Amazon S3 is a cloud-based object storage service from Amazon Web Services. It's key features are storage management and monitoring, access management and security, data querying, and data transfer.
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Databricks Lakehouse Platform
Score 8.3 out of 10
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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…
Amazon S3 is a great service to safely backup your data where redundancy is guaranteed and the cost is fair. We use Amazon S3 for data that we backup and hope we never need to access but in the case of a catastrophic or even small slip of the finger with the delete command we know our data and our client's data is safely backed up by Amazon S3. Transferring data into Amazon S3 is free but transferring data out has an associated, albeit low, cost per GB. This needs to be kept in mind if you plan on transferring out a lot of data frequently. There may be other cost effective options although Amazon S3 prices are really low per GB. Transferring 150TB would cost approximately $50 per month.
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.
Fantastic developer API, including AWS command line and library utilities.
Strong integration with the AWS ecosystem, especially with regards to access permissions.
It's astoundingly stable- you can trust it'll stay online and available for anywhere in the world.
Its static website hosting feature is a hidden gem-- it provides perhaps the cheapest, most stable, most high-performing static web hosting available in PaaS.
Web console can be very confusing and challenging to use, especially for new users
Bucket policies are very flexible, but the composability of the security rules can be very confusing to get right, often leading to security rules in use on buckets other than what you believe they are
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
It is tricky to get it all set up correctly with policies and getting the IAM settings right. There is also a lot of lifecycle config you can do in terms of moving data to cold/glacier storage. It is also not to be confused with being a OneDrive or SharePoint replacement, they each have their own place in our environment, and S3 is used more by the IT team and accessed by our PHP applications. It is not necessarily used by an average everyday user for storing their pictures or documents, etc.
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
AWS has always been quick to resolve any support ticket raised. S3 is no exception. We have only ever used it once to get a clarification regarding the costs involved when data is transferred between S3 and other AWS services or the public internet. We got a response from AWS support team within a day.
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.
Overall, we found that Amazon S3 provided a lot of backend features Google Cloud Storage (GCS) simply couldn't compare to. GCS was way more expensive and really did not live up to it. In terms of setup, Google Cloud Storage may have Amazon S3 beat, however, as it is more of a pseudo advanced version of Google Drive, that was not a hard feat for it to achieve. Overall, evaluating GCS, in comparison to S3, was an utter disappointment.
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 time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
It practically eliminated some real heavy storage servers from our premises and reduced maintenance cost.
The excellent durability and reliability make sure the return of money you invested in.
If the objects which are not active or stale, one needs to remove them. Those objects keep adding cost to each billing cycle. If you are handling a really big infrastructure, sometimes this creates quite a huge bill for preserving un-necessary objects/documents.
