Databricks Data Intelligence Platform Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Databricks has made working with massive datasets so much easier for our team. The collaborative notebooks help us share ideas and troubleshoot together, and the platform’s ability to scale means we don’t have to worry about hitting limits. It’s sped up our analytics and machine learning projects, and connecting to different data sources is a breeze. Review collected by and hosted on G2.com.

The initial setup was a bit confusing, and some of the advanced features could use better documentation. Figuring out the pricing took some time, but once we got going, the benefits were clear. Review collected by and hosted on G2.com.

I am using data bricks from around 1 year. In a week I use it approx 3-4 days. Everything is integrated, which means I don’t have to switch between multiple tools to do different tasks. It really improves team collaboration. Sharing notebooks and collaborating on models is super easy. This has been great for our team since we often work together on projects and need to see each other's code and progress. Review collected by and hosted on G2.com.

This is probably the biggest downside. Databricks can get pretty expensive, especially if you’re not monitoring your usage carefully. It’s definitely a platform that’s best suited for teams with a larger budget or organizations that need heavy-duty data processing. Review collected by and hosted on G2.com.

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.

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.