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Databricks corporation is fantastic to work with
What do you like best about the product?
quality (features, performance, stability) of the product and associated documentation, and responsiveness of Databricks support staff
What do you dislike about the product?
at present i don't dislike anything, i hope databricks coroporation will be able to continue providing a resilient well maintaned product
What problems is the product solving and how is that benefiting you?
clinical trial data review
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Unified Platform for Data Engineering, Data Science , Generative AI and Data Governance
What do you like best about the product?
Ease of use for setting up data pipelines
What do you dislike about the product?
the customer support is little less useful for more complex issues.
Many times patches are applied on workspaces which lead to failues
Many times patches are applied on workspaces which lead to failues
What problems is the product solving and how is that benefiting you?
Databricks is providing one single platform for all of Data Engineering , Data Science and Ops
It is leading and fast in adopting the new cutting edge tech
It is leading and fast in adopting the new cutting edge tech
Centralized Governance through Unity Catalog.
What do you like best about the product?
My comments on the Lakehouse are specific to Unity Catalog (UC):
Governance is all about being a " benevolent bad cop" to the enterprise audiences! That message , up until now(i.e advent of UC), was mostly /only possible via a 'stale Power Point' and , after the Governance teams enforce compliance standards , possibly due to an adverse event of data breach. WHat I have been able to 'show-and-tell' via live DBX UC demo's to the largest healthcare provider enterprise users has captured the rapt attention of the folks! That is my experience. Now coming to the features that UC offers - OKTA Inegration to rope in the Identities of any IAM system over to UC, APIs to setup ACCESS GRANTS & SCHEMA OBJECTS creation, Security via RLS/CLM, and above all, I feel, the cross-workspace access setup to ensure LOBs/Teams with Data Assets across several Catalogs, goes a long way to ensure seamless & ubiqutous data sharing.
The featuers allow for Power Users who are skilled in ANSI SQL to execute their querries across the three namespace architectures (catalog.schema.tables) once the cross WS access is setup. Now coming to the ML Model building Data Scientists and Citizen Data Scientist, the centralized storing of the Model Experiment with its features can be registered in Unity Catalog to ensure Centralized governance of the ensuring endpoints that enable Model Serving.
The Future release of ABACS (as opposed to RBACs) could deliver compute/cluster economies of scale/scope from a cost perspective while making Sensitive Data MAsking and Tagging at a DDL level seamless.
Another eagerly anticipated feature would be autmated sensitive data identification & tagging via the OKERA Integration of all "DBx registered Data Assets in DBx Catalogs".
The use of Service PRinciples as identities opens the scope to intelligently manage /address the limitation of the number of AD groups /Global Groups that can be created.
These are my current observations.
Governance is all about being a " benevolent bad cop" to the enterprise audiences! That message , up until now(i.e advent of UC), was mostly /only possible via a 'stale Power Point' and , after the Governance teams enforce compliance standards , possibly due to an adverse event of data breach. WHat I have been able to 'show-and-tell' via live DBX UC demo's to the largest healthcare provider enterprise users has captured the rapt attention of the folks! That is my experience. Now coming to the features that UC offers - OKTA Inegration to rope in the Identities of any IAM system over to UC, APIs to setup ACCESS GRANTS & SCHEMA OBJECTS creation, Security via RLS/CLM, and above all, I feel, the cross-workspace access setup to ensure LOBs/Teams with Data Assets across several Catalogs, goes a long way to ensure seamless & ubiqutous data sharing.
The featuers allow for Power Users who are skilled in ANSI SQL to execute their querries across the three namespace architectures (catalog.schema.tables) once the cross WS access is setup. Now coming to the ML Model building Data Scientists and Citizen Data Scientist, the centralized storing of the Model Experiment with its features can be registered in Unity Catalog to ensure Centralized governance of the ensuring endpoints that enable Model Serving.
The Future release of ABACS (as opposed to RBACs) could deliver compute/cluster economies of scale/scope from a cost perspective while making Sensitive Data MAsking and Tagging at a DDL level seamless.
Another eagerly anticipated feature would be autmated sensitive data identification & tagging via the OKERA Integration of all "DBx registered Data Assets in DBx Catalogs".
The use of Service PRinciples as identities opens the scope to intelligently manage /address the limitation of the number of AD groups /Global Groups that can be created.
These are my current observations.
What do you dislike about the product?
