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Databricks is seriously good stuff
What do you like best about the product?
Date of breakfast well integrated and crafted suite of applications making a harmonious environment for all kinds of data, data warehouse and applications.
What do you dislike about the product?
Sometimes things are not as smooth and easy as you wish but principally because features still in progress of improvements and discovery. I actually often find the shortcoming today is a naturally smooth add to the suite after the next release
What problems is the product solving and how is that benefiting you?
The common ones you are most looking for help with, velocity variety, column, and veracity. Made best and easiest with a thoughtfully integrated and crafted singular platform.
The best platform for all Data & AI needs
What do you like best about the product?
I've implemented Databricks solutions for multiple large enterprises and have therefore frequently used the product over many years. The platform scales extremely well and really helps build the data & AI ecosystem within large enterprises. Their support from solutions architects has always been extremely good. The platform itself is easy to use and integrating it with for example other cloud services (in Azure) is easy.
What do you dislike about the product?
For legacy users, migrating stuff from all purpose compute to UC can be painful.
What problems is the product solving and how is that benefiting you?
Many. For my current client (IKEA) We are building an e-learning data platform, to share learning insights to all IKEA franchisees globally.
Databricks Unified Data Intelligence Platform for all Genes - Data Engineers, Data Scientists
What do you like best about the product?
I've being using Databricks for more than 7 years and used almost all of services workflow, Unity Catalog, scale in and scale out the cluster depending on the demand.
Due to the in-build capabiity of spark , ease of integration and wonderful support, we have seen customers are relying more towards to the databricks.
Due to the in-build capabiity of spark , ease of integration and wonderful support, we have seen customers are relying more towards to the databricks.
What do you dislike about the product?
During 2nd quarter of 2023 we have have not seen Databricks was not focusing towards to the Gen AI.
What problems is the product solving and how is that benefiting you?
Databricks Unified platform serving for all genes Data Engineers, Data Scientists.
Utilize out of box wokflow to automate the entire jobs.
Flexiblity of scheduling the batch vs continuous jobs depending on the need.
Utilize out of box wokflow to automate the entire jobs.
Flexiblity of scheduling the batch vs continuous jobs depending on the need.
Perfect Location for Lakehouse
What do you like best about the product?
With Lakehouse bringing in all company data on one place in layered medallion architecture, we could use Open AI services right out of the box to leverage Gen AI capabilities either out of the box or by connecting to Cloud providers Open AI services. And with recent acquisition of Einblick it makes it much more intuitive for self-service analytics for business users without stressing on the analysts.
What do you dislike about the product?
More support for DevOps could be better. Also with unity catalog, if there are too many tables, views in one schema or too many schema, it makes it difficult to scroll thru in UI
What problems is the product solving and how is that benefiting you?
We are bringing in all of our clients products data, customer data, transactions, end customers, distributors data in one place for better understanding of customers buying patterns and feedback to help our marketing and sales team to collaborate more effectively
Databricks has being able to allow me better technical solution by being in the edge of data
What do you like best about the product?
I've being using Azure Databricks everyday for the last 5 years, it allow easy corporate level security and governance. Relieve us on the need to deal with the setting up of clusters and bring to us the most up to date technologies in the data world.
The customer support has been effective, with people that really want to help. We were able to integrate with the Azure services, usually easier then when use native Azure like Data Factory and Synapses.
The customer support has been effective, with people that really want to help. We were able to integrate with the Azure services, usually easier then when use native Azure like Data Factory and Synapses.
What do you dislike about the product?
The notebook only concept was the most downside of Databricks, Not being able to follow the usual ways of working of the development was really critical, even though the notebooks allow easy testing and data analysis. Gladly it was solved it year by a better integration with IDEs
What problems is the product solving and how is that benefiting you?
Enabling building ETL Pipelines that make governance, security and robustness out of the box
Data mining
What do you like best about the product?
It is the best way to manage all the data at a single place.
What do you dislike about the product?
I guess nothing is for disliking about it
What problems is the product solving and how is that benefiting you?
By the help of databricks i used to to process my data in one short and manage it and spread it among the client side for the further processing
Use databricks!
What do you like best about the product?
The best thing about the platform is the ease of use, it is literally Spark as a service.
What do you dislike about the product?
What you need is to have a more comfortable way to connect to the notebooks via an IDE.
What problems is the product solving and how is that benefiting you?
It saves me from having to create my own services and manage them.
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
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!!
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