Databricks Data Intelligence Platform
Databricks, Inc.External reviews
637 reviews
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Slice through billions of records like butter
What do you like best about the product?
As a data scientist I'm able to analyze billions of records from my web browser without having to worry about memory or CPU limitations on my laptop.
What do you dislike about the product?
There are a lot of ways to configure the platform which can be intimidating for new users.
What problems is the product solving and how is that benefiting you?
Enable analysis of large datasets that was not possible with legacy tools.
"Unlocking Data Potential: A Comprehensive Review of Databricks"
What do you like best about the product?
Interactive user interface.
A powerful tool that contains Computer power, integration with different sources of data, integrated Machine Learning, and collaboration with team using the workspace.
Provide workflow and Delta live Table pipeline which makes this fantastic.
A powerful tool that contains Computer power, integration with different sources of data, integrated Machine Learning, and collaboration with team using the workspace.
Provide workflow and Delta live Table pipeline which makes this fantastic.
What do you dislike about the product?
I am not able to migrate Dlt pipelines from the dev workspace to the prod workspace using Azure DevOps, if there is any way please let me know.
What problems is the product solving and how is that benefiting you?
it provides workspace, notebook, Apache spark, Delta Lake, and Scalability which processes large amounts of data easily.
It has a centralized environment by which team can work collaboratively.
Its integration power can integrate with ADF, SYNAPSE and Data warehouse easily.
It has a centralized environment by which team can work collaboratively.
Its integration power can integrate with ADF, SYNAPSE and Data warehouse easily.
Interesting Product for Data Science
What do you like best about the product?
Data lineage is one of the valuable features I like in Unity Catalog. It makes it easy for us to do data debugging and analysis of data calculations with access control.
What do you dislike about the product?
Not much, but for ease of understanding, the data lineage visualization section needed some work.
What problems is the product solving and how is that benefiting you?
In the data science project, many times we needed to verify and recheck the calculations of the KPI matrix and what table and transformation were needed for this. We had difficulty tracing back the flow of calculations through the original data. The Unity Catalog directly provides a data lineage feature for this. We can easily determine what data transfer was required and how this KPI matrix was calculated from the original data through the dashboard and graphs with the access control feature.
Databricks - Powerful Product for all Data and AI Needs
What do you like best about the product?
Databricks Data platform is single unified, democratized solution for all data needs, I really like the recently launched SQL warehouse serverless feature where you don't need to worry about ec2 machines provisioning as well in your cloud account. Apart from it, I really like unity catalog , sql alerts, databricks dashboards features and creating automated workflow(jobs flow) either via databricks api or integrating Databricks Airflow operator. The visibility that databricks provide via audit tables like infrastructure cost, checking user activities etc. is something that set it apart. Also Pandas Spark Api provides us feasiblity to use distributed computing in existing python pandas code without much changes.
What do you dislike about the product?
Sometimes there are unplanned downtime for the platform which irritates us. Some documentation pages lacks examples. The AI assistant right now only able to solve/give sql responses only for simple sql asks. In cases of complex sql and sql failures, the `Diagnose error` is not relevant.
What problems is the product solving and how is that benefiting you?
The benefit that we are getting from databricks platform is that we don't need to manage inhouse spark plus cataloging service and we as a data team can focus more on generating useful insights from raw data. Using Databricks All Purpose clusters, we are able to provide our customer (Business Analytics, Product, Backend) separate clusters to do adhoc analysis such that one system doesn't impact other one. We are able to generate quick ROI in terms of performance and less query errors while migrating from Amazon redshift to databricks
Good product for data engineering.
What do you like best about the product?
A clear architecture that follows a pattern makes the tool easier to use.
Customer service is fantastic; they helped me with every problem I ran across.
Using Terraform for databricks integration and setup greatly facilitates workspace management.
With databricks, creating data products is simple. We have no trouble providing our clients with our product on schedule.
Work is made easier with excellent performance in data intake and curation.
economical: Starting clusters and stopping them after work saves a lot of money compared to using other data performance solutions.
Customer service is fantastic; they helped me with every problem I ran across.
