Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.
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Everpure FlashBlade
Score 9.9 out of 10
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Everpure (formerly Pure Storage) offers FlashBlade, a scale-out file and object storage – architected to consolidate complex data silos (like backup appliances and data lakes) while accelerating tomorrow's discoveries and insights.
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Google Cloud Storage
Score 9.0 out of 10
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Google Cloud Storage is unified object storage for developers and enterprises.
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.
If data storage, access, and security [are] of the highest priority to your business then Pure Storage FlashBlade is an excellent tool that must be considered. Analytics or sharing that requires the fastest speeds available will benefit from the NVMe solid-state drives they use which are far superior to spinning rust. It is less ideal for those who do not require such time-critical work.
[Google Cloud Storage is] great for storing and playing large video files, and even sharing them securely with others, whether or not they are part of your organization. No need to download video files before watching, and can also be used to store any other kinds of files.
EMR does well in managing the cost as it uses the task node cores to process the data and these instances are cheaper when the data is stored on s3. It is really cost efficient. No need to maintain any libraries to connect to AWS resources.
EMR is highly available, secure and easy to launch. No much hassle in launching the cluster (Simple and easy).
EMR manages the big data frameworks which the developer need not worry (no need to maintain the memory and framework settings) about the framework settings. It's all setup on launch time. The bootstrapping feature is great.
Really great, easy to use interface helps us manage files easily. Storage is fast and inexpensive, so we don't have to spin up storage instances locally
Great set of command-line tools to manage data and storage options via scripts and apps, as well as an SDK means we can build GCS into our orchestration and operations tools
Robust integration with other Google cloud tools means that we don't have to think too hard about using GCS for a variety of storage tasks as we interact with other Google services.
It would have been better if packages like HBase and Flume were available with Amazon EMR. This would make the product even more helpful in some cases.
Products like Cloudera provide the options to move the whole deployment into a dedicated server and use it at our discretion. This would have been a good option if available with EMR.
If EMR gave the option to be used with any choice of cloud provider, it would have helped instead of having to move the data from another cloud service to S3.
When reporting out a user has exceeded there quote, it only references the UID. It would certainly be nice it calls out the UID name that is clearly present in the Dashboard.
The ability to determine a snapshot total size would be helpful.
Proactive reachout to discuss new versions and assist in planning the upgrade would be a key win.
after all of the investment made in the tool and considering how many teams use it I think we would not be likely to move away from this tool. A lot of our information including historical is already here and we are happy with the capabilities of the tool currently
Documentation is quite good and the product is regularly updated, so new features regularly come out. The setup is straightforward enough, especially once you have already established the overall platform infrastructure and the aws-cli APIs are easy enough to use. It would be nice to have some out-of-the-box integrations for checking logs and the Spark UI, rather than relying on know-how and digging through multiple levels to find the informations
Very easy to use. I love having my data backed up. I love that Google Cloud Storage provides me with the peace of mind that I no longer need to worry about my data being lost. I can now sleep better at night. Google Cloud Storage is very easy to use. Overall, you save time and have less stress by using Google Cloud Storage.
For performance i give Google Cloud Storage 10 of 10 on performance because even though there are other softwares that do exactly the same thing as Google Drive, it still works exceptionally well. It is very fast, and and far as integration, the only software I have used with it that integrated was Google Docs, and of course it integrates perfectly.
I give the overall support for Amazon EMR this rating because while the support technicians are very knowledgeable and always able to help, it sometimes takes a very long time to get in contact with one of the support technicians. So overall the support is pretty good for Amazon EMR.
Without exception, the contacts with support have been quick and extremely knowledgeable. I do not fear getting an underqualified engineer to assess or work on my arrays. In addition to this support structure, the sales engineers are top notch as well.
We have never used official support from Google for our Google Cloud Storage, but there is plenty of documentation in place already. With a small amount of work, anybody should be able to get started. Once needs get more complicated, there is still documentation from Google, but also plenty of community support for common use cases around the internet.
overall I was not directly involved but hears the teams were satisfied with the implementation. the teams that used the tool did not encounter major issues, it was as expected with minor issues and bugs that were resolved later. The more significant learning curve was actually starting to use the tool
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.
The NetApp a800 we tested was 14% faster than Pure FlashBlade with NFS workloads. However, NetApp lacked ease of administration and performing simple tasks such as creating multiple NFS volumes required scripting from the command line. Our flashblade contained 15 baldes and our NetApp was a clustered pair with each half containing 24 nvme devices.
We prefer Google Cloud Storage over Amazon Web Services because of the tools and code integration offered by Google Cloud Storage. We found the Google Cloud Storage toolset to be highly usable and give us the fine-grained control we need for managing digital assets. Ultimately, we chose Google Cloud Storage because we found the API and suitability for code integration with our Java codebase to be impeccable and because we had excellent direct support from the Google Cloud Storage team
It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.
We were able to consolidate 5 different storage platforms of lesser performance onto a single Flashblade and achieve much, much lower latency and higher throughput.
We've been able to reduce the amount of training and configuration required to just Pure Flashblade, instead of 5 different vendors and products.
In addition to our core use cases, Flashblade has capabilities that we are pursuing for some new projects, i.e. analytics data store and the object store features.
It has assisted greatly with our ability to share documents/information cross functionally. Especially within our advertising team, we store a large amount of information to assist new hires and refresh current employees.
Something that could improve is employees' understanding of how to best utilize Google Cloud Storage. This could improve by implementing a potential training video or tutorial.
Overall, Google Storage has been great. I have not used a similar storage product that had the same enterprise level capabilities.
