Databricks Data Intelligence Platform
Databricks, Inc.External reviews
692 reviews
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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 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.
Unified Data Engineering, Analytics, and ML on a Scalable Databricks Platform
What do you like best about the product?
What I like most about Databricks is how it brings data engineering, analytics, and machine learning together in one platform. It streamlines the entire data pipeline—from ingestion and transformation through to serving—so I don’t have to rely on multiple separate tools to get end-to-end workflows done.
Its integration with Spark and Delta Lake is another big plus, making it both scalable and dependable when working with large datasets.
Its integration with Spark and Delta Lake is another big plus, making it both scalable and dependable when working with large datasets.
What do you dislike about the product?
One challenge with Databricks is cost management and visibility. Since compute is abstracted through clusters and jobs, it can sometimes be difficult to track and optimize costs without additional monitoring or governance in place.
What problems is the product solving and how is that benefiting you?
Solves the problem of fragmented data ecosystems, where data engineering, analytics, and machine learning are handled in separate tools.
Databricks Brings Spark, Delta, and ML Together with Effortless Auto-Scaling
What do you like best about the product?
Databricks is hands down my favorite platform for data engineering because it brings everything together in one place Spark processing, Delta Lake, and ML tools all play nice without the usual headaches. The auto-scaling clusters save tons of time on big ETL jobs, like the SAP integrations I've done, letting me focus on logic instead of babysitting resources. Unity Catalog has been a game changer for governance in our lakehouse setups too.
What do you dislike about the product?
Costs can sneak up fast if you're not watching usage closely, especially with premium features on large pipelines. The notebooks are great for prototyping but get messy in production without strict discipline. Setup for advanced stuff like custom Unity Catalog policies sometimes feels overly complex for what it delivers.
What problems is the product solving and how is that benefiting you?
Databricks tackles key data engineering headaches like scaling massive Spark jobs, data quality issues, and siloed teams by providing a unified lakehouse platform with Delta Lake for ACID transactions and reliable pipelines. When I have a large number of files or tables to process like in supply chain ETL from SAP systems it shines with optimized Delta processing, serverless compute, and Photon engine, slashing run times from days to hours while cutting costs through auto-scaling. This benefits me directly by speeding up project delivery, reducing debugging time on failures, and enabling seamless collaboration with analysts on notebooks without tool switches.
Genie Code and Inline Assistant Dramatically Boosted My Debugging Productivity
What do you like best about the product?
Genie code and the inline Assistant were the most helpful tools for me on my project. They helped me debug a 2k-line codebase and clearly explained why I wasn’t getting accurate data. It also provided a query to run in my source system (SQLMI). By running the discrepancy script in parallel on the source and target, I was able to debug the entire code much faster and improve my productivity. Overall, it cut my work time from about 8 hours down to around 1 hour.
What do you dislike about the product?
In Delta Sharing, there isn’t a catalog-level SELECT permission, and I sometimes think having that would be helpful. Also, when I use the Genie code inside a VM, it can make the website unresponsive at times. These are areas that could be improved.
What problems is the product solving and how is that benefiting you?
In one of our claims-processing migration projects, the client needed near real-time data availability for downstream applications. Previously, the architecture used Amazon Redshift as the data warehouse, with Jasper and Sisense consuming the data for reporting and analytics. However, that setup didn’t support real-time or near real-time streaming efficiently, which led to delays in data availability for downstream systems.
After migrating the platform to Databricks, we were able to substantially improve the data pipeline architecture. We implemented streaming along with optimized ETL pipelines, reducing the data refresh cycle to about 30 minutes. We also created a dedicated view that retains data from the previous run, so downstream systems always have a consistent dataset available while the next pipeline execution is still in progress.
Before, we struggled with delayed refresh cycles and a limited ability to meet near real-time data needs in our Redshift-based architecture. After moving to Databricks, we enabled faster ETL processing and improved near real-time data availability.
As a result, we reduced ETL refresh time to roughly 30 minutes and enabled near real-time access for downstream tools like Jasper and Sisense. Reliability also improved because the stable view continues to serve the previous run’s data during pipeline updates. Finally, the overall architecture became simpler by consolidating processing and analytics capabilities within Databricks.
