Databricks Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I use Databricks for ETL, Reporting, and AI, and I appreciate that it works as one unified solution for all data and AI needs. It makes it easier to track data and create insights, helping us deal with data silos. I like the Unity Catalog as it helps us manage and govern data in one place. I also like using AgentBricks as a multi-agent system for creating AI applications from PDFs and other documents. I find Genie valuable as it allows business users to ask questions in natural language and get exact answers. The initial setup of Databricks was very easy, making the transition smooth. Review collected by and hosted on G2.com.

I think workflow could be improved by adding multiple triggers to the same pipeline, as for now, if we want to schedule the same pipeline multiple times in a day, we have to clone it for each time. Review collected by and hosted on G2.com.

Genie Code (Databricks Assistant Agent) — I’m currently working on migrating existing workloads from ADF and SQLMI to Databricks. As part of that, I need to convert stored procedures and ADF dataflows into Databricks notebooks. Initially, we refactored all the code manually, but once Agent Mode was available in preview, we tried using it to convert the stored procedures and dataflows into Databricks PySpark code. I was impressed by the accuracy: it handled about 90% of the code conversion without errors, aside from some case-handling and similar adjustments.

Also, Lakeflow Connect helped me connect SharePoint and SFTP data to Databricks more easily. Review collected by and hosted on G2.com.

It’s not a major issue, but in my project the client asked us to generate table and column descriptions using AI in Unity Catalog. For each environment, these descriptions vary, and I have around 300 tables just in the Bronze zone. Having to click into each table and generate AI descriptions one by one is very time-consuming, and the results are not consistent across environments.

It would be much more efficient if we had an option to generate descriptions at the schema level, and if there were an information schema or system tables that stored table and column descriptions as metadata. That way, we could easily replicate them across environments. In some cases, clients also have source system documentation we could leverage to generate more accurate table and column descriptions. Review collected by and hosted on G2.com.

What I like best about Databricks is how it finally delivered what every data engineer/data professional has been wishing for — a true unified analytics and AI platform.

I remember working across five different tools just to get a single pipeline from ingestion to reporting. Databricks collapsed all of that into one environment, and that changed everything for me.

Delta Lake was the first breakthrough. When it arrived around 2020, ACID transactions and time‑travel immediately eliminated the operational pain we used to consider “normal.” If a job corrupted a table, I could roll back to a previous version in seconds instead of spending hours restoring backups. That reliability alone saved multiple downstream failures.

Before Delta existed, our pipelines relied heavily on overwrite patterns because there was no reliable way to apply updates or handle late‑arriving data safely. Overwrites were slow, expensive, and risky — especially for large tables. A single failure during overwrite could leave the table in a half-written, inconsistent state. Processing took longer, compute costs shot up, and recovery often meant manually rebuilding partitions from scratch.

The ROI became obvious as soon as we used Databricks end‑to‑end. Because one platform handles ingestion → transformation → ML → BI → governance, we retired entire categories of legacy tools and reduced operational overhead dramatically.

Then Genie arrived — and it genuinely transformed my day‑to‑day work.

I once needed a PySpark module for data quality checks. Genie generated the full logic — null checks, schema validation, aggregations — in seconds. Instead of spending 30 minutes writing boilerplate, I spent 3 minutes