Databricks Data Intelligence Platform
Databricks, Inc.External reviews
669 reviews
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External reviews are not included in the AWS star rating for the product.
Unified Data Management and Cost Savings with Databricks
What do you like best about the product?
We handle sensitive financial data across both Azure and AWS. Before using Databricks, our access control was disorganized, with a confusing mix of different IAM roles. The lakehouse architecture now allows us to keep our data in one place, so we no longer need to move it to a separate warehouse. This has helped us avoid the extra storage costs that would have come with maintaining a separate warehouse.
What do you dislike about the product?
The only problem I have encountered is with the pricing; otherwise, I haven't faced any other issues.
What problems is the product solving and how is that benefiting you?
This is an excellent solution for centralizing data governance and ensuring compliance with banking data requirements.
Effortless Workflow Management with Databricks Data Intelligence
What do you like best about the product?
As a tour and travels organization our goal is to provide seamless and fast experience to our customers. With Databricks Data Intelligence we can track and manage our internal workflow very efficiently, as it gives a very brief report our analytics. We often uses it to scale our business and security. It's UI is very clean and easy to understand dashboard gives us total freedom of our management.
What do you dislike about the product?
The cost is greater compared to similar platforms.
What problems is the product solving and how is that benefiting you?
In today's world, where AI is becoming increasingly prevalent, it offers many useful features that help save time and automate much of our work efficiently.
Unified, User-Friendly Platform That Accelerates Data Insights
What do you like best about the product?
What I like best about the Databricks Data Intelligence Platform is how it brings everything data engineering, analytics, and machine learning together in one unified environment. It’s very user-friendly despite being powerful, and it makes collaboration between technical and non-technical teams much easier. The platform handles large volumes of data efficiently, scales smoothly, and integrates well with cloud services, which saves a lot of time and effort. Overall, it helps turn raw data into meaningful insights faster without making the process overly complex.
What do you dislike about the product?
One thing I dislike about the Databricks Data Intelligence Platform is that it can feel complex and overwhelming for new users, especially those without a strong technical background. The learning curve is quite steep, and it takes time to fully understand how to use all the features effectively. Additionally, the pricing can become expensive as usage scales, and cost management isn’t always very transparent. Sometimes, debugging errors or performance issues can also be challenging, particularly in large or highly integrated workflows.
What problems is the product solving and how is that benefiting you?
Databricks Data Intelligence Platform solves the problem of working with fragmented data systems by bringing data engineering, analytics, and machine learning into a single, unified platform. Instead of managing multiple tools, everything is available in one place, which reduces complexity and data silos. This benefits me by saving time, improving collaboration across teams, and enabling faster access to reliable insights. It also handles large-scale data processing efficiently, allowing better decision-making based on real-time and accurate data rather than delayed or incomplete information.
Versatile Coding and Pipeline Creation Made Easy
What do you like best about the product?
Databricks is very helpful for writing code in various languages, including SQL and Python, among others. It is also valuable for building pipelines and creating new notebooks, which assist in designing complex queries.
What do you dislike about the product?
The cost of the cluster we use is somewhat high, which restricts the amount of code we are able to run and execute.
What problems is the product solving and how is that benefiting you?
In Databricks, we have the flexibility to write a combination of both Python and SQL code, which makes it easier to break down complex queries into smaller, more manageable parts. Additionally, Databricks supports integration with external platforms like GitHub, allowing us to map the code path for pipeline execution.
Powerful unified data platform with great collaboration
What do you like best about the product?
Databricks is easy to use and simple to get started with, even when handling large amounts of data. It brings everything like data processing, analytics, and AI into one place, so my team do not need multiple tools. The platform integrates smoothly with common tools like BI dashboards, workflows, and cloud services. Its notebooks make day to day work frequent and convenient by allowing our teams to collaborate in real time. There are many built in features for data, analytics, and machine learning without added complexity. Customer support, documentation, and community resources make it easier to solve issues quickly.
What do you dislike about the product?
Databricks can be expensive and unpredictable in cost, especially for small teams if workloads run longer than expected. It takes technical expertise to set up, manage, and optimize performance, which can be challenging for non-technical users. Costs need frequent monitoring since compute and storage are billed separately. While it has basic dashboards, it still depends on tools like Power BI or Tableau for full reporting.
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of managing data across many different tools by putting everything in one place. This saves time, reduces confusion, and makes it easier for teams to work together. It helps us handle large data smoothly, keep access and security under control, and get useful insights faster even for people who are not very technical.
Unified Data Platform That Simplifies Complex Workflows
What do you like best about the product?