Overall, Databricks helped us build a more scalable and efficient near real-time data processing platform, significantly improving the timeliness and reliability of analytics for the claims-processing workflow.
After migrating the platform to Databricks, we were able to substantially improve the data pipeline architecture. We implemented streaming along with optimized ETL pipelines, reducing the data refresh cycle to about 30 minutes. We also created a dedicated view that retains data from the previous run, so downstream systems always have a consistent dataset available while the next pipeline execution is still in progress.
Before, we struggled with delayed refresh cycles and a limited ability to meet near real-time data needs in our Redshift-based architecture. After moving to Databricks, we enabled faster ETL processing and improved near real-time data availability.
As a result, we reduced ETL refresh time to roughly 30 minutes and enabled near real-time access for downstream tools like Jasper and Sisense. Reliability also improved because the stable view continues to serve the previous run’s data during pipeline updates. Finally, the overall architecture became simpler by consolidating processing and analytics capabilities within Databricks.
Overall, Databricks helped us build a more scalable and efficient near real-time data processing platform, significantly improving the timeliness and reliability of analytics for the claims-processing workflow.
A Unified Platform for Scalable Data & AI Workloads
What do you like best about the product?
Databricks is great because it brings everything you need for data and AI into one place.
Instead of switching between different tools for data engineering, data cleaning, analytics, and machine learning, you can do it all in a single environment. That makes life a lot easier.
Instead of switching between different tools for data engineering, data cleaning, analytics, and machine learning, you can do it all in a single environment. That makes life a lot easier.
What do you dislike about the product?
Databricks is not beginner-friendly. You often need solid data engineering skills to use it effectively.
Reviews point out that while Databricks is extremely capable, it’s “a high‑end workshop” that requires expertise and is not easy for less technical teams.Databricks uses cost units (DBUs), which many people find difficult to estimate and manage.
Even expert reviews highlight that its pricing is famously complicated and can hide unexpected costs.
Reviews point out that while Databricks is extremely capable, it’s “a high‑end workshop” that requires expertise and is not easy for less technical teams.Databricks uses cost units (DBUs), which many people find difficult to estimate and manage.
Even expert reviews highlight that its pricing is famously complicated and can hide unexpected costs.
What problems is the product solving and how is that benefiting you?
Databricks uses the Lakehouse architecture to combine the strengths of data lakes and data warehouses into one unified platform. This means structured and unstructured data live together and are ready for analytics or machine learning.
Databricks Keeps Removing Friction with Strong Governance and Intuitive AI Tools
What do you like best about the product?
What I like most about Databricks is how its features have consistently matched the evolving needs of engineering teams. Over the years, I’ve seen it grow from a solid data platform into a workspace that genuinely streamlines how we build and manage data and AI solutions. Unity Catalog has been one of the biggest improvements for us having a single place to manage permissions and lineage has removed a lot of manual steps we used to handle separately across systems. Genie AI and BI have also become part of my regular workflow; being able to generate SQL or explore datasets through natural conversations helps teams get to answers faster, especially when we’re under time pressure. The Apps capability has added unexpected value by letting us create and share simplified internal tools directly within the platform, eliminating the need to stand up extra infrastructure. And with Lakebase, we’ve been able to support more transactional-style use cases without losing the flexibility of a lake, which has made certain pipelines far easier to maintain. Altogether, these improvements have removed a lot of friction from day‑to‑day work and made the platform something I genuinely enjoy using as it continues to evolve.
What do you dislike about the product?
What I dislike about Databricks is that some of the newer AI experiences especially Genie for code generation can feel unstable at times and may lose context during longer development sessions. It disrupts my workflow when the assistant can’t retain earlier logic or maintain continuity across multiple iterations.
I’ve also noticed a gap in native connectors for certain enterprise systems like DFS, SMB shares or windows-based source systems, and platforms such as DB2 on AS/400, which many customers still rely on. Even though Databricks continues to expand its ecosystem, the lack of direct connectivity in these areas often means we need extra middleware or custom pipelines to bridge the gap.
None of these are deal-breakers, but they’re areas where the platform’s otherwise smooth experience can still feel a bit incomplete.
