Amazon EMR (Elastic MapReduce) vs. Azure Data Lake Storage

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

Azure Data Lake is an absolutely essential piece of a modern data and analytics platform. Over the past 2 years, our usage of Azure Data Lake as a reporting source has continued to grow and far exceeds more traditional sources like MS SQL, Oracle, etc.

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

• Setting up Azure Data Lake Storage account, container is quite easy
• Access from anywhere and easy maintenance
• Integration with Azure Data Factory service for end to end pipeline is pretty easy
• Can store Any form of data (Structured, Unstructured, Semi) in faster manner

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

• study for the certifications also to have them as a reference for work when you have any questions about applying a configuration to the equipment.
• The Internet interface is simple and easy to use. Capacity is good and it's good that HP continues to innovate with this technology

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

Azure Data Lake Storage from a functionality perspective is a much easier solution to work with. It's implementation from Amazon EMR went smooth, and continued usage is definitely better. However, Amazon EMR was significantly cheaper overall between the high transaction fees and cost of storage due to growth. The two both have their advantages and disadvantages, but the functionality of Azure Data Lake Storage outweighed it's cost

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

• Instead of having separate pools of storage for data we are now operating on a single layer platform which has cut down on time spent on maintaining those separate pools.
• We have had more of an ROI with the scalability as we are able to control costs of storage when need be.
• We are able to operate in a more streamlined approach as we are able to stay within the Azure suite of products and integrate seamlessly with the rest of the applications in our cloud-based infrastructure

Incentivized