Using Terraform for databricks integration and setup greatly facilitates workspace management.
With databricks, creating data products is simple. We have no trouble providing our clients with our product on schedule.
Work is made easier with excellent performance in data intake and curation.
economical: Starting clusters and stopping them after work saves a lot of money compared to using other data performance solutions.
What do you dislike about the product?
I feel there are no disadvantages in databricks.
What problems is the product solving and how is that benefiting you?
improving the problem of having to spend more money on more data engineering tools. provide prompt, high-quality customer service to enable the team to work more effectively.
The Most Efficient and Detailed AI Solution for Data
What do you like best about the product?
Databricks has been integrated with nujmerous software, such as Grammarly.
The software has a unified implementation plan, which makes the process of content generation manageable.
Databricks provides a unique analytical feature, with simplistic deployment measures.
The software has a unified implementation plan, which makes the process of content generation manageable.
Databricks provides a unique analytical feature, with simplistic deployment measures.
What do you dislike about the product?
Databricks has adeqaute assistance in making every AI generative process swift and resourceful. We have obtained maximum support and detailed analytics from Databricks.
What problems is the product solving and how is that benefiting you?
Databricks acts as a reliable data analytical solution, when handles even master database swiftly.
More so, the data warehouse makes the accessibility process faster, and all the predictive analysis is done accurately.
The integration with other functional software including Grammarly makes the tool more helpful.
More so, the data warehouse makes the accessibility process faster, and all the predictive analysis is done accurately.
The integration with other functional software including Grammarly makes the tool more helpful.
The ETL Governance Monitoring stands out from the vast landscape of Big Data tools.
What do you like best about the product?
Espically the "Unity Catalog" and "Fix feature on the anomaly in Data lineage" are the wow features that truly impress.
What do you dislike about the product?
Nothing, because as of now it offers all the features in one tool, which is truly impressive.
What problems is the product solving and how is that benefiting you?
We haven't encountered any issues; our POC is working well and the actual implementation is excellent.
My databricks experience in my work
What do you like best about the product?
Databricks is useful for his scabilty, control large volumetry of data and also small volume.
What do you dislike about the product?
Maybe the point of attention is to improve the dashboard part.
What problems is the product solving and how is that benefiting you?
Transfer large volume of data quickly.
Data Engineering with Databricks
What do you like best about the product?
I like Databricks because it provides all the elements necessary for mass data processing, the different components (notebook, cluster, jobs) are very well integrated and quite easy to use. A lot of training is offered to understand in depth the possibilities offered in Databricks, which makes it much easier to use.
What do you dislike about the product?
The Dashboard part integrated into Databricks is quite limited and does not allow the creation of complex and relevant dashboards
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of scalability, it's easy to manage a large amount of data as a smaller one.
Databricks for data world
What do you like best about the product?
It provides some key features like workspace, notebook, Apache spark, Delta Lake, and Scalability. It provides a centralized environment where teams can collaborate. We can work on Jupyter Notebook with Apache Spark which provides the engine that powers all processing of dataset. It provides Cross-team collaboration so engineers, analysts, scientists, and ML engineers can work seamlessly on the same platform. It also provides consistency with notebooks, users can transition between tasks and programming languages without the need for context-switching.
What do you dislike about the product?
In this Databricks platform, we can't migrate a library from the dev workspace to the prod workspace.
What problems is the product solving and how is that benefiting you?
It provide some key features like workspace, notebook, Apache spark, Delta Lake and Scalability.
It provide centralized evn where team can collaborate.
We can work on jupyter notebook with Apache spark which provide engine that power all processing of dataset.
It provide Cross-team collaboration so enengineers, analysts, scientists and ML engineers can work seamlessly in the same platform.
Consistency: with notebooks, users can transition between tasks and programming languages without the need for context-switching.
It provide centralized evn where team can collaborate.
We can work on jupyter notebook with Apache spark which provide engine that power all processing of dataset.
It provide Cross-team collaboration so enengineers, analysts, scientists and ML engineers can work seamlessly in the same platform.
Consistency: with notebooks, users can transition between tasks and programming languages without the need for context-switching.
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