Databricks Data Intelligence Platform Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I find the Databricks Data Intelligence Platform fairly easy to use and set up. The initial setup was pretty seamless and easy, which I appreciate. Review collected by and hosted on G2.com.

I don't like that we got really screwed on pricing. Their pricing setup seemed to be based on assumptions that were completely wrong. Their calculator for consumption was way off, like 10x off, and when we entered into the contract, they didn't offer a good remedy for how far off it was. This has made me consider going back to Snowflake because of how poorly this was handled. Plus, their calculator just isn't correct, so be careful with it. Review collected by and hosted on G2.com.

I like that it's fast, and excellent all-in-one analytics solution, with excellent scalability that reduces time to market. The user interface is intuitive and perfect for users with varying skill levels. I also like that the database has the ability to terminate or time out instances, which helps us manage costs. Review collected by and hosted on G2.com.

I have no complaints about this tool; it allows us to run code efficiently without getting bogged down by infrastructure or optimization concerns. Additionally, there is a wealth of helpful training resources available for both developers and data scientists. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I like that Databricks provides a unified environment for data engineering, analytics, and machine learning. The platform makes it easy to collaborate across teams, manage large-scale data efficiently, and build advanced AI models using the same infrastructure. The integration with major cloud providers and the Lakehouse architecture make data management both flexible and scalable. Review collected by and hosted on G2.com.

While Databricks is a powerful and flexible platform, it can be complex to set up and manage, especially for teams without strong data engineering expertise. The cost structure can also become expensive for continuous workloads, and performance tuning sometimes requires deep knowledge of Spark and cluster optimization. Additionally, the user interface could be more intuitive for non-technical users. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

-- 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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The cost of the cluster we use is somewhat high, which restricts the amount of code we are able to run and execute. Review collected by and hosted on G2.com.

What I really like about the Databricks Data Intelligence Platform is how it brings everything together in one place. Instead of juggling different tools for data engineering, analytics, machine learning, and governance, you can do it all in a single environment. Review collected by and hosted on G2.com.

Honestly, what I find a bit frustrating about the Databricks Data Intelligence Platform is that while it’s incredibly powerful, it can also feel overwhelming at times. There’s a steep learning curve, especially for teams who are just getting started and don’t have much experience with Spark or distributed systems. Review collected by and hosted on G2.com.