Databricks Data Intelligence Platform
Databricks, Inc.External reviews
763 reviews
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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.
Databricks: A Unified, Scalable Platform for Faster Collaboration and Innovation
What do you like best about the product?
Databricks stands out because it provides a unified platform that seamlessly combines data engineering, machine learning, and analytics, making collaboration across teams much easier. I especially appreciate how it simplifies working with big data by integrating with popular tools like Apache Spark, offering scalability, and enabling faster experimentation. The collaborative notebooks, strong support for multiple programming languages, and built-in security features make it both powerful and user-friendly. Overall, it helps accelerate innovation by reducing complexity and improving productivity across the entire data lifecycle.
What do you dislike about the product?
One drawback of Databricks is that it can feel overwhelming for new users because of its complexity and steep learning curve. The platform offers a wide range of powerful features, but navigating them effectively often requires significant technical expertise. Additionally, costs can escalate quickly if clusters are not managed carefully, and performance tuning sometimes demands deep knowledge of Spark internals. Integration with certain external tools can also be less seamless compared to other platforms.
What problems is the product solving and how is that benefiting you?
Databricks is solving the challenge of managing and analyzing massive amounts of data by providing a unified platform for data engineering, machine learning, and analytics. It eliminates the need to juggle multiple tools, making workflows more streamlined and collaborative. For me, this means faster access to insights, easier experimentation with models, and reduced complexity in handling big data. The benefit is clear: improved productivity, better collaboration across teams, and quicker decision-making powered by reliable data.
Databricks: Feature-Rich, User-Friendly, and Keeps Everything in One Platform
What do you like best about the product?
Among the various platforms I’ve worked with, Databricks stands out as a genuinely cohesive environment. It feels less like a bundle of disconnected features and more like a unified workspace—one that can evolve alongside the teams using it. The interface is intuitive enough to lower the barrier to entry, while still delivering the depth and power needed for heavy-duty engineering.
One of its biggest strengths is how it consolidates the data lifecycle. By bringing engineering, data science, and SQL analytics under one roof, it helps dissolve the silos that often lead to “data drift” and miscommunication between departments. In practice, it also simplifies the underlying infrastructure, replacing a dozen specialized (and sometimes conflicting) tools with a single, clearer source of truth.
Beyond simply “keeping things clean,” the platform also shines when it comes to collaborative transparency. With notebooks and experiments shared in real time, the gap between an initial data idea and a production-ready model can be dramatically shortened. On top of that, its commitment to open standards like Delta Lake means you’re not boxed into a proprietary black box—you’re building on a foundation that aligns with the broader data community’s direction. Overall, it strikes a rare balance: a polished, user-friendly wrapper around some of the most powerful distributed computing engines available today.
One of its biggest strengths is how it consolidates the data lifecycle. By bringing engineering, data science, and SQL analytics under one roof, it helps dissolve the silos that often lead to “data drift” and miscommunication between departments. In practice, it also simplifies the underlying infrastructure, replacing a dozen specialized (and sometimes conflicting) tools with a single, clearer source of truth.
Beyond simply “keeping things clean,” the platform also shines when it comes to collaborative transparency. With notebooks and experiments shared in real time, the gap between an initial data idea and a production-ready model can be dramatically shortened. On top of that, its commitment to open standards like Delta Lake means you’re not boxed into a proprietary black box—you’re building on a foundation that aligns with the broader data community’s direction. Overall, it strikes a rare balance: a polished, user-friendly wrapper around some of the most powerful distributed computing engines available today.
What do you dislike about the product?
The “Big Task” Breakdown
When Genie processes a large volume of data, it often ends up sending a huge amount of JSON back to the browser so it can render those tables and visualizations.
Memory overload: Browsers (and especially Chrome) can be real memory hogs. If a Genie response includes a very large result set or a massive execution plan, RAM usage can spike quickly, which can lead to that familiar “Not Responding” hang.
The “DOM” lag: Every row in a table and every line of code becomes an element the browser has to keep track of. As you scroll or type, the browser has to repaint thousands of these elements. When the task is too large, the browser’s main thread can get tied up rendering, and your typing starts to feel like it’s trailing behind by a few seconds.
When Genie processes a large volume of data, it often ends up sending a huge amount of JSON back to the browser so it can render those tables and visualizations.
Memory overload: Browsers (and especially Chrome) can be real memory hogs. If a Genie response includes a very large result set or a massive execution plan, RAM usage can spike quickly, which can lead to that familiar “Not Responding” hang.
The “DOM” lag: Every row in a table and every line of code becomes an element the browser has to keep track of. As you scroll or type, the browser has to repaint thousands of these elements. When the task is too large, the browser’s main thread can get tied up rendering, and your typing starts to feel like it’s trailing behind by a few seconds.