What I like best about Databricks is how it brings data engineering, analytics, and machine learning together on one platform. Having a unified environment built around Apache Spark makes collaboration between data teams much easier. The Lakehouse approach works well because it removes the need to move data across multiple tools. Performance is strong for large datasets, and notebooks make experimentation, analysis, and collaboration more efficient. Overall, it simplifies complex data workflows while still being powerful.
What do you dislike about the product?
The main downside is the cost and complexity for new users. Pricing can be hard to predict, especially when workloads scale unexpectedly, and compute costs can rise quickly if not monitored closely. There is also a learning curve for teams that are not already familiar with Spark or cloud-based data platforms. Some advanced configurations and optimizations require experienced resources, which can slow adoption for smaller or less mature data teams.
What problems is the product solving and how is that benefiting you?
Databricks helps us solve the problem of working with large, fragmented datasets across different tools and teams. Earlier, data engineering, analytics, and machine learning were handled in separate systems, which created silos and slowed down insights. With Databricks, we can process, analyze, and model data on a single platform, which improves collaboration and reduces data movement.
From a business perspective, it helps us generate insights faster, scale analytics as data grows, and improve data reliability. This leads to quicker decision-making, more consistent reporting, and better use of data for forecasting and optimization, while reducing operational overhead.
From a business perspective, it helps us generate insights faster, scale analytics as data grows, and improve data reliability. This leads to quicker decision-making, more consistent reporting, and better use of data for forecasting and optimization, while reducing operational overhead.
It is a very expensive cloud stack but it does deliver the right performance along with it
What do you like best about the product?
Databricks Data Intelligence Platform is very reliable and that is nice to know that cloud native architecture did not go down right after I deployed it on kubernetes. Honestly, I thought the python/r integration would be busted so that was a shock to find that both ran with out any lag.
What do you dislike about the product?
Those license prices are criminal for what you receive. Additionally, the “cas server” management of the product has been an absolute pain to try and figure out. I spent two hours just trying to understand how to use the sessions. There is no straight shot of getting help from the documentation or google.
What problems is the product solving and how is that benefiting you?
That saves me from having to load up heavy software onto my laptop because it loads into the browser. I am able to at least somewhat manage the inventory information without opening 10 different tabs.
The smoothest my big data work has ever felt
What do you like best about the product?
I mostly use the Databricks Data Intelligence Platform to mangle large datasets that we store across cloud buckets and create etl pipelines, as well as stand up notebooks on which I do a lot of explorative work. I very much like that everything feels ready to go such as clusters start quickly, scaling just works in the background and I can really stop worrying about infrastructure stuff and focus on analysis.
What do you dislike about the product?
The UI can feel slow especially when I’m deep in the middle of a heavy notebook session and sometimes things are just SLOW to click or jobs don’t cancel when I ask it to. And I’ve only just started messing around with the cluster software actually, and while it’s powerful there’s definitely a learning curve as I still dig through stuff on occasion trying to figure out which cluster or runtime setting is making things run differently than others.
What problems is the product solving and how is that benefiting you?
Databricks has, quite literally, taken the hassle out of dealing with big data infrastructure and instead of waiting for clusters to spin up and spending time hunting down why jobs failed, I can open a notebook and start working. That change alone has accelerated our etl development, lowered our cloud expenses significantly and made it far less risky to experiment with ml workflows without having to think about scaling.
Databricks review
What do you like best about the product?
What I like the most is the ease with which I can work with data from different sources in one place. Before, I had to jump between various tools, but here everything is integrated: from ingestion to advanced analysis.
What do you dislike about the product?
What I like least is that, at the beginning, the learning curve can be a bit steep.
What problems is the product solving and how is that benefiting you?
It has made it easier to reduce information processing times, which speeds up strategic decision-making, but a challenge is that with so many features, it can initially feel complex to navigate the platform and know exactly what to use.
Power of lakehouse to support AI
What do you like best about the product?
The Databricks Data Intelligence Platform is a unified, AI-native environment that brings together data engineering, analytics, governance, and machine learning on top of the Lakehouse architecture. Its strength lies in combining open data formats with centralized governance via Unity Catalog and embedding intelligence through DatabricksIQ, which allows enterprises to securely connect their data with large language models. From an evaluation standpoint, the platform’s value is in enabling organizations to not only manage and analyze data at scale but also to operationalize generative AI use cases in a governed and collaborative manner.
What do you dislike about the product?
Current stage of evolving technology may require rebuilding on future if adapted by enterprise
What problems is the product solving and how is that benefiting you?
Across industries, the platform solves problems like fraud detection, personalization, compliance, predictive analytics, and AI-driven customer engagement—all by combining data unification, governance, and machine learning in one environment.
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