I’ve also noticed a gap in native connectors for certain enterprise systems like DFS, SMB shares or windows-based source systems, and platforms such as DB2 on AS/400, which many customers still rely on. Even though Databricks continues to expand its ecosystem, the lack of direct connectivity in these areas often means we need extra middleware or custom pipelines to bridge the gap.
None of these are deal-breakers, but they’re areas where the platform’s otherwise smooth experience can still feel a bit incomplete.
What problems is the product solving and how is that benefiting you?
Databricks has helped us address several long‑standing challenges in how we manage and deliver data and AI. Before adopting its newer capabilities, we were dealing with fragmented governance, duplicate datasets, and a lot of manual effort to keep permissions and lineage consistent across different systems. Unity Catalog improved this by giving us a single place to manage security and ownership, which reduced confusion across teams and noticeably cut down on rework during audits.
We also used to spend a significant amount of time helping teams explore data or draft queries. With Genie AI and BI, they can now generate SQL, summaries, and visual insights more independently. As a result, the time from a question to a usable answer has shortened, especially when we’re working under tight delivery cycles.
Another pain point was building small internal tools around our data. Setting up separate infrastructure or hosting environments created unnecessary overhead. With Databricks Apps, we can now build and share these tools within the platform itself, which saves setup time and reduces ongoing maintenance.
Finally, we struggled to support workloads that needed both the flexibility of a lake and the reliability of a database. Lakebase helped close that gap by enabling transactional‑style operations directly on our lake data, which simplified several pipelines and reduced the number of systems we have to maintain.
Overall, Databricks has moved us from juggling multiple disconnected tools to working in a more unified and predictable environment. That shift has sped up delivery, lowered operational overhead, and improved the clarity of our workflows.
We also used to spend a significant amount of time helping teams explore data or draft queries. With Genie AI and BI, they can now generate SQL, summaries, and visual insights more independently. As a result, the time from a question to a usable answer has shortened, especially when we’re working under tight delivery cycles.
Another pain point was building small internal tools around our data. Setting up separate infrastructure or hosting environments created unnecessary overhead. With Databricks Apps, we can now build and share these tools within the platform itself, which saves setup time and reduces ongoing maintenance.
Finally, we struggled to support workloads that needed both the flexibility of a lake and the reliability of a database. Lakebase helped close that gap by enabling transactional‑style operations directly on our lake data, which simplified several pipelines and reduced the number of systems we have to maintain.
Overall, Databricks has moved us from juggling multiple disconnected tools to working in a more unified and predictable environment. That shift has sped up delivery, lowered operational overhead, and improved the clarity of our workflows.
Unified Lakehouse Architecture for ETL, Analytics, and ML in One Stack
What do you like best about the product?
Unified lakehouse architecture: Databricks lets me treat my data lake more like a “lakehouse,” combining data-lake flexibility with data-warehouse-like features such as ACID transactions, schema enforcement, and time travel on Delta tables. As a result, I can handle ETL, ad hoc analytics, and ML on a single stack, rather than juggling separate warehouses, lakes, and Spark clusters.
What do you dislike about the product?
The platform can feel heavy and is sometimes slow, especially when working with large notebooks or running long jobs. Databricks can also be expensive to operate, particularly if clusters are left idle or aren’t well optimized.
What problems is the product solving and how is that benefiting you?
Faster, collaborative workflows
Databricks simplifies big-data complexity by abstracting much of the Spark and cluster management, so I can focus more on logic and less on infrastructure. The built-in notebooks, jobs, and versioning make it easy to prototype quickly, collaborate with analysts and DS, and move code from experimentation into production with less rework.
Unified platform for data and AI
Databricks reduces the need for separate data-lake, data-warehouse, and ML tools by providing a single lakehouse platform where you can store, transform, and analyze data, and run ML workloads in the same place. This helps cut down on tool sprawl and makes it easier to share data and models across engineering, analytics, and data science teams.
Databricks simplifies big-data complexity by abstracting much of the Spark and cluster management, so I can focus more on logic and less on infrastructure. The built-in notebooks, jobs, and versioning make it easy to prototype quickly, collaborate with analysts and DS, and move code from experimentation into production with less rework.
