What do users say about Databricks?

As an Azure data engineer, I like Databricks because it gives me the flexibility to build data pipelines that can handle both structured and semi-structured data. I can use PySpark and Spark SQL to clean raw files, apply business rules, and prepare curated datasets for analytics. Working with Delta Lake is especially useful when managing incremental loads and maintaining reliable data across different processing stages. My favorite features are Delta Lake, PySpark notebooks, Spark SQL, workflow scheduling, and the medallion architecture. Delta Lake supports reliable table operations, and the workspace provides the best environment for developing and testing data pipelines. Notebooks make it easy to combine code, SQL, and output results in one place. Review collected by and hosted on G2.com.

Some configurations require a good understanding of compute resources, access permissions, and workspace settings. Troubleshooting slow jobs can also take time when data is skewed or transformations are not optimized. Monitoring compute usage is important because poorly managed workloads can increase cloud costs. Review collected by and hosted on G2.com.

What I like best about Databricks is its ability to bring data engineering, data analytics, and machine learning together in one platform. It makes it easier to process large datasets, build data pipelines, and collaborate using notebooks and SQL. I also like its integration with cloud platforms and its support for scalable data processing, which helps simplify complex data workflows. Review collected by and hosted on G2.com.

One thing I dislike about Databricks is that it can be challenging for beginners to understand because of its wide range of features and configuration options. Cluster setup and compute costs can also be difficult to manage if resources are not monitored properly. A simpler onboarding experience, clearer cost tracking, and more beginner-friendly guidance would make the platform easier to use. Review collected by and hosted on G2.com.

I like Databricks because it provides a flexible environment for building and maintaining data transformation pipelines at scale. I can use PySpark and Spark SQL to handle complex transformations, process large datasets, and implement incremental data loads. Delta Lake is particularly useful for managing reliable tables and handling updates without relying on multiple disconnected process tools. It helps ETL development by bringing data processing transformation and pipeline management into a unified workspace. I can consolidate data from multiple sources, apply business rules, perform data quality checks, and prepare curated datasets for downstream analytics. Its features like PySpark, Spark SQL, Delta Lake workflow scheduling, and Medallion architecture make Delta tables useful for reliable data updates. It provides a convenient way to develop, test, and ETL logic. Notebook supports collaboration, and job monitoring helps track pipeline executions. Its integration with cloud storage, orchestration tools, and data warehouse, combining Azure Data Factory, Azure Data Lake Storage, and Databricks, is useful for creating end-to-end ETL pipelines. Review collected by and hosted on G2.com.

It has a learning curve, especially when working with advanced cluster configuration, access control, and performance tuning. Troubleshooting inefficient Spark jobs can take time when dealing with skewed data or memory issues. Compute costs also require attention when workspaces are not configured efficiently. Review collected by and hosted on G2.com.

I bring campaign data from social platforms, client websites and online stores into one workspace, so I don’t have to rebuild the weekly attribution view across separate tools, and my figures match the account managers’ figures. Since we share a workspace, our web team usually catches broken tags before they distort a report. I value having analytics and machine learning together: I can write SQL, test a Python model and share the result with colleagues in the same notebook. Setup was simple, and our team was sharing notebooks soon after the first login. I also value the cluster settings, which let me size compute for a heavy year of ad impressions and keep the weekly attribution work manageable. Review collected by and hosted on G2.com.

Speed is the weak point for me. When I process a full year of ad impressions across several client accounts, queries slow down noticeably. I filter data early and right-size my clusters, which softens the problem, but I’d also like Databricks to suggest cluster sizes and flag slow queries automatically so I can fix slowdowns without trial and error. Review collected by and hosted on G2.com.

I like that Databricks lets me apply complex transformations and rely on a single source of truth. I value being able to share data with my team so they understand my business logic. Automatic scaling is valuable at the end of each quarter for in-depth customer churn and upsell analysis, and it helps high-volume processes run quickly without the system crashing. Setup was very easy in our environment. Review collected by and hosted on G2.com.

I think Databricks’ interface could be improved because it seems too focused on machine learning and generic analytics. I also have to keep a close eye on costs, especially toward the end of the billing cycle, because usage can increase significantly. Review collected by and hosted on G2.com.

The best thing that I admire about Databricks is the strong data expertise that it has provided us, also they have helped us to bridge the gap between our engineering and. ML along with BI tracks. It's has helped us to speed up the delivery timelines by 39.89% Also With the ETL pipelines has helped us to deploy without upfront infra provisioning. We have seen a change in collaboration since teams are able to collaborate better by 58% since it has vc integrations and multi language support such as (Pyspark, SQL & Scala). Review collected by and hosted on G2.com.

There are things which I feel that could be improved in Databricks, first thing is the consumption based model can escalate quickly and it is something not suitable for small and mid businesses. Second is learning curve it is hard to learn and it has complex architecture involved in it. Third, the integrations and dashboards can be better and more intuitive. Review collected by and hosted on G2.com.

I like that Databricks brings our finance exports, timesheets, pipeline, and Revit quantity schedules into one governed layer, so I can connect fee performance with resourcing without the weekly reconciliation chore, and our post-occupancy energy models use those same tables. Autoscaling on Delta Lake work is what sold me. With Photon, aggregations across years of project and utilization history run fast enough that I’ll actually iterate on a model instead of kicking it off and going to make coffee. Unity Catalog is the other piece I’d fight to keep: partners see exactly the tables they should and nothing else, and the lineage view helped settle a disagreement about a margin figure that had been going in circles for a fortnight. Review collected by and hosted on G2.com.

Git integration for notebooks leaves a good deal to be desired. When two of us touch the same notebook, merge conflicts take real effort to untangle, so I have taken to claiming files outright. I’d like cell-by-cell conflicts with a proper side-by-side diff and the option to resolve them in the workspace. Large result sets in workspace cells can also lock up the tab, so I sample before displaying anything substantial. Setup was steep at the start, mainly getting Unity Catalog structured properly. Review collected by and hosted on G2.com.

What I like most about Databricks is how its user friendly UI brings the entire data engineering workflow into one platform. In my project, data lands in the Bronze layer through snaplogic, and we use databricks to transform it into Silver and Gold data products. The serverless compute option has significantly reduced the effort of managing infrastructure, while Unity Catalog makes governance and access control straightforward. I also find AI Genie and the built-in monitoring features useful for investigating pipeline failures, checking job runtimes, and debugging issues much faster.From a pricing perspective, the pay-for-what-you-use model works well for us, especially with serverless compute and auto-scaling, as we avoid paying for idle infrastructure. The documentation is comprehensive, onboarding new team members is relatively straightforward, and there is a strong knowledge base and community that helps resolve issues quickly. Overall, it has made our ETL development and day 2 day operations more efficient. Review collected by and hosted on G2.com.

One downside is that troubleshooting can sometimes be difficult when a pipeline fails, as the error messages aren't always detailed enough and you may need to dig through multiple logs to find the root cause. The platform also has a lot of features, so it can take some time for new users to become comfortable with everything. While serverless is convenient, costs can increase if compute usage isn't monitored properly. I'd also like to see faster UI responsiveness in some areas, especially when navigating large job histories or catalogs. Review collected by and hosted on G2.com.

The best thing I like about Databricks is its overall end-to-end experience which makes it different from the other solutions. As a project manager, we had used Databricks in our organization for almost 3 years. In those 3 years, we have used it for its unified data lake for data warehouse. Also, it has helped us to reduce the time to market for our projects by almost 45%. With its unified environment, it has helped our teams to reduce friction and to move at a faster pace. Review collected by and hosted on G2.com.

There are no particular things that I dislike, but they may improve on auto-scaling compute since it can get small companies in trouble as it may create a budget overhead. Also, it is a code-heavy platform, which also means that non-tech users can't use it much. Review collected by and hosted on G2.com.

I've been using Databricks as part of our data engineering workflow to build and maintain ETL pipelines, analyze large datasets, and support reporting requirements. One of the things I like most is that it brings data engineering, analytics, and notebooks into a single workspace. Instead of switching between multiple tools, I can write PySpark code, validate transformations, collaborate with teammates, and schedule jobs from the same platform. This has made day-to-day development more organized, especially when working on multiple data pipelines.

Another feature I rely on frequently is the notebook environment. It's convenient for developing and testing transformations before moving them into production. During development, I often use notebooks to inspect sample data, troubleshoot failed transformations, and validate business logic with SQL and PySpark. The ability to mix code, markdown documentation, and query results in one place also makes it easier for team members to understand the implementation during code reviews or knowledge transfer sessions.

I also appreciate the platform's scalability. Some of our data processing jobs involve millions of records, and Databricks handles distributed processing efficiently without requiring us to manage the underlying infrastructure directly. Features like cluster management, job scheduling, and integration with cloud storage reduce operational overhead. That said, cluster startup times can occasionally delay quick debugging sessions, and managing compute resources carefully is important to avoid unnecessary costs. Overall, Databricks has helped simplify large scale data processing while giving enough flexibility for both development and production workloads. Review collected by and hosted on G2.com.

While Databricks has been reliable for our data engineering workloads, there are a few areas where I think it could be improved. One challenge I've experienced is cluster startup time. When I only need to test a small code change or validate a transformation, waiting for a cluster to start can interrupt the development flow. It's not a major issue for scheduled production jobs, but during active development and debugging, those extra minutes add up.

Another limitation is cost management. Since compute resources are tied to cluster usage, it's important to monitor cluster configurations and ensure they are shut down when not needed. We've had situations where development clusters remained active longer than expected, resulting in higher cloud costs. The platform provides tools to manage this, but it still requires teams to establish good governance and usage policies. I also found that some configuration settings for jobs, permissions, and clusters have a learning curve, especially for new team members who are unfamiliar with the Databricks environment.

From a day to day perspective, debugging distributed Spark jobs can sometimes be challenging. While the logs provide useful information, identifying the exact cause of failures often requires navigating through multiple execution logs and Spark UI details. For straightforward issues this isn't a problem, but troubleshooting more complex pipeline failures can take time. Despite these limitations, none of them outweigh the benefits of the platform, and most challenges can be managed with proper cluster configuration, monitoring, and team practices. Review collected by and hosted on G2.com.