytics Cloud.
- Data warehouse in-cloud, that is the biggest plus point.
- No need to worry about backups or maintenance since it is in-cloud.
- Oracle Analytics Cloud comes with a default connector to ADW which makes it handy in terms of integration. OAC is our reporting tool that sits on top of ADW.
- ODI Marketplace is a free add-on that can be installed on the compute of ADW and could be used as a complete ETL tool.
- Oracle ADW should definitely improve in terms of cumbersome connectivity with third-party tools like ODI Marketplace.
- Oracle Data Sync that is used to load data into ADW does not work if we have single sign-on enabled.
- ADW can also be priced on a subscription basis instead of universal credits. This way customers need not be hooked all the time concerned about overages but use the tool to its full capacity.
October 02, 2019
Not the most affordable data warehousing solution
Oracle Data Warehouse is being used across our entire organization. Although each department in our organization has its own individual use cases for maintaining a data warehouse, the overall problems that are being solved are the same. We are using Oracle Data Warehouse for business intelligence related queries, finding trends in periods of time using all the historical data, and for deep data exploration.
- Quick and easy deployment. There is no hassle in setting up software and maintaining hardware.
- Highly available and scalable. Accessing the data warehouse is easy, and it can scale up based on the data size requirements.
- Autonomous functionality. With the help of machine learning, autonomous data warehousing reduces the amount of time spent managing it.
- 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.
September 18, 2019
Oracle Autonomous Warehouse Review
We used Oracle Autonomous Database for our warehouse reporting. It is a 50 TB huge database and reports are generated every day. It is pretty read intensive database. It's availability is required for 24 X 7. We have several thousands customers using it on a regular basis. The performance is phenominal.
- Read queries
- ETL process
- Performance.
- Use case for hybrid environment
- Price factor
- Clustering
January 04, 2019
Oracle Data Warehouse is the 800 Pound Gorilla
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.
- Handles workloads like a champ.
- Uses state-of-the-art analytic functions and allows for quick, easy SQL.
- "Secret Sauce" integrates the hardware and the software for faster I/O.
- Supports thousands of concurrent users.
- We had issues converting a legacy DW (with its existing indexes, etc) over to the new DW hardware. Given the memory-intensive resources, not all indexes are advised. Traditional query tuning methods do not work. You have to re-learn some tuning tactics.
- Given the number of features it has, it is far more complex to administer. Requires trained staff to support.
- Support in these areas is generally poor. Oracle is, sadly, no exception.
- I HATE the current push to the cloud. Seems like a gigantic money-grab.
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 is a well-known and already validated product. Its performance, technical support, documentation, online community, and sustainability is the best among the area.
- It is easy to find and hire good data developers, data architects, and analysts who specialize in Oracle Data Warehouse.
- It is easy to develop a financial plan based on the product, as its licensing is systematic. Also, the product's scalability is well developed with licensing policy and it makes it easier to flexibly plan budgets as we need more functionalities and services.
- It is very expensive product. But not to mention, there's good reasons why it is expensive.
- The product should support more cloud based services. When we made the decision to buy the product (which was 20 years ago,) there was no such thing to consider, but moving to a cloud based data warehouse may promise more scalability, agility, and cost reduction. The new version of Data Warehouse came out on the way, but it looks a bit behind compared to other competitors.
- Our healthcare data consists of 30% coded data (such as ICD 10 / SNOMED C,T) but the rests is narrative (such as clinical notes.). Oracle is the best for warehousing standardized data, but not a good choice when considering unstructured data, or a mix of the two.
August 02, 2018
My Oracle DW Review
The Oracle Data Warehouse is a robust platform being used by many departments for Data Storage, Reporting from Stored Data, & also to build Marketing products. Mostly used for Analytics, as it is easy to query the data & stores a huge amount of data in a pre-processed form required for analytics of a variety of business aspects.
- Scalable for the storage of very large data.
- Simple queries to pull down the data compared to transactional databases.
- Compatibility with variety of other tools, & industry support.
- Not effective if we compare it with current Big Data applications.
- Other warehouses are better for parallel processing.