Unified platform for data and AI
Databricks reduces the need for separate data-lake, data-warehouse, and ML tools by providing a single lakehouse platform where you can store, transform, and analyze data, and run ML workloads in the same place. This helps cut down on tool sprawl and makes it easier to share data and models across engineering, analytics, and data science teams.
All-in-One Databricks Platform with Strong Governance, Fast Spark Performance, and Genie
What do you like best about the product?
The all-in-one platform eliminates tool sprawl. Unity Catalog gives you governance, lineage, and discoverability without bolting on a separate catalog. The notebook UI is clean and makes iterating on PySpark fast. Genie is the standout AI feature it turns curated tables into natural language interfaces for business users, and the SDK lets you configure it programmatically so it stays maintainable. DLT handles pipeline orchestration well. Performance on Spark workloads is solid, especially with Photon. Integrations with Airflow, S3, and the broader ecosystem are straightforward. For the ROI, consolidating what used to require multiple tools into one platform pays for itself in reduced complexity.
What do you dislike about the product?
Pricing can be hard to predict. Compute costs scale quickly if you're not careful with cluster sizing and SKU selection, and it's not always obvious which workload tier you actually need until you see the bill. The notebook IDE, while functional, still lags behind a real editor for refactoring, multi-file navigation, and code review workflows
What problems is the product solving and how is that benefiting you?
Tool consolidation is the biggest one. Before, you'd need separate systems for ingestion, transformation, warehousing, governance, and serving each with its own learning curve, maintenance overhead, and integration headaches. Databricks collapses that into a single platform. Unity Catalog solves the data governance problem by giving you lineage, access control, and discoverability in one place instead of managing permissions across disconnected systems.
Fast, Governed Self-Service Data Exploration with Databricks Genie
What do you like best about the product?
As a data engineer, I use Databricks Genie to interact with data in natural language, while still relying on the same governed tables, metrics, and semantic models that my team has built. Instead of jumping straight into SQL notebooks for every exploratory ask, I or business users can phrase questions in plain language and let Genie translate them into structured, catalog‑aware queries. This keeps self‑service fast but also secure and governed.
What do you dislike about the product?
Laptop stability when multitasking
My laptop can hang or become noticeably sluggish when I’m working with multiple Genie tabs and dashboards at the same time, especially during heavier queries or more demanding visualizations. This hurts the overall user experience and can slow down iterative development and analysis.
Latency with complex data models
With very wide schemas or more complex semantic models, Genie sometimes selects suboptimal joins or an overly broad/narrow level of granularity. As a result, I still need to review the generated SQL and optimize it myself. In that sense, it remains a helpful assistant rather than a fully autonomous query engine.
My laptop can hang or become noticeably sluggish when I’m working with multiple Genie tabs and dashboards at the same time, especially during heavier queries or more demanding visualizations. This hurts the overall user experience and can slow down iterative development and analysis.
Latency with complex data models
With very wide schemas or more complex semantic models, Genie sometimes selects suboptimal joins or an overly broad/narrow level of granularity. As a result, I still need to review the generated SQL and optimize it myself. In that sense, it remains a helpful assistant rather than a fully autonomous query engine.
What problems is the product solving and how is that benefiting you?
In a recent project, the business wanted to understand a decline in customer‑lifetime‑value (CLV) in a specific region. A product manager used Genie to explore CLV trends by region and cohort, excluding refunds, directly from an AI/BI dashboard. From that conversation, I captured the core logic, wrapped it into a Delta Live Table pipeline, and scheduled it as a recurring job. This reduced ad‑hoc requests by roughly 30–40% and enabled ongoing self‑serve access to CLV insights while I focused on tuning performance and data‑quality rules.
Overall, Genie helps me talk with my data in natural language, improves how quickly we uncover insights, and supports better data‑quality practices—though working across many Genie‑backed tabs can strain local hardware and sometimes slow down the workflow.
Overall, Genie helps me talk with my data in natural language, improves how quickly we uncover insights, and supports better data‑quality practices—though working across many Genie‑backed tabs can strain local hardware and sometimes slow down the workflow.
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