Databricks Data Intelligence Platform
Databricks, Inc.External reviews
768 reviews
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External reviews are not included in the AWS star rating for the product.
Scalable, Unified Platform with a Steep Learning Curve
What do you like best about the product?
I use Databricks for my office projects, and I really like its ability to unify the entire data workflow in a single platform. It lets me seamlessly collaborate with data scientists and analysts, making it easy to ingest, clean, analyze, and model data. I appreciate its scalability and automation features, which save me time and reduce complexity when working with large datasets. I also like that it offers a scalable compute and storage solution, reducing infrastructure management overhead. The integration of shared notebooks and tools like Databricks Genie helps improve collaboration and speed up development.
What do you dislike about the product?
I haven't faced major issues with Databricks itself, but during my initial phase of using the platform, it wasn't very easy to get up to speed with all the features, tools, and configurations. Databricks evolves quickly and in the beginning, it was a bit challenging to match the pace of updates and fully leverage all its capabilities. The initial setup was moderately challenging. While the platform is well documented and user-friendly, getting familiar with all the features, configuring clusters and integrating it with our existing workflows required some learning and experimentation.
What problems is the product solving and how is that benefiting you?
Databricks solves scalability and performance issues, centralizing data from multiple sources and reducing silos. It simplifies collaboration among data professionals, offers scalable compute, and integrates advanced analytics, saving time and reducing complexity with large datasets.
Versatile Data Platform with Seamless Integration
What do you like best about the product?
What I like most about Databricks is that it's integratable with other platforms. I can literally set up a Databricks workspace using Azure data services from the Azure portal, and I can also use Databricks within AWS. It gives me the opportunity to integrate my Databricks notebooks into other environments and orchestration tools or ETL tools, like Azure Data Factory.
What do you dislike about the product?
For now, I noticed when I'm using Azure Databricks, particularly the Azure Databricks cluster, it usually times out, and it's kind of frustrating for me. Most times when I'm working, I just go into another tab. Every time I come back in a minute or two, it's timed out, and I have to sign in again. That experience can be frustrating. I would like that to be looked into. I don't know if it's an issue with Databricks or if it's an issue from the Azure side from the intraident authentication part of things.
What problems is the product solving and how is that benefiting you?
I use Databricks to unify my data by managing governance within the Unity catalog, simplifying user access and report sharing.
Databricks Makes Collaboration and Reliable Data Pipelines Easy
What do you like best about the product?
I really enjoy working in the Databricks environment because it makes it easy to collaborate with others through shared notebooks. Delta Lake technology has also been great for ensuring data quality and reliability across our pipelines. It lets us manage data, build pipelines, and run AI/BI workloads all in one place.
What do you dislike about the product?
The interface is quite laggy at times, especially when I’m scrolling through a notebook or spinning up a cluster.
What problems is the product solving and how is that benefiting you?
Because it’s a unified, end-to-end platform covering everything from data ingestion and transformation to AI and BI insights, it enables faster analysis and helps convert complex datasets into actionable decisions more efficiently
Robust Infrastructure and Easy Setup
What do you like best about the product?
I think the infrastructure in Databricks is really useful, especially its facilities and usage for an admin handling workspaces and client issues. It was easy to use token access, which made setup possible and fairly simple. I also appreciate how daily use in my community involves about 120-200 people. I find Databricks' infrastructure valuable in supporting my role effectively.
What do you dislike about the product?
I think it would be better if Databricks could send people alerts when any new features come to the market.
What problems is the product solving and how is that benefiting you?
I use Databricks to manage workspaces and client issues efficiently. It's easy to use, especially with token access management, facilitating smooth operations. The infrastructure and features make handling queries and workspace access straightforward, improving overall administration.
Databricks Genie and AgentBricks Make “Talk to Data” Easy
What do you like best about the product?
In databricks I like genie and agentbricks that help me to solve business process as talk to data
What do you dislike about the product?
I think all the functionality works as expected for me.
What problems is the product solving and how is that benefiting you?
It’s mainly about giving my business users more flexibility to talk directly to the data and run their own analysis without needing to know SQL.
Databricks Saves Time with Smooth, High-Performance Data Pipelines
What do you like best about the product?
Databricks saves time by automating data pipelines, improving performance, and reducing infrastructure management.
Overall, it provides a smooth experience for building, analyzing, and deploying data solutions.
Overall, it provides a smooth experience for building, analyzing, and deploying data solutions.
What do you dislike about the product?
Databricks provides strong capabilities for large‑scale data processing and collaboration, but there are areas for improvement.
What problems is the product solving and how is that benefiting you?
We use Databricks for building and managing large‑scale data pipelines and analytics workloads.
It helps us process high‑volume data faster by using scalable Spark clusters and automated workflows.
It helps us process high‑volume data faster by using scalable Spark clusters and automated workflows.
Databricks’ Unified Platform: Fast SQL, Streamlined Pipelines, and Context-Aware AI
What do you like best about the product?
The unified platform experience is what keeps me on Databricks. Having notebooks, pipelines, SQL warehouses, ML, and governance all in one place under Unity Catalog means I’m not constantly stitching together five different tools just to get work done.
Lakeflow Pipelines (formerly DLT) makes it straightforward to build medallion-architecture pipelines, and the Photon engine delivers real performance gains on SQL workloads without requiring any code changes. Recent additions like Genie Code and background agents also show they’re serious about agentic AI—it doesn’t feel like a bolt-on copilot, because it can actually understand your data context through Unity Catalog. Serverless compute has been another big quality-of-life improvement as well, since I no longer have to wait for cluster spin-up when I just want to run quick, ad hoc queries.
