Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Saturn Cloud
Score 7.6 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.…
Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life …
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same …
The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when …
Databricks has a much better edge than Synapse in hundred different ways. Databricks has Photon engine, faster available release in cloud and databricks does not run on Open source spark version so better optimization, better performance and better agility and all kind of …
Databricks [Lakehouse Platform (Unified Analytics Platform)] can work with all data types in their original format while Snowflake requires additional structures to fit the data before loading it. Databricks is open source so potential is far greater.
Databricks was picked among other competitors. Closest competition in our organization was H2O.ai and Databricks came out to be more useful for ROI and time to market in our internal research. We could have used AWS products, however Databricks notebooks and ability to launch …
When we started using it, only the notebook experience was mature. However, DB was very helpful giving us direct support to get onto their platform. Really there was little in the way to compare to them at the time. AWS has services but not the same low-cost angle.
I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer …
Like said earlier the main keypoint being more compute time . This is the single most advantage Saturn Cloud has over others. This enables us to first try and then eventually move onto Saturn Cloud for the work making it a smooth experience and a rewarding one to say the least.
unlike google colab, the free part of saturn cloud is much more robust. I recently tried to train some BERT models with some data, and to be honest, colab was lagging behind in terms of processing and ram. also, the paid part is much more expensive compared to saturn cloud.
Runtimes are not automatically deleted, transparent free resources: 30hrs a month, persistent disk of 10 GB. The pricing on the premium plan is transparent. No credits that get deleted after 90 days. Possibility to run ipynb notebooks that were written in an IDE, no need to …
Saturn Cloud is an exceptional data science platform that offers a multitude of advantages to organizations. It excels in simplifying and optimizing data science workflows, providing scalable infrastructure resources, and promoting efficient collaboration among teams. With its …
We chose Saturn Cloud over other tools like Databricks and Google Colab because:1. Easy to Use: Saturn Cloud is simple and fun, just like working on a notebook.2. Sizing Help: Saturn Cloud lets us use bigger or smaller computers when we need them, saving money and time.3. Super …
I have used AWS EC2 , GCP Compute Instance, Paperspace Gradient etc. I have selected Saturn Cloud since it provides cost effective and also more reliable and available compute machines compared to the above list.
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 …
Saturn cloud has a niche market, being a reseller of cloud instances, it ofcourse has a higher cost than the giants but Saturn Cloud is well-suited for organizations that want a cloud-based data science platform that is easy to use and scalable. AWS, GCP, and Azure are more …
It has more free resources to use. Nowadays, all platforms are using the cloud, so I currently use it often. Previous platforms are being used in local host environments. Local host = not so much usage of resources. Cloud platforms = High usage of free resources and …
Saturn Cloud is way cheaper as compared to AWS Sage Maker, and also easy to use we get a notebook setup with the correct environment on the click of a single button. The UI is also a bit simpler and understandable which helps in explaining non-tech individuals and reduces the …
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
1. Large-scale data processing: If your organization needs to process vast amounts of data, Saturn Cloud's parallel computing capabilities make it an ideal choice for handling these tasks efficiently and quickly.
2. Complex machine learning projects: Saturn Cloud is beneficial when working on machine learning projects requiring scalable resources and powerful computational capabilities, such as training deep learning models or running complex algorithms.
3. Collaborative data science work: Saturn Cloud provides an excellent environment for data scientists and engineers to collaborate on projects, share resources, and maintain version control, ensuring consistency and smooth teamwork.
Less appropriate scenarios for Saturn Cloud: Small-scale projects: For smaller projects with limited data and less demanding computational requirements, Saturn Cloud's advanced features might not be necessary.
There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.
Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
Visualization in MLFLOW experiment can be enhanced
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.
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.
in terms of graph generation and interaction it could improve their UI and UX
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
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life boost.
Saturn Cloud is an exceptional data science platform that offers a multitude of advantages to organizations. It excels in simplifying and optimizing data science workflows, providing scalable infrastructure resources, and promoting efficient collaboration among teams. With its user-friendly interface and seamless integration with popular tools, Saturn Cloud enhances productivity and accelerates the development of data science models. The platform's automation capabilities streamline repetitive tasks, freeing up valuable time for experimentation and analysis. Additionally, Saturn Cloud's cost-effective approach, with on-demand cloud resources, ensures efficient resource utilization and budget optimization. Its features for version control, reproducibility, and deployment management further solidify Saturn Cloud's position as a superior choice for organizations seeking to leverage the power of data science effectively.
Faster experimentation and model iteration: Saturn Cloud's scalability and user-friendly interface can help organizations to reduce the time required to set up and run experiments, as well as to iterate on models more quickly. This can help to speed up the development cycle and get products to market more quickly.
Increased productivity and efficiency: Saturn Cloud's built-in tools and pre-built environments can help to streamline data science workflows and reduce the time required to set up and configure environments. This can help data scientists to focus on higher-value tasks and improve overall productivity.