Databricks Data Intelligence Platform
Databricks, Inc.External reviews
634 reviews
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External reviews are not included in the AWS star rating for the product.
It's super handy for analytics, scales well, and you can easily rely on it.
What do you like best about the product?
The best thing about Databricks is that it very easily consolidates data engineering, data science, and analytics – all in one place Therefore, I can process huge data sets quickly, run very complex machine learning operations, all without switching tools. Collaboration through notebooks with my team in real-time really reduced a lot of back and forth that I used to have.
What do you dislike about the product?
The big issue is pricing can quickly ramp up if I’m not careful with cluster size or if I forget to turn them off. I had to learn how to use some of the more advanced features, like Unity Catalog and MLflow connectors if I was to use them well. The only thing I would add is an easy interface, like in some BI products. It may be overwhelming for the newbie.
What problems is the product solving and how is that benefiting you?
I can construct fraud detection models, run queries over billions of rows, and test proof-of-concepts for clients all on one platform. This cost me days in setting work up. Huge workloads on peak hours will not slow me down as I can scale my computing power instantly. This is why I think Databricks gives me more freedom to play around with data models and machine learning at scale as compared to Snowflake or any other alternative.
Brings together data engineering, analytics & machine learning into a single integrated platform.
What do you like best about the product?
Databricks brings together data engineering, analytics, and machine learning into a single, integrated platform, reducing the need for separate tools and simplifying workflows.
What do you dislike about the product?
Some users find the platform challenging to learn, especially for those unfamiliar with distributed computing or specific Databricks features.
What problems is the product solving and how is that benefiting you?
Databricks reviews mention its Delta Lake architecture and governance features help ensure data reliability and security.
Great platform for big data and ML, with some learning curve
What do you like best about the product?
What I really like about Databricks is how it brings everything—data engineering, analytics, and machine learning—into one place. It saves a lot of time when switching between workflows. The collaborative notebooks are super handy when working with a team, and the Spark integration just works without much hassle. Delta Lake is also a plus—being able to manage large datasets with versioning and ACID support is honestly a lifesaver in production scenarios.
What do you dislike about the product?
The platform can feel a bit intimidating at first, especially if you're new to big data tools. Setting up clusters and understanding the pricing model took me a while. Also, the UI sometimes lags when you're dealing with large notebooks or switching between multiple tabs. I wish the onboarding was a bit more beginner-friendly
What problems is the product solving and how is that benefiting you?
Databricks helps us streamline our entire data pipeline — from ingestion to analytics to machine learning. Earlier, managing large-scale datasets and running ML models used to be fragmented across tools, but Databricks made it way smoother. It saves us a lot of dev time and reduces the friction between data engineering and data science teams. Having everything in one place also makes debugging and scaling much easier.
Best Collaborative platform for data engineer, analyst and scientists
What do you like best about the product?
Easy to use, it provides one under umbrella platfrom where different teams collborate their work together, which is very helpful for development and data sharing.
What do you dislike about the product?
as of now i dont find any issues, but we can improve on unity catalog side.
What problems is the product solving and how is that benefiting you?
We have different pipelines in databricks, we are utlisinf it for getting spark benifts and colloborative developement and data sharing between teams.
Worth the effort
What do you like best about the product?
Databricks excels at unifying data engineering, analytics, and machine learning into one seamless platform. What I like best is how effortlessly it handles massive data volumes while enabling collaborative development through notebooks. The integration with Apache Spark and the ability to run scalable workloads with ML, SQL, and Python side-by-side makes it a powerhouse for data-driven teams. Its governance and Delta Lake architecture also ensure reliability and security across the data pipeline.
What do you dislike about the product?
While Databricks is incredibly powerful, the learning curve can be steep for non-technical users or teams new to distributed computing. The UI, though functional, can sometimes feel a bit clunky compared to more modern data platforms. Additionally, managing costs in a multi-user environment requires careful governance, especially for teams running large-scale compute-heavy jobs.
What problems is the product solving and how is that benefiting you?
Databricks is helping us break down data silos by centralizing data engineering, analytics, and machine learning into a unified environment. It simplifies handling large datasets, automates ETL processes, and enables real-time analytics and AI-driven insights. As a result, we’ve significantly improved our data pipeline efficiency, reduced time to insights, and empowered both data scientists and analysts to collaborate more effectively using a single platform.
Unlocking Scalable Data Insights with Databricks
What do you like best about the product?
Databricks excels in unifying data engineering, analytics, and machine learning in a collaborative, cloud-based environment. Its support for multiple programming languages (Python, SQL, Scala, R) makes it incredibly flexible. The Lakehouse architecture simplifies data management by combining the best of data lakes and data warehouses. The auto-scaling compute clusters, tight integration with tools like MLflow, and powerful notebooks streamline experimentation and production deployment. I also appreciate the frequent product updates and commitment to open-source technologies like Apache Spark and Delta Lake.
What do you dislike about the product?
While powerful, Databricks has a learning curve—especially for non-technical users or those new to Spark-based architectures. Pricing can escalate quickly if not closely monitored, particularly with always-on clusters. The UI, although improving, still feels unintuitive in certain areas (like managing jobs or cluster permissions). Some integrations, especially with on-premise systems, require additional effort or custom workarounds.
What problems is the product solving and how is that benefiting you?
Databricks addresses the fragmentation between data engineering, data science, and analytics by offering a unified platform. Previously, we struggled with maintaining multiple disconnected tools for ETL, machine learning, and BI. Databricks' Lakehouse architecture allows us to manage structured and unstructured data in a single place, simplifying our data pipelines and reducing operational overhead.