Lakeflow Pipelines (formerly DLT) makes it straightforward to build medallion-architecture pipelines, and the Photon engine delivers real performance gains on SQL workloads without requiring any code changes. Recent additions like Genie Code and background agents also show they’re serious about agentic AI—it doesn’t feel like a bolt-on copilot, because it can actually understand your data context through Unity Catalog. Serverless compute has been another big quality-of-life improvement as well, since I no longer have to wait for cluster spin-up when I just want to run quick, ad hoc queries.
What do you dislike about the product?
Cost management can be tricky—DBUs add up quickly if you’re not careful with cluster sizing and warehouse auto-scaling. The pricing model also isn’t always transparent, especially when you’re mixing serverless and classic compute.
Unity Catalog is powerful, but the initial setup and the migration from legacy HMS can be painful, particularly for large orgs with years of existing Hive metastore objects. The documentation is generally good, yet it sometimes lags behind new feature releases. On top of that, the workspace UI can feel sluggish at times, especially when you’re working with a large number of assets.
Unity Catalog is powerful, but the initial setup and the migration from legacy HMS can be painful, particularly for large orgs with years of existing Hive metastore objects. The documentation is generally good, yet it sometimes lags behind new feature releases. On top of that, the workspace UI can feel sluggish at times, especially when you’re working with a large number of assets.
What problems is the product solving and how is that benefiting you?
Before Databricks, our data stack was fragmented — separate tools for ETL, analytics, ML, and governance. That meant constant context-switching, duplicated data, and governance gaps. Databricks consolidates all of that into one lakehouse platform. Delta Lake gives us reliable ACID transactions on the data lake, Unity Catalog handles lineage and access control across the board, and SQL warehouses let our analysts self-serve without needing a separate data warehouse product. It's cut our pipeline development time significantly and made data governance something we can actually enforce consistently instead of hoping for the best.
Databricks Genie A/BI and Genie Code: Amazing Features on My Favorite Platform
What do you like best about the product?
I think almost all the features, being MVP Databricks, are always my favourite platform. If one object I pick, then its Databricks Genie A/BI and Genie Code, so its genie ...... genie.... really amazing name and amazing feature.
What do you dislike about the product?
The Databricks team is not hiring me. I am one of the great, great fans of databricks.
What problems is the product solving and how is that benefiting you?
More visibility, making things easier and easier, data access is not a challenge for anyone.
Unified Lakehouse Powerhouse: Fast, Scalable Analytics in One Databricks Workspace
What do you like best about the product?
What I like best about Databricks is the unified lakehouse platform. Everything—ingestion with Auto Loader/Lakeflow, Delta Live Tables pipelines, Spark transformations, SQL analytics, MLflow experiments, and governance via Unity Catalog—lives in one workspace. No more tool sprawl. Delta Lake delivers reliable ACID transactions, time travel, and schema evolution on massive datasets, while Photon makes queries fly. Serverless compute simplifies scaling, and collaboration in notebooks/repos is seamless for data teams.
What do you dislike about the product?
What I dislike most is the cost. It can spike quickly with poorly tuned jobs, forgotten clusters, or over-provisioning—DBU pricing adds up fast even with optimizations. Cold starts on interactive clusters slow quick prototyping, and it's overkill (and expensive) for tiny datasets or simple queries. The Spark/Delta learning curve is steep for newcomers, and heavy use creates some vendor-specific lock-in.
What problems is the product solving and how is that benefiting you?
Databricks solves data silos, unreliable lakes, and fragmented tooling by providing a governed lakehouse where raw data becomes clean, queryable assets for BI and AI. This benefits me by cutting infra firefighting so I focus on pipelines and quality; for the business, it means faster insights, better data reliability, easier AI adoption, and less tool sprawl—delivering real value from petabyte-scale data without constant re-architecture. (248 chars)
Databricks Unifies Data Engineering, Science, and Analytics Exceptionally Well
What do you like best about the product?
The ability to converge data engineering, data science, and analytics on a single platform without compromising on governance, performance, or flexibility is still rare in the industry. Databricks executes this exceptionally well.
What do you dislike about the product?
Reducing the spinning time of all purpose clusters and job clusters. It would be more usefula nd helpful if it starts as quick as serverless
What problems is the product solving and how is that benefiting you?
In enterprise banking, where regulatory compliance, data accuracy, and operational resilience are non-negotiable, Databricks is solving some of our most critical challenges. As a Lead Data Engineer managing end-to-end ETL pipelines, dashboard delivery, monitoring alerts, and data governance for a major banking client, the platform has become the backbone of our modern data architecture. Databricks unifies our fragmented data landscape through Delta Lake and Unity Catalog, giving us ACID-compliant transactions for reliable ETL, automated lineage for audit-ready governance, and fine-grained access controls to protect sensitive PII and financial data—all while enabling seamless schema evolution to handle the constant changes in source systems. This directly translates to faster, more trustworthy reporting: our dashboards in Power BI and Tableau now pull from a single source of truth, eliminating metric disputes between Risk, Finance, and Compliance teams. On the operational side, native alerting integrated with Slack and PagerDuty, combined with Databricks System Tables for observability, lets us proactively catch data quality issues or SLA breaches before they impact business decisions—reducing incident resolution time by over 60%. Ultimately, Databricks isn't just improving our engineering efficiency; it's enabling us to innovate responsibly in a highly regulated environment, delivering trusted insights at scale while keeping auditors confident and stakeholders aligned.
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