- Prices are high for few functionalities which are supposed to be bought separately.
December 12, 2017
Great performance for large databases
The Oracle Data Warehouse provides a robust data warehousing platform that is used across my entire organization. Oracle Data Warehouse is directly used by a wide range of departments from analytics and data sourcing, to finance and marketing. For an organization that deals with huge volumes of data, Oracle DW is a great option for warehousing.
- Running analytics queries is made simple on Oracle DWH.
- Great support for large databases.
- Even while dealing with large volumes of data, the compression capabilities of Oracle DWH ensure that most use cases are scalable.
- While the Oracle Data Warehouse provides top notch performance, the pricing options make it unaffordable for most small and medium businesses.
- Most add-on features that businesses can’t do without are priced separately.
- Customizability options are limited. Cannot modify most features to suit the custom needs of Business.
November 01, 2017
Complete Data Warehouse Solution - ODW
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 is scalable and reliable.
- Seamless integration with oracle database's using synchronous and asynchronous connectivity thus giving real time data representation to the key business users.
- Good performance and high availability of data from the Data Warehouse for analytical reporting.
- Like any data warehouse, one needs to conduct a cost benefit analysis to see whether the IT efforts required for implementation of the data warehouse and the cost involved in maintaining the data would be adding monetary values to the organization.
- Data Ownership could be one concern where the management needs to decide who would be having what access to the Data Warehouse. So proper configuration of the access would be required, so that there is no breach in data ownership.
July 26, 2016
Oracle based BI Solution with Oracle Data Warehouse
Currently, we are using Oracle Data Warehouse for service analytics, sales order management and install base module analytics. Business users are using it to solve their business problems where they want to analyse & understand different service contracts made by company, products orders, sales in different territories.
- It provides ease, feasibility and flexibility to use Oracle out of box pre-built solutions with different verticals and horizontals of industries such as Insurance Analytics, Financial Analytics, Sales and Marketing Analytics, etc.
- It's always easy to use Oracle Data Warehouse where you have Oracle EBS and other Oracle Transactional Systems. Since it provides pre-built data models based on various standard BI solutions (in ERP & CRM Areas).
- With various customers and businesses using Oracle dbs for transactional systems, it is always easy to recommend oracle data warehouse in numerous beneficial ways like cost, ease for implementation and maintenance, support for various top Oracle BI tools efficiently, etc
- Query Performance in Oracle Data warehouse compared to other parallel execution dbs is a little low. But given a chance on concentration with good Oracle DB design can overcome those problems in warehouse.
- For Big Data Implementation, it's not very cost effective compared to other big data solutions that are based on Hadoop systems and light weight BI Tools.
- It's more preferable with Dimensional Modeling compared to 3NF models datawarehouse.
July 22, 2016
Data Warehousing with a hint of OLTP
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.
- Strong developer toolset. e.g. PL/SQL, partitioning, compression, etc.
- Rich syntax
- Rock solid reliability
- Compatibility with other tools
- Industry support
- More automated functionality (e.g. automated table analysis, better automated partitioning)
- Support for shared nothing architecture
- Steep learning curve
July 20, 2016
Build Your Data Warehouse Using Oracle
My organization designs, builds and maintains a data warehouse for various clients both from public and private sectors. Based on the individual client needs the data warehouse is being used both at the department level and as well as at the entire organization level. The data warehouse is being used to answer and provide insights to the organizations' various activities. Using the data warehouse the organizations are able to derive advanced analytic analysis and in turn increase fraud detection, efficiency and much more.
- 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.
- For the query rewrite related to the materialized views the optimizer at times goes against the base tables. There is room for improvement for the optimizer to make more intelligent choices. There does exist functionality to identify the reason why the optimizer failed to do the rewrite of the original query. This can be further expanded.
- On the storage indexes currently there is limitation of only 8 columns. In addition Oracle decides which of the columns are chosen as part of the storage index. It would be nice to see if both of these are addressed in future versions/releases.
- Some of the features are 'Options' which would increase the overall licensing cost and is an important factor for certain class of users/clients. It would be nice to see if at least some of the options are standard functionality.