What problems is the product solving and how is that benefiting you?
You’ve nailed the core reason Databricks is winning over so many data teams: they’re reducing the “integration tax.” In most companies, you can easily lose around 30% of your time just moving data between the “storage” tool, the “processing” tool, and the “BI” tool.
The AI/BI Dashboard is a great example of this broader shift—from a “collection of tools” to a more unified platform.
What began as a basic visualization layer has evolved into a “Compound AI” system. Here’s how it has become so useful:
The “Ask Genie” integration: You’re no longer limited to staring at a static chart. As of 2026, every published dashboard includes an “Ask Genie” button by default. If a stakeholder notices a spike in a line chart, they don’t have to call you; they can right-click the chart and ask, “Genie, why did this drop on Tuesday?” and it will use Agent mode to track down the driver.
Direct-to-warehouse speed: Because it lives inside Databricks, there’s no need to “extract” data to a separate BI server. It queries the data where it already lives (Unity Catalog), which means the dashboard stays as fresh as your last ETL run.
AI-assisted authoring: You can build entire widgets just by describing what you want. Instead of dragging fields around, you can type, “Show me a funnel chart of our sales conversion by region,” and it generates the SQL and the visualization for you.
Deep governance: Since it’s built in, your security policies (row-level security, tags) follow the data automatically. You don’t have to recreate permissions in a separate tool like Tableau or Power BI.
The AI/BI Dashboard is a great example of this broader shift—from a “collection of tools” to a more unified platform.
What began as a basic visualization layer has evolved into a “Compound AI” system. Here’s how it has become so useful:
The “Ask Genie” integration: You’re no longer limited to staring at a static chart. As of 2026, every published dashboard includes an “Ask Genie” button by default. If a stakeholder notices a spike in a line chart, they don’t have to call you; they can right-click the chart and ask, “Genie, why did this drop on Tuesday?” and it will use Agent mode to track down the driver.
Direct-to-warehouse speed: Because it lives inside Databricks, there’s no need to “extract” data to a separate BI server. It queries the data where it already lives (Unity Catalog), which means the dashboard stays as fresh as your last ETL run.
AI-assisted authoring: You can build entire widgets just by describing what you want. Instead of dragging fields around, you can type, “Show me a funnel chart of our sales conversion by region,” and it generates the SQL and the visualization for you.
Deep governance: Since it’s built in, your security policies (row-level security, tags) follow the data automatically. You don’t have to recreate permissions in a separate tool like Tableau or Power BI.
Databricks Genie Nails Unity Catalog Migrations with Context-Aware Guidance
What do you like best about the product?
Databricks Genie's contextual understanding of Unity Catalog is genuinely impressive. While working through a complex UC migration, navigating three-level namespaces, volume paths, security modes, and widget-driven SQL execution, Genie reasoned through the specifics instead of falling back on generic answers. It really speaks the UC migration language, which cuts down on a lot of back-and-forth and makes troubleshooting feel more direct. Overall, the platform is powerful for managing large-scale data engineering work across Python, Scala, and notebook-based pipelines, all in one place.
What do you dislike about the product?
My biggest frustration with Genie is the lack of persistent session memory. On a long-running migration project with 60+ test cases and multiple interconnected components, having to re-establish context every session creates real overhead. Genie also struggles with cross-component reasoning: it handles individual notebooks well, but tracing issues across multiple layers of a framework is still largely a manual effort. Occasionally, the responses feel overly cautious when what’s needed is a more direct, confident answer.
What problems is the product solving and how is that benefiting you?
We’re using Databricks to carry out a full Unity Catalog migration for a large, automated ingestion framework, moving off the legacy Hive Metastore while also upgrading the runtime environment. Databricks provides a unified platform where the migration work, testing, and validation can all happen in one place. During testing, Genie in particular helped speed up root-cause analysis, for example, it pinpointed why a data extraction notebook was failing to resolve UC-managed table references and identified that adding a USE CATALOG statement was the fix. That kind of targeted, context-aware assistance directly reduces investigation time during complex migrations.
Centralized Governance and Fine-Grained Access Control with Unity Catalog
What do you like best about the product?
What I like best about Unity Catalog in Databricks is its ability to provide centralized data governance and fine-grained access control across all data assets, making it easier to manage and secure data in a collaborative environment.
What do you dislike about the product?
I created a notebook with more than 70 cells that I use to parse XML files. When I try to debug issues using Genie, it doesn’t work properly and ends up hanging.
What problems is the product solving and how is that benefiting you?
I used Spark functions to define the XML structure dynamically, assigning mpid and mpparentid as needed. This approach has been very beneficial for me.
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