Cloudera Data Platform (CDP), launched September 2019, is designed to combine the best of Hortonworks and Cloudera technologies to deliver an enterprise data cloud. CDP includes the Cloudera Data Warehouse and machine learning services as well as a Data Hub service for building custom business applications.
$0.04
per CCU (hourly rate)
Saturn Cloud
Score 7.7 out of 10
N/A
Saturn Cloud is an ML platform for individuals and teams, available on multiple clouds: AWS, Azure, GCP, and OCI. It provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a wholly hosted environment. Saturn Cloud is presented as flexible and straightforward for new data scientists while giving senior and experienced staff the
capabilities and configurability they need.…
$10
hourly $5 credit purchase to start
Snowflake
Score 8.7 out of 10
N/A
The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
I have seen that Cloudera Data Platform is well suited for large batch processes. It works really well for our indication analyses that are performed by the actuaries. I feel that rapid streaming operations may be a situation where additional technology would be needed to provide for a robust solution.
Saturn Cloud is a powerful data science platform that offers numerous benefits to organizations. It simplifies and streamlines the development, deployment, and scaling of data science and machine learning models. The platform addresses common business problems such as scalability, collaboration, efficiency, and cost-effectiveness. With Saturn Cloud, organizations can easily handle large datasets and complex computations, collaborate effectively among data science teams, automate repetitive tasks, optimize workflows, and utilize flexible and cost-efficient cloud resources. By leveraging Saturn Cloud, organizations can accelerate their data science projects, improve productivity, and achieve better outcomes in areas such as predictive modeling, recommendation systems, fraud detection, and more.
Snowflake is well suited when you have to store your data and you want easy scalability and increase or decrease the storage per your requirement. You can also control the computing cost, and if your computing cost is less than or equal to 10% of your storage cost, then you don't have to pay for computing, which makes it cost-effective as well.
Snowflake scales appropriately allowing you to manage expense for peak and off peak times for pulling and data retrieval and data centric processing jobs
Snowflake offers a marketplace solution that allows you to sell and subscribe to different data sources
Snowflake manages concurrency better in our trials than other premium competitors
Snowflake has little to no setup and ramp up time
Snowflake offers online training for various employee types
While Saturn Cloud offers a range of pre-built templates and workflows, there is currently limited support for customization. For example, users may not be able to modify the pre-configured environments that come with the templates, or may find it difficult to integrate their own custom libraries and tools. Offering more flexibility in this area could help users tailor the platform to their specific needs and workflows.
While Saturn Cloud offers a variety of pre-built environments for data science and machine learning workloads, some users may prefer to use custom Docker images instead. However, the platform currently has limited support for Docker, which can be a limitation for users who need to work with specific dependencies or custom libraries. Adding more robust support for Docker could help to make the platform more versatile and adaptable to a wider range of use cases.
Do not force customers to renew for same or higher amount to avoid loosing unused credits. Already paid credits should not expire (at least within a reasonable time frame), independent of renewal deal size.
SnowFlake is very cost effective and we also like the fact we can stop, start and spin up additional processing engines as we need to. We also like the fact that it's easy to connect our SQL IDEs to Snowflake and write our queries in the environment that we are used to
This is user friendly , better than its counterparts. Anyone familiar working with other cloud solutions for GPU will agree on this. Hence the rating of 10 was given to this. I personally love the fact that I get so much compute time for being a free user which is very efficient in terms of budget
Because the fact that you can query tons of data in a few seconds is incredible, it also gives you a lot of functions to format and transform data right in your query, which is ideal when building data models in BI tools like Power BI, it is available as a connector in the most used BI tools worldwide.
We have utilized Cloudera support quite frequently and are very satisfied with the capability and responsiveness of that team. Often, the new features delivered with the platform give us an opportunity to mature the way we're doing things, and the support team have been valuable in developing those new patterns.
We have had terrific experiences with Snowflake support. They have drilled into queries and given us tremendous detail and helpful answers. In one case they even figured out how a particular product was interacting with Snowflake, via its queries, and gave us detail to go back to that product's vendor because the Snowflake support team identified a fault in its operation. We got it solved without lots of back-and-forth or finger-pointing because the Snowflake team gave such detailed information.
IBM's offering of the Cloud Pak for Data has been a moving target and difficult to compare to Cloudera Data Platform. We have implemented our solution on Amazon Web Services, which appears to be supported by IBM at this point, but the migration would be very expensive for us to endeavor.
Saturn Cloud provides an R server, that's super important. Even you can write R on CoLab with different settings, but it is inconvenient and slow. Saturn Cloud can give me a different IDE environment that I'm more used to, even if I'm using Python. Whereas CoLab is more dedicated to Jupyter notebook
I have had the experience of using one more database management system at my previous workplace. What Snowflake provides is better user-friendly consoles, suggestions while writing a query, ease of access to connect to various BI platforms to analyze, [and a] more robust system to store a large amount of data. All these functionalities give the better edge to Snowflake.
Although we are still in the implementation phase with Saturn Cloud, we anticipate significant positive impacts on our business objectives.
The platform is expected to enhance our computational capabilities with its easy access to top-tier NVIDIA GPUs, which should accelerate our AI and machine learning projects. We believe this will lead to reduced development times and faster deployment of our generative AI models.
While Saturn Cloud provides excellent computational resources and reliable uptime, I find that their user interface could be improved. The UI can be unintuitive at times, making it a bit challenging to navigate and configure certain settings. Enhancing the user interface to be more streamlined and user-friendly would significantly improve the overall experience. Having pre-configured stacks readily available would also save time and make the platform even more efficient to use.
