Amazon DynamoDB is a cloud-native, NoSQL, serverless database service.
$0
capacity unit per hour
Oracle Autonomous Data Warehouse
Score 8.3 out of 10
N/A
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.
N/A
Pricing
Amazon DynamoDB
Oracle Autonomous Data Warehouse
Editions & Modules
Provisioned - Read Operation
$0.00013
capacity unit per hour
Provisioned - Write Operation
$0.00065
capacity unit per hour
Provisioned - Global Tables
$0.000975
per Read Capacity
On-Demand Streams
$0.02
per 100,000 read operations
Provisioned - Streams
$0.02
per 100,000 read operations
On-Demand Data Requests Outside AWS Regions
$0.09
per GB
Provisioned - Data Requests Outside AWS Regions
$0.09
per GB
On-Demand Snapshot
$0.10
per GB per month
Provisioned - Snapshot
$0.10
per GB per month
On-Demand Restoring a Backup
$0.15
per GB
Provisioned - Restoring a Backup
$0.15
per GB
On-Demand Point-in-Time Recovery
$0.20
per GB per month
Provisioned - Point-in-Time Recovery
$0.20
per GB per month
On-Demand Read Operation
$0.25
per million requests
On-Demand Data Stored
$0.25
per GB per month
Provisioned - Data Stored
$0.25
per GB per month
On-Demand - Write Operation
$1.25
per million requests
On-Demand Global Tables
$1.875
per million write operations replicated
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Offerings
Pricing Offerings
Amazon DynamoDB
Oracle Autonomous Data Warehouse
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Amazon DynamoDB
Oracle Autonomous Data Warehouse
Features
Amazon DynamoDB
Oracle Autonomous Data Warehouse
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad
II would recommend Oracle Autonomous Data Warehouse to someone looking to fully automate the transferring of data especially in a warehouse scenario though I can see the elasticity of the suite that is offered and can see it is applicable in other scenarios not just warehouses.
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.
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.
It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.
Does not require continous attention from the DBA, autonomous features allows the database to perform most of the regular admin tasks without need for human intervention.
Allows to integrate multiple data sources on a central data warehouse, and explode the information stored with different analytic and reporting tools.
Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.
It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.
Understanding Oracle Cloud Infrastructure is really simple, and Autonomous databases are even more. Using shared or dedicated infrastructure is one of the few things you need to consider at the moment of starting provisioning your Oracle Autonomous Data Warehouse.
The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.
As I mentioned, I have also worked with Amazon Redshift, but it is not as versatile as Oracle Autonomous Data Warehouse and does not provide a large variety of products. Oracle Autonomous Data Warehouse is also more reliable than Amazon Redshift, hence why I have chosen it
I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.
Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.
It has allowed us to add more scalability to some of our systems.
As with any new technology there was a ramp up/rework phase as we learned best practices.
Overall the business objective of all of our clients have been met positively with Oracle Data Warehouse. All of the required analysis the users were able to successfully carry out using the warehouse data.
Using a 3-tier architecture with the Oracle Data Warehouse at the back end the mid-tier has been integrated well. This is big plus in providing the necessary tools for end users of the data warehouse to carry out their analysis.
All of the various BI products (OBIEE, Cognos, etc.) are able to use and exploit the various analytic built-in functionalities of the Oracle Data Warehouse.