DC Diana C. Lead Business Efficiency Architect Information Technology and Services Mid-Market (51-1000 emp.) 7/29/2026 Business partner of the seller or seller's competitor, not included in G2 scores. "Databricks Streamlines ETL and Analytics with Scalable Notebooks" 5/5 What do you like best about Databricks? 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. What do you dislike about Databricks? 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. What problems is Databricks solving and how is that benefiting you? Databricks has helped address one of the biggest challenges in our data engineering workflow: processing and transforming large volumes of data efficiently. Before the data reaches reporting or downstream applications, we need to ingest data from multiple sources, apply business rules, clean inconsistent records, and create curated datasets. Databricks provides a single platform where we can develop, test, and run these data pipelines using PySpark and SQL instead of managing multiple disconnected tools. This has made our development process more consistent and easier to maintain. A practical example is one of our daily ETL pipelines that processes data from different source systems before loading it into curated tables for reporting. We use Databricks notebooks during development to validate transformations on sample data and then schedule the same logic as production jobs. If a pipeline fails, the job history and execution logs help us identify the stage where the failure occurred, making troubleshooting more efficient than manually tracing scripts across different servers. Having notebooks, job scheduling, and cluster management in one platform has reduced the effort required to manage these workflows. From a business perspective, the biggest benefit is faster availability of reliable data for reporting and analytics. Our team spends less time managing infrastructure and more time implementing business logic and improving data quality. While optimizing Spark jobs and monitoring cluster costs still require attention, Databricks has streamlined our daily workflow by providing a scalable environment for developing, testing, and running data pipelines. This has improved collaboration within the team and made it easier to deliver data that downstream users can trust. Review collected by and hosted on G2.com. Show More Response from Jess Darnell of Databricks [Editedit](https://www.g2.com/survey_responses/databricks-review-13181721/official_response/edit) We're glad to hear that Databricks has streamlined your data engineering workflow and provided a single workspace for analytics and notebooks. We understand the importance of having a convenient environment for developing and testing transformations, and we appreciate your feedback on the cluster startup times and cost management. We're continuously working to improve the platform and provide better tools for managing compute resources and reducing operational overhead. Thank you for sharing your experience with Databricks! Current UserValidated ReviewerSource: Organic See what 1337 reviewers think of Databricks 4.6 out of 5 ยท Verified reviews from real users [ Read all reviews ](https://www.g2.com/products/databricks/reviews)