Likelihood to Recommend We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.
Read full review 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.
Read full review Pros Amazon Elastic MapReduce works well for managing analyses that use multiple tools, such as Hadoop and Spark. If it were not for the fact that we use multiple tools, there would be less need for MapReduce. MapReduce is always on. I've never had a problem getting data analyses to run on the system. It's simple to set up data mining projects. Amazon Elastic MapReduce has no problems dealing with very large data sets. It processes them just fine. With that said, the outputs don't come instantaneously. It takes time. Read full review 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. Read full review Cons Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR. Like some of the enterprise editions EMR does not provide on premises options. No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well. Read full review 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. Read full review Likelihood to Renew Because
It is really simple to provision and configure. 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. Read full review Usability I give Amazon EMR this rating because while it is great at simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data, it can be run slow, freeze and glitch sometimes. So overall Amazon EMR is pretty good to use other than some basic issues.
Read full review Support Rating There's a vast group of trained and certified (by AWS) professionals ready to work for anyone that needs to implement, configure or fix EMR. There's also a great amount of documentation that is accessible to anyone who's trying to learn this. And there's also always the help of AWS itself. They have people ready to help you analyze your needs and then make a recommendation.
Read full review Implementation Rating 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.
Read full review Alternatives Considered Snowflake is a lot easier to get started with than the other options.
Snowflake 's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
Read full review 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
Read full review Return on Investment Positive: Helped process the jobs amazingly fast. Positive: Did not have to spend much time to learn the system, therefore, saving valuable research time. Negative: Not flexible for some scenarios, like when some plugins are required, or when the project has to be moved in-house. Read full review 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. Read full review ScreenShots