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
Snowflake
Score 8.9 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.
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.
If you need a quick query, snowflake is the way to go. It's super simple and scalable; we were struggling before with Azure, and with Snowflake, everything runs smoothly, and we have more control over our schemas and warehouses. Snowflake, in my opinion, is the next step when you want to scale your business and manage data. If your company is still small, there may be cheaper options.
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.
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
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
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
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
The interface is similar to other SQL query systems I've used and is fairly easy to use. My only complaint is the syntax issues. Another thing is that the error messages are not always the easiest thing to understand, especially when you incorporate temp tables. Some of that is to be expected with any new database.
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.
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.
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.
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can buy according to your needs. And the integration options with various tools like Sigma are add-ons.
