We work on three platforms. Databricks is hosted on Azure for us, so we work with ADFS, Azure Data Factory, and also the AWS Cloud. We work for some customers.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
628 reviews
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and
External reviews are not included in the AWS star rating for the product.
Ease to use for data management
What do you like best about the product?
I have very great experience with Databricks and I love to work on their platform. Their supptive nature make data visualization easy provide hassle free services.
What do you dislike about the product?
The integration of vanta provide ease implementation and also gives best ML algorithm to work on big data analytics.
What problems is the product solving and how is that benefiting you?
Overall, my experience with databricks data management system is really great and I am satisfied with their features and pricing that fullfill our all business needs and requirements.
Excellent platform for data professionals
What do you like best about the product?
With Databricks, my experience has incresead a lot! It's wonderful how simple are to share my work with peers and use incredible tools with a great performance when it's processing huge pipelines of data.
What do you dislike about the product?
Nothing to say at this time, the platform are perfect
What problems is the product solving and how is that benefiting you?
Databricks solve the problem of share information and work together with other people. Before my team had a lot of issues when we needed to backup each other in difficult tasks. Now, anyone can help, see and develop in the project.
Perfect solution for Data Insights
What do you like best about the product?
The reliability and user interface of Databricks gives next level of experience to working on it. Their infrastructure gives multiple features and ease to integrate option where we directly connected them with our cloud system.
What do you dislike about the product?
Their pipeline and data governance tool make work more ease and effective that gives high quality insights to our operational team.
What problems is the product solving and how is that benefiting you?
Their , ML infrastructure and new updated features provides a quality of services with reliable experience. I really refer Databricks with my social and personal group.
Databricks Intelligence Change the data perception
What do you like best about the product?
its unified approach to data management, allowing seamless integration of data warehousing, data lakes, and machine learning capabilities on a single platform, making it easy to work with diverse data types while maintaining high performance and scalability, all while offering a user-friendly interface with natural language features for simplified data exploration and analysis.
What do you dislike about the product?
its relatively high cost, especially for smaller organizations, potential complexity in managing cluster configurations, challenges with identifying error sources within large data pipelines, and occasional difficulties with integrating with other tools, particularly for data visualization purposes.
What problems is the product solving and how is that benefiting you?
reducing costs, improving data quality, and accelerating decision-making processes across an organization
Highly Recommended Data Intelligence platform
What do you like best about the product?
They have very good product features and different type of Integrations and customer support is also support. your ticket will be ressolved within minutes.
What do you dislike about the product?
Home screen User Interface they can improve and for the first time user they can definitely add the learning or guide features
What problems is the product solving and how is that benefiting you?
We used this software for BDA for our business where we deal with huge database on daily basis and to find out some outcomes and patterns . which will be passed on to our data science team and after further verification we use those finding in our new product development cycle.
All in all Data Intelligence software
What do you like best about the product?
I liked the lakehouse architecture and performance of data warehouse helps to analyse data in large scale and it can be integrated with AWS, gcloud , azure , and MLflow integration feature was good.it also usable with tablue and database sql.
What do you dislike about the product?
Prize is high and not suitable for small companies
What problems is the product solving and how is that benefiting you?
We used this tool for analyzing data in large scale for that this tool was efficient
Ideal for large-scale data processing and collaboration
What do you like best about the product?
I like the way Databricks does data management, all in one place. What it does is it unites the data engineers and data scientists on the same platform to collaborate and solve problems quickly. Scaling became effortless thanks to the integration with tools like AWS, as well as keeping up with the progress in the notes continues to keep us all on the same page in the notes. It’s helped remove communication issues and it’s helped take care of things faster.
What do you dislike about the product?
Databricks has one downside and that is the learning curve, especially for people that want to get started with a more complex configuration. We spent some time troubleshooting the setup, and it’s not the easiest one to begin with. The pricing model is also a little unclear, so it isn’t as easy to predict cost as your usage gets bigger. At times that has led to some unforeseen expenses that we might have cut if we had better cost visibility.
What problems is the product solving and how is that benefiting you?
We’ve seen Databricks drastically improve our efficiency. Now we can manage large datasets, run machine learning models, and work in real time but without worrying about working with different tools. It’s allowed us to simplify our data pipeline and speed up decision making, which has been a big win for the team. At the same time, this has allowed faster product development cycles all the way to shorter time to market for new features.
Big data processing software tool of 2025
What do you like best about the product?
The user interface is very easy to use and simple of databricks and navigation is also simple which helps you to do the things quickly
What do you dislike about the product?
Customer support need to improve and they need to recruit people in their team from Asia to overcome the regional language issue
What problems is the product solving and how is that benefiting you?
We used databricks for big data processing as we usually cater to US real estate client, we process large volume of data using the datbricks and able to find out some insights and patterns around the data and based on those result we presented the strategy to our clients for implementation and the end result is aslo very much satisfied and we retained most of our US client by giving them such kind of strategic planning.
A trusted partner for scalable and smart data workflows.
What do you like best about the product?
A productive maintenance solution for manufacturing equipment was implemented by my team using Databricks. We identified patterns that indicated potential failures by analysing IoT sensors data. What I like best about Databricks is the integration with IoT hubs, which allowed us to collect data from our devices. It also offers pre-build ML libraries which help us during the model development phase and saved us valuable time.
What do you dislike about the product?
Some optimal configurations needs to be added when integrating IoT data streams with Databricks, which was a bit complex. That's why we faced some challenges during this phase of project.
What problems is the product solving and how is that benefiting you?
Our team used Databricks to optimise inventory management by analysing sales pattern, supply, Chinita and seasonal trends. This automated pipeline/workflow, reduced errors and insured timely analysis. The platform also improved team collaboration, as multiple departments could view and edit the data directly within the Databricks environment.
Shared notebooks and scheduling enhance cost efficiency
What is our primary use case?
What is most valuable?
The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster. This reduces costs. The scheduling part is managed by Databricks itself, for example, when it is idle, it will automatically turn off. All these features are handled by Databricks, reducing costs. We do not need to schedule separately.
For example, on AWS EC2, we have to create a Lambda function or use System Manager templates to schedule EC2 and EMRs. Here, it is taken care of, saving significant resources.
Additionally, notebooks can be shared within the development team which saves effort. Developers can share their notebooks. Git and Azure DevOps integration on the Databricks side is also very helpful.
What needs improvement?
The API deployment and model deployment are not easy on the Databricks side. We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools. Moreover, the API deployment should be simplified for ease of deployment and consumption.
For how long have I used the solution?
I have been using Databricks for approximately two and a half to three years.
What do I think about the scalability of the solution?
We have not faced any shortages so far. The clusters are available on demand, thus we have not encountered any scalability issues.
How are customer service and support?
We mostly had limited data support required from Databricks. Whenever we did need support, within two or three days the problem was solved. I would rate them ten out of ten.
How would you rate customer service and support?
Positive
What about the implementation team?
We bought it as a service, which is why we never implemented it ourselves. We do not have any implementation team.
Which other solutions did I evaluate?
For companies focused solely on data transformation, transferring data between databases, and not tackling machine learning or deep learning problems, I recommend ADF. It would be sufficient and cost-saving compared to a full-fledged solution like Databricks. However, for data analytics and solving ETL problems, one should consider Databricks.
What other advice do I have?
I would rate it nine out of ten.
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