It also improves collaboration across teams—data engineers, analysts, and data scientists can work together in shared notebooks with version control and built-in visualizations. With Delta Lake, we now have ACID-compliant data reliability and time-travel capabilities, which help ensure data quality and reproducibility.
As a result, project delivery times have decreased, and our ability to iterate quickly on models and reports has improved significantly—leading to faster business insights and better data-driven decision-making.
It also improves collaboration across teams—data engineers, analysts, and data scientists can work together in shared notebooks with version control and built-in visualizations. With Delta Lake, we now have ACID-compliant data reliability and time-travel capabilities, which help ensure data quality and reproducibility.
As a result, project delivery times have decreased, and our ability to iterate quickly on models and reports has improved significantly—leading to faster business insights and better data-driven decision-making.
Databricks unifies majority of engineering platforms.
What do you like best about the product?
There are various reasons why I like the Databricks Data Intelligence Platform.
We have been using Databricks as a Unified Platform for All of our Data Workloads (pipelines and models) that encompasses data engineering, data science, analytics and agentic AI.
We love the lakehouse architecture that assists in onboarding traditional data warehousing specialists to Databricks in a fast and scalable way.
We did Unity Catalog upgrade last year that streamlined governance and access control across assets; and did Serverless compute this year that decreased cluster starting/waiting time tremendously.
We have been playing around with Agentic AI space now where we build easy to understand prompts to train agents built on top of datasets. This helps in profiling/slicing/analyzing data by anyone (even a non-technical business person).
BI team has been using Databricks to connect with our Power BI dashboards while Engineering team has been using Databricks to connect with Airflow to create/visualize native DAGs.
Last but not the least, Databricks Notebook concept is awesome.
In the same Databricks notebook, one can have code in multiple languages (Python, Scala, SQL etc.) and each can be flipped at runtime.
Built-in collaborative notebooks with support for multiple languages (Python, Scala, SQL, R) and real-time co-authoring make it easier for teams to iterate together quickly.
We have been using Databricks as a Unified Platform for All of our Data Workloads (pipelines and models) that encompasses data engineering, data science, analytics and agentic AI.
We love the lakehouse architecture that assists in onboarding traditional data warehousing specialists to Databricks in a fast and scalable way.
We did Unity Catalog upgrade last year that streamlined governance and access control across assets; and did Serverless compute this year that decreased cluster starting/waiting time tremendously.
We have been playing around with Agentic AI space now where we build easy to understand prompts to train agents built on top of datasets. This helps in profiling/slicing/analyzing data by anyone (even a non-technical business person).
BI team has been using Databricks to connect with our Power BI dashboards while Engineering team has been using Databricks to connect with Airflow to create/visualize native DAGs.
Last but not the least, Databricks Notebook concept is awesome.
In the same Databricks notebook, one can have code in multiple languages (Python, Scala, SQL etc.) and each can be flipped at runtime.
Built-in collaborative notebooks with support for multiple languages (Python, Scala, SQL, R) and real-time co-authoring make it easier for teams to iterate together quickly.
What do you dislike about the product?
-- Unpredictable costs for small teams. We faced it when one of our agents AI workflow was running for a long time on a very small dataset.
-- Compute + storage based costing means optimization is critical. Every QTR, we have to really keep our Databricks forecasts updated as we don’t want a big deviation between forecast vs. actual.
-- While Databricks supports dashboards (with limitations), it can’t replace BI tools like Power BI, Tableau etc.
-- Compute + storage based costing means optimization is critical. Every QTR, we have to really keep our Databricks forecasts updated as we don’t want a big deviation between forecast vs. actual.
-- While Databricks supports dashboards (with limitations), it can’t replace BI tools like Power BI, Tableau etc.
What problems is the product solving and how is that benefiting you?
We have been playing around with Agentic AI space now where we build easy to understand prompts to train agents built on top of datasets. This helps in profiling/slicing/analyzing data by anyone (even a non-technical business person).
Business teams are also using Power BI dashboards (revenue, corporate accounting, HR, treasury and commerce) fed from Databricks data now.
Business teams are also using Power BI dashboards (revenue, corporate accounting, HR, treasury and commerce) fed from Databricks data now.
Make your Data solid BRICK with Databricks
What do you like best about the product?
What I like the best about Databricks data intelligence platforms is the uninterrupted integration of data engineering, data science and AI workflows on an single platform, which improves cooperation, robustness and performance for large data and ML projects.
What do you dislike about the product?
The lowest useful aspect of Databricks data intelligence platform is its complication for new users and steep learning state, especially for those without a strong background in spark or distributed computing. Additionally, it can be expensive on a scale, and debugging large workflows can sometimes be challenging due to limited transparency in error tracing.
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of silent data and fragmented devices by providing integrated environment for intelligence platform data engineering, analytics and machine learning. It enables rapid data processing, real -time cooperation, and streamlined workflows, eventually improves decision making and intensifies innovation in teams.
Databricks compute user
What do you like best about the product?
We have been using Databricks compute for our observability product. It’s super efficient and helps scale seamlessly with data
What do you dislike about the product?
Starting cost is high and makes the entry cost for our solution a little bigh
What problems is the product solving and how is that benefiting you?
Helps generate quality metrics
All under one hood
What do you like best about the product?
I like that everything you need is in the same platform.
What do you dislike about the product?
It is a bit too easy to overspend, need billing alerts in the platform.
What problems is the product solving and how is that benefiting you?
We are able to easily ingest, transform, display, and train on all our data, in one place.
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