Not a "poke in the eye" of the hard working Solutions Enginners who face us the clients, music , but ....
1. The Product Engg teams appear to lack digesting the Governance Narratives that enterprises expect , out of the box, not wait for a product release.
2. The fact that Spark engine centric DBx compoutes/workspaces will see a heavy legacy SQL code with all its fun (hard coding, nest sub-querries, temp tables use, CTAS et al....) , the product engg teams appear to not hav such folks at " Product Desgin" phase. Ditto, moresoever, for point #1
3. The publicly available documentation pertaining to features appears to be stale when compared with the features being released.
4. The commitment to deliver a features (example ABACS) on the set date, has spanned several quarters over close to two years! When you promise solving world hunger and keep moving the goal post , credibility is impaired.
1. The Product Engg teams appear to lack digesting the Governance Narratives that enterprises expect , out of the box, not wait for a product release.
2. The fact that Spark engine centric DBx compoutes/workspaces will see a heavy legacy SQL code with all its fun (hard coding, nest sub-querries, temp tables use, CTAS et al....) , the product engg teams appear to not hav such folks at " Product Desgin" phase. Ditto, moresoever, for point #1
3. The publicly available documentation pertaining to features appears to be stale when compared with the features being released.
4. The commitment to deliver a features (example ABACS) on the set date, has spanned several quarters over close to two years! When you promise solving world hunger and keep moving the goal post , credibility is impaired.
What problems is the product solving and how is that benefiting you?
Hey, how come your smart alecs did not realize that we use Dbx for "Data Governance ". List that also!!
Databricks provides seamless faster data processing for our customers.
What do you like best about the product?
Unity Catalog, Delta Live Tables, Lakehouse solutions
What do you dislike about the product?
Nothing as such I observed so far, All the features are awesome.
What problems is the product solving and how is that benefiting you?
Enterprise Lakehouse, Delta Live Tables
Databricks Lakehouse Platform Advantages
What do you like best about the product?
1. Support for ACID transactions, time travelling, versioning
2.unity catalog for access control
2.unity catalog for access control
What do you dislike about the product?
As databricks Lakehouse is built on top of delta lakes, it some times throws errors that are related to storage
What problems is the product solving and how is that benefiting you?
1. Storage and retriving the data and able to perform transformation on huge amounts of data without any hiccups
Data quality
What do you like best about the product?
Inbluild authentication authorization/ unity catalog
What do you dislike about the product?
Would be nice if we have proper documentation and notes for every activities in databricks. Not able to get some documents via datsbricks website
What problems is the product solving and how is that benefiting you?
No need of external extraction tool to pull datas from different sources
Advance cloud
What do you like best about the product?
While processing the data from source to destination and accessing verity of data
What do you dislike about the product?
Some more features and streaming data not possible
What problems is the product solving and how is that benefiting you?
Everything is fine but other platforms are advanced now
So user friendly and a platform to make the organization's data value chain delivering value
What do you like best about the product?
unified platform for both BI and AI workload
What do you dislike about the product?
To difficult to keep on track with the evolution pace that platform is growing
What problems is the product solving and how is that benefiting you?
Its helping to realise the paradigm of data-centric AI
A complete platform for data science and engineering
What do you like best about the product?
Cluster creation is now made easy through a simple configuration page.
Workspace allows you to organise all your notebooks in one place.
Job mode allows to plan notebook execution and to plan dev/prod pipelines.
Workspace allows you to organise all your notebooks in one place.
Job mode allows to plan notebook execution and to plan dev/prod pipelines.
What do you dislike about the product?
Data visualization of notebooks output cells is basic, even if it is good for simple application. Dashboard section could be improved by increasing clarity. These are however minor complaints.
What problems is the product solving and how is that benefiting you?
Databricks is helping me saving time when developing code and running jobs at given datetimes.
The autocomplete tool is very efficient, specially when dealing with very long codes and installing python packages or java library is no longer a problem.
The autocomplete tool is very efficient, specially when dealing with very long codes and installing python packages or java library is no longer a problem.
Databricks Lakehouse
What do you like best about the product?
Its awesome data warehouses platform to help to extract the data or metadata from data lakehouse. Data tables it help is to build the Ai/ML models.
What do you dislike about the product?
all services are good nothing to dislike.
What problems is the product solving and how is that benefiting you?
it's solve the Data Science and Machnie learning problems
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