We use the solution for data analytics of industrial data.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
637 reviews
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External reviews are not included in the AWS star rating for the product.
A scalable and cost-effective solution that has excellent translation features and can be used for data analytics
What is our primary use case?
What is most valuable?
We extensively use the product’s notebooks, jobs, and triggers. We can create activities. Wherever translation is required, we use Databricks. The product fulfills our customer requirements. It is a cost-effective solution.
What needs improvement?
The product should provide more advanced features in future releases.
For how long have I used the solution?
I have been using the solution for six months.
What do I think about the stability of the solution?
Our data was not too huge. It worked well. It is easily adaptable.
What do I think about the scalability of the solution?
The tool is scalable. We can make it available for a larger audience.
How was the initial setup?
The initial setup is not that difficult. I rate the ease of setup a seven out of ten. The solution is cloud-based. We use native services like Data Factory for orchestration. Sometimes, the customers require us to use Amazon as the cloud provider instead of Azure.
What's my experience with pricing, setup cost, and licensing?
The pricing is average.
What other advice do I have?
There are many services which are coming up. They are still in the preview stage. Overall, I rate the product an eight out of ten.
Helps to have a good data presence but needs to incorporate learning aspects
What is our primary use case?
The product has helped in data fabrication.
How has it helped my organization?
Databricks has helped us have a good presence in data.
What needs improvement?
The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice.
For how long have I used the solution?
I have been using the product for more than six months.
What do I think about the stability of the solution?
I rate Databricks' an eight out of ten.
What do I think about the scalability of the solution?
I rate the tool's scalability an eight out of ten.
How was the initial setup?
The transition to Databricks was smooth.
What's my experience with pricing, setup cost, and licensing?
Databricks' price is high.
What other advice do I have?
I rate the solution a nine out of ten.
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
Onboarding can be smoother
The onboarding process is not smooth. When account setup begins, theere is no way to move to a new email if previous one has not yet been activated. Also no way to know which email was used to setup the subscription sign up.
Had a great impressive experience
What do you like best about the product?
Totally impressive with Delta live tables, and created a poc with its and its really impressive with ease of use and high efficient for large datset.
What do you dislike about the product?
Azure data factory integration is not available to trigger the Delta live tables
What problems is the product solving and how is that benefiting you?
Effective in handling incremntal loads
Databricks, a rising hero for your complex data problems!
What do you like best about the product?
The ease of working at a cloud based data system (compatible with Azure/AWS/GCP) is far more better than those slow, laggy, boring legacy data warehouse systems like hadoop and others. Due to its Spark engine based built, big data processing is speedy, efficient and scalable.
Its a go to solution to my data warehouse / data lake based problems, the best of both worlds. One can integrate variety of data sources/formats like SQL-NoSQL, excel, csv, streaming data, APIs,etc. One can use a feature called Autoloader that eases our implementations of detecting and loading any kinda data in Delta Lake tables without any hard coding.
The platform is designed in such a way that in my 1 year of daily usage I haven't faced any kind of unexpected downtime/issue on platform for which I have to reach out to their customer support group.
I would highly recommend Databricks Lakehouse Platform to anyone who is looking for a powerful and flexible analytics platform.
Its a go to solution to my data warehouse / data lake based problems, the best of both worlds. One can integrate variety of data sources/formats like SQL-NoSQL, excel, csv, streaming data, APIs,etc. One can use a feature called Autoloader that eases our implementations of detecting and loading any kinda data in Delta Lake tables without any hard coding.
The platform is designed in such a way that in my 1 year of daily usage I haven't faced any kind of unexpected downtime/issue on platform for which I have to reach out to their customer support group.
I would highly recommend Databricks Lakehouse Platform to anyone who is looking for a powerful and flexible analytics platform.
What do you dislike about the product?
Browsing and accessing is a bit confusing if one is working on a resource group accounts which could have several main and segregated user based notebooks/files, accessing them is tideous task as a developer, one has to open a seperate new tab just for browsing or opening a seperate file/notebook for referencing puposes. Another thing is the frequency of UI changes in every few months, once a person gets comfortable/used to, a new UI update would pop up making it difficult for a person to adapt these changes once again.
What problems is the product solving and how is that benefiting you?
From its suit of features, it helped me processing daily arriving data from APIs and CSVs from a railway networking website platform, turing it into an Analytical solution in form of dashboard KPIs which play a vital role in taking key decisions and maintaining a lossless on-schedule railway network for real world users of the web product. It has helped me and my team in reducing operational overhead cost with its performance and reliability featues like data versioning and schema enforcement. It has also facilitated collaboration and communication in my team who are working from different corners of world remotely.
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
Best product for both datalake and data warehouse reduce the cost and faster deliver the data
What do you like best about the product?
Best product for both datalake and data warehouse
cost reduce
cost reduce
What do you dislike about the product?
logging is not good
integration to visual is bit complex
integration to visual is bit complex
What problems is the product solving and how is that benefiting you?
data distribution on big data
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