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.

I use Databricks to run ingestion, transformation, and reconciliation pipelines for several clients’ data on one platform, and to prepare feature tables for fraud detection. The setup was smoother than I expected: my workspaces, cluster policies, and Unity Catalog were ready within the first sprint. Genie Code drafts and debugs my PySpark, so I can spend more time on edge cases than boilerplate, and it often reads a job’s stack trace faster than I do. It also helped me pinpoint a provider file-format mismatch, so I could add an expectation and get reconciliation reports to the client’s finance team on time. Separate workspaces keep client projects apart, cluster policies help prevent oversized clusters on client budgets, and Unity Catalog gives me one place to manage table permissions and trace lineage when a source change breaks something downstream. Databricks helps me fix schema changes and failed jobs quickly so clients receive reliable data without delays. Review collected by and hosted on G2.com.

My main reservation is the pipelines dashboard. Since I deliver for several clients, their pipelines end up in one flat list and blur together. Assigning permissions pipeline by pipeline is tedious and, with client data involved, leaves too much room for error. Folders with inherited access, tags for client and environment, filters, and a summary of failed or delayed runs would make it easier to check one client’s pipeline health at a glance. Review collected by and hosted on G2.com.

What I like most about Databricks is that it helps our team monitor rider service operations more efficiently. We use Databricks Dashboards and SQL Warehouses to track daily revenue, service requests, customer satisfaction scores, completed services, and failed transactions in one place. It has made operational reporting much faster and allows us to identify trends without manually preparing multiple reports. Review collected by and hosted on G2.com.

One thing I found slightly challenging with Databricks was understanding how different components such as SQL Warehouses, dashboards, and permissions work together when building operational reports. Setting up dashboards for rider service metrics took some time initially. After getting familiar with the workflow, it became much easier to manage, but better onboarding guidance for first-time users would improve the overall experience. Review collected by and hosted on G2.com.

We use Databricks to process booking and service data before creating operational dashboards for our team. I imported datasets into Delta tables, used SQL queries to prepare revenue and booking metrics, and built KPI dashboards to monitor provider performance and monthly trends. Catalog Explorer made it easier to inspect schemas and validate data before reporting. Keeping data preparation, querying, and visualization in one workspace reduced manual work and helped us generate reports faster. Review collected by and hosted on G2.com.

The main challenge was the initial learning curve. Understanding the workspace layout, Catalog Explorer, SQL Warehouses, and dashboard configuration took some time when I first started using it. After a few days of working with the platform, navigation became much more intuitive, but better onboarding for new users would make the experience smoother. Review collected by and hosted on G2.com.

I like Databricks' Unity Catalog, AI features, and governance features, which include data lineage, ABAC policies, AI agents, Genie, Genie code, and AI functions. These features simplify our work and are easy to configure and manage. I also switched to Databricks' workflows because of cost reduction and ease of maintenance and scaling. Plus, the initial setup was super easy. I'm really likely to recommend Databricks, and I gave it a 10 out of 10 on the recommendation scale. Review collected by and hosted on G2.com.

Databricks could use improvement in the frontend dashboard features to better align with other BI tools. Also, the UI for jobs, workflows, and pipelines is cumbersome, not very helpful, and difficult to navigate. Review collected by and hosted on G2.com.

I like Databricks for their ability to innovate quickly and deliver products to the market at a fast pace. The way they have developed products like Genie and agent bricks and continuously matured them is quite impressive. I appreciate how they integrate with other tools, especially in AI and BI dashboards. Building dashboards directly in Databricks instead of using external partners is helpful. The integration ecosystem with Unity catalog, which centralizes governance, is also impressive. Additionally, the support from Databricks during setup was smooth and the dedicated support team continues to be very helpful, especially in expanding our AI capabilities. Review collected by and hosted on G2.com.

Too many products to address similar problems at times creates confusion Review collected by and hosted on G2.com.

I like being able to integrate PySpark and natural language processing (NLP) models into a single workflow. It lets me query historical records with very high performance using SQL, while also running Python scripts to group results by keywords. Review collected by and hosted on G2.com.

Computing costs can be high, and cluster management expenses can escalate quickly if you don’t keep tight control over autoscaling and idle time. Review collected by and hosted on G2.com.

Databricks offers a versatile platform that streamlines data tasks, enabling the creation and implementation of machine learning models. It optimizes risk assessment, claims management, and fraud detection while ensuring efficient data processing and collaboration. The platform's integration with various data sources and robust visualization features supports informed decision-making and regulatory compliance. Review collected by and hosted on G2.com.

I use Databricks extensively because it's excellent software that allows me to create and implement machine learning models for optimizing risk assessment, claims management, and fraud detection. I really like its integration with different data sources to consolidate the information we use in decision-making. It helps me process data faster and more efficiently, which is essential given the volume and variety of data I handle. I appreciate how it facilitates collaboration between data teams and addresses data security management challenges with integrated tools ensuring regulatory compliance. I like the interface and its robust data visualization features, which make interpreting the results more accessible to all team members. My favorite aspect is its flexibility, allowing me to work with different programming languages and choose the best approach for each task based on team skills and project requirements. Also, the implementation was straightforward as it was a collaborative process involving several teams within the organization. Review collected by and hosted on G2.com.