Saturn Cloud
Deepnote
Apache Zeppelin
Amazon SageMaker
Databricks Unified Analytics Platform
Azure Synapse Analytics
Google BigQuery
GeoSpock
Apache Cassandra
Redis
MongoDB
ArangoDB
neo4j
PostgreSQL
OrientDB
MySQL
Saturn Cloud is an award-winning ML platform with 75,000+ users, including NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. It is an all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Users can spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, build large language models, and more in a completely hosted environment.
Data scientists and analysts work best using the tools they want to use. You can use your preferred languages, IDEs, and machine-learning libraries in Saturn Cloud. We offer full Git integration, shared custom images, and secure credential storage, making scaling and building your team in the cloud easy. We support the entire machine learning lifecycle from experimentation to production with features like jobs and deployments. These features and built-in tools are easily shareable within teams, so time is saved and work is reproducible.
Saturn Cloud Apache CassandraSmooth and bug free experience. There are ready data science images with pre loaded packages for most common scenarios, making you focus on the project/problem and leave the infrastructure part to Saturn Cloud.
True story, way better than just sweating Colab. The best and cheapest compute services there is.
I have started using this to run the computations which generally require like 64+GB of RAM, and the procedure to setup the enviroment is also nice. Got all the R packages running smoothly.
Based on our record, Apache Cassandra should be more popular than Saturn Cloud. It has been mentiond 42 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Not 100% sure of your intention, but if you work with python, and you're familiar with (or can spend the time learning) dask, and willing to pay, you can consider coiled.io or saturncloud.io that offer managed dask that you can scale and use GPUs etc (again, not sure if applicable to your use case). Source: over 1 year ago
SaturnCloud - Data science cloud environment, that allows to run Jupyter notebooks and Dask clusters. 30 hours free computation and 3 hours of Dask per month. - Source: dev.to / over 1 year ago
I think your site looks good and I have used the type of service you offer, but there are 2 potential problems. As SheepherderPatient51 said,Google already offers all of this for free (and so does https://kaggle.com and https://www.paperspace.com ). There are also other sites just like yours such as https://deepnote.com,https://saturncloud.io, and https://lambdalabs.com . Source: over 1 year ago
* How does it differ from other GPU cloud providers that offer ready to use Jupyter notebooks? (E.g. https://support.genesiscloud.com/support/solutions/articles/47001170102-running-jupyter-notebook-or-jupyterlab-on-your-instance or https://saturncloud.io/). - Source: Hacker News / over 2 years ago
At the moment I am going to go to https://saturncloud.io/ or https://www.cloudeo.group/. Source: over 2 years ago
Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many commodity servers without a single point of failure. - Source: dev.to / 3 days ago
Distributed storage Distributed storage systems like Cassandra, DynamoDB, and Voldemort also use consistent hashing. In these systems, data is partitioned across many servers. Consistent hashing is used to map data to the servers that store the data. When new servers are added or removed, consistent hashing minimizes the amount of data that needs to be remapped to different servers. - Source: dev.to / about 2 months ago
On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra. - Source: dev.to / 3 months ago
HBase and Cassandra: Both cater to non-structured Big Data. Cassandra is geared towards scenarios requiring high availability with eventual consistency, while HBase offers strong consistency and is better suited for read-heavy applications where data consistency is paramount. - Source: dev.to / 4 months ago
Dear r/python, we are happy to present you with our first open-source project. We have managed to implement a new driver for Python that works with Apache Cassandra, ScyllaDB and AWS Keyspaces. Source: 9 months ago
Deepnote - A collaboration platform for data scientists
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Apache Zeppelin - A web-based notebook that enables interactive data analytics.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
