Amazon EMR (Elastic MapReduce) vs. SAP Vora

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.

Incentivized

I spent more than 1 year with SAP Vora, SAP Datahub and SAP Leonardo with ML, iOt. I believe this product has potential but it is not easy to adopt. SAP has to keep in mind how open-source big data technologies are able to deliver quick results. I know SAP is stabilizing and fighting hard against many open source technologies, but it still has a long way to go there.

Incentivized

• 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.

Incentivized

• Modelling with SAP HANA and Hadoop
• Realtime Analysis using Vora and HANA as a Streaming engine
• Time series Analysis on large chunks of datasets
• Machine learning capabilities on Hadoop tables and spark contexts

Incentivized

• 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.

Incentivized

• Vora 2.0 in on premise scenarios could be improved, as adoption of the cloud is not an easy sell.
• Kubernetes and Docker integration need to be more seamless and quick to understand. If this is simplified, it will be easy to adopt
• Data hub orchestration and integrations could be simplified so that quick adoption within SAP BW, ECC, S4 HANa scenarios is possible.

Incentivized

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.

Incentivized

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.

Incentivized

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.

Incentivized

• 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.

Incentivized

• Negative impact would be Poc and RFI will need more time to adopt and decision making gets delayed
• Positive impact would be it's a great leap from SAP to adopt a Big data technologies and AI within cloud stream. But selling is going to take time.

Incentivized