What do users say?

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.

I use Databricks to transform and analyze large volumes of data, and it allows me to unify data from various SQL functions to cloud storage systems, which makes analysis easier. I find eliminating information silos vital for team collaboration and appreciate the optimized Spark engine for its performance with large volumes of data. I appreciate its compatibility with languages like Python, Scala, and SQL, making it comfortable for every team member to work with Databricks. It fosters smoother communication and allows for making immediate adjustments, which is something I value. Additionally, I like how it facilitates collaboration between different teams. Review collected by and hosted on G2.com.

I would appreciate specific training resources to help me become familiar with the advanced features. Also, I found the initial setup to be a little difficult. Review collected by and hosted on G2.com.

I love how Databricks processes information at scale and orchestrates large data flows effectively. It breaks down barriers caused by data silos, making it a single source of reliable information, which is a huge relief for me. I like that it eliminates a lot of friction when connecting microservices or APIs to large flows of processed information. What I value most is the centralized governance. Review collected by and hosted on G2.com.

I find it problematic to configure certain network permissions and connectivity between services. I also believe that cost reductions and better infrastructure management would be beneficial. Honestly, I found the implementation a bit complicated, but it was worth it. Review collected by and hosted on G2.com.

What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments. Review collected by and hosted on G2.com.

One downside is that the initial setup and networking configuration can be complex and require technical expertise. Also, the cost can scale up quickly depending on usage, so cost monitoring is essential. Additionally, the lack of comprehensive documentation in some languages like Japanese can be a limitation. Review collected by and hosted on G2.com.

What I personally liked most was how easy it became to handle large-scale data workflows in one place. Earlier we were using separate tools for processing, notebooks, and collaboration which became messy very fast. With Databricks, the team could collaborate on notebooks, run pipelines, and experiment with ML models without constantly switching environments. Review collected by and hosted on G2.com.

The pricing can become expensive if clusters are not managed properly, especially for smaller teams or startups. There’s definitely a learning curve as well if someone is coming from traditional SQL-only environments. Review collected by and hosted on G2.com.