Databricks brings data engineering, analytics, machine learning, and AI workflows together in one platform, making collaboration much easier across teams. I especially value the flexibility of working with SQL, Python, and notebooks in the same environment, along with the scalability of Spark for large datasets. Features like Delta Lake, Unity Catalog, and the growing AI capabilities have helped streamline development, improve governance, and accelerate time to insight. Review collected by and hosted on G2.com.

While Databricks is a very powerful platform, the learning curve can be steep for new users, especially those without a distributed computing background. Some features and UI experiences can feel fragmented or evolve quickly, making it difficult to keep up with best practices. Cost management and cluster optimization also require careful monitoring to avoid unexpected expenses, particularly for teams new to the platform. Review collected by and hosted on G2.com.

We use Databricks daily as our core data platform for building and running pipelines across a medallion architecture, from extracting data out of SAP and Arkieva all the way to reporting-ready datasets. The notebook experience is intuitive, the feature set is massive, and Asset Bundles have made our CI/CD story with Azure DevOps really solid. Integration with cloud services was smooth, and once things are set up they just work. The learning curve can be steep for newer team members, especially around things like Unity Catalog and DABs, and costs can creep up if you're not staying on top of cluster configurations. Support is decent and the docs are strong enough that we rarely need to open a ticket. Overall, it's a powerful platform that does a lot under one roof, and it's hard to imagine our data engineering workflow without it. Review collected by and hosted on G2.com.

The cost can creep up fast if you're not careful with cluster sizing and job configurations, so it takes some effort to keep things optimized. Also, the learning curve for newer team members can be steep, especially around things like Asset Bundles, Unity Catalog, and getting the CI/CD pieces wired up properly. Review collected by and hosted on G2.com.

Very innovative platform which provides ai capability to perform various data processing Review collected by and hosted on G2.com.

The Terraform provider is the biggest one. The account-vs-workspace provider split, the alias chains, and the fact that genuinely important operations aren’t first-class resources — so you end up dropping to terraform_data + local-exec curl against the Accounts API for things like PE approval or NCC bootstrap. That’s not declarative infra, that’s a shell script wearing an HCL costume. Add in the periodic provider bugs (the network policy resources have been rough) and state drift on resources that should be stable, and a lot of “Databricks on Terraform” is really “Databricks despite Terraform.” Review collected by and hosted on G2.com.

What I like most about Databricks is how it streamlines the entire data workflow by bringing processing, analysis, and machine learning into one platform. The collaborative notebook environment makes it easy to share code, context, and reasoning with teammates, which helps everyone stay aligned. It also performs strongly on large datasets while abstracting away most of the cluster management, so I can focus on solving the problem rather than dealing with infrastructure. On top of that, centralized access control and clear visibility into data usage support responsible data governance, offering a solid balance between power and ease of use. Review collected by and hosted on G2.com.

Databricks has a few downsides, although many of them feel more like trade-offs than outright negatives. My biggest concern is cost: if clusters aren’t managed carefully, expenses can climb quickly, even though the platform can scale very efficiently when it’s tuned properly. There’s also a real learning curve with Spark and distributed computing concepts, and debugging or performance tuning can be more involved than with simpler tools. Lastly, because it’s a managed service, you give up some low-level control compared with self-hosted systems, but the upside is that it takes a lot of the operational and infrastructure work off your plate. Review collected by and hosted on G2.com.

I use Databricks to import all of our SQL server data and transform it to the medallion layers for bronze, silver, and gold, which I find very intuitive. It allows us to create our BI reports using Power BI and utilize the Unity Catalog for governance. I appreciate the different sections and layers it provides. The data lake pipelines are something I use every day, and I like that I'm able to deploy all of our pipelines using code. Our management saw Databricks as a very robust platform and ecosystem, allowing us to build and expand faster. Review collected by and hosted on G2.com.

I think something that could be improved is having a governance dashboard or panel where we can control specific access to features for our developers. I'm a workspace admin and sometimes find it hard to control what new features Databricks releases and how available those features should be for specific sets of our data engineers or business analysts. I don't think there's a method for an admin to control those settings right now. Review collected by and hosted on G2.com.

Scalability for big data workloads.

Unified analytics and AI platform.

Delta Lake's reliability and performance.

Strong integration with cloud platforms like aws.amazon.com⁠�, azure.microsoft.com⁠�, and cloud.google.com⁠�.

Collaborative notebooks and workflow automation. Review collected by and hosted on G2.com.

One challenge I see with Databricks is that the platform evolves very quickly. New features, AI capabilities, and product enhancements are released frequently, which can create a steep learning curve for both customers and employees. However, I actually view this as a positive challenge because it reflects Databricks' commitment to innovation. I enjoy learning new technologies, so working in an environment that is constantly pushing the boundaries of data and AI would be motivating for me. Review collected by and hosted on G2.com.

De integration and ability to work with data under very different aspects, influences under a single unified platform, the performance and how easy is to connect to complex data sites and organize data. Extracting value from the data is also one of the things that I love Data Bricks made possible that huge volumes of data that companies that didn’t have the technology to achieve could handle this data in a very short period reducing complexity, and the data democratization Review collected by and hosted on G2.com.

The platform is becoming very crowded with features and products, and that sometimes are not yet fully operational or fully integrated. There are limitations in the newer products that Mia’s customer expected that they they would be they would have same features as previous product and capacity to customize and govern things in an easier way for instance, unity catalogues is too very complex to deploy integrate and it’s not in embedded or native to other product integrations Review collected by and hosted on G2.com.

I like Databricks because it's versatile and offers all the possibilities with pretty low effort, especially for data scientists. I'm impressed by how quickly the auto scale and infrastructure can be set up to achieve great outcomes. It's amazing how it simplifies everything from collecting and storing tabular customer data to analyzing and training models, leading to real impacts on optimizing supply chains. The Genie integration makes it easy to create dashboards in seconds using natural language. I really appreciate the availability of compute resources, and I use Genie code, Databricks Notebooks, and dashboards frequently. Databricks has been reliable throughout my experience, and it helps bridge gaps in a small workforce effectively. Review collected by and hosted on G2.com.

I hope that they continue to offer connectivity with Claude Code. And, just continue to expand the tool kits that they offer in terms of cloud skills to empower people who are a bit more technical. Review collected by and hosted on G2.com.

I appreciate that Databricks is very organized and offers a convenient UC for data storage and script writing capabilities. The ease with which I can update tables in UC and enable permissions is a big plus. I find model storing and deploying super convenient and structured, making it easy to select any preexisting model or one I've created and share it with users in apps or other services. It's also great that I can do a lot within just one workspace. The initial setup seems easy, which adds to the overall experience. Review collected by and hosted on G2.com.

I think copilot features could be smoother. Currently, there is no claude code integration unless working through an IDE and enabling Databricks connect. If going through Databricks, genie doesn’t provide the best results. Review collected by and hosted on G2.com.

I like the scalability aspect of Databricks, where I can tailor the back end based on the data volume I'm working with, and even right size it if the volume isn't large. The efficiency and the speed of the product also stand out to me, speaking volumes about what it offers. Additionally, Databricks' user-friendly interface makes coding and managing errors easier. The installation from a package standpoint is relatively straightforward and easy to use, which helps me understand what I need to do. Review collected by and hosted on G2.com.

I think I'd like to learn a little bit more about the BI capabilities. In terms of once you run your production pipeline or process, how can I initiate directly sort of a report? That can give me more analytics in terms of the quality of the data and also trending of certain key elements that are key or integral to the data that we are trying to create. Review collected by and hosted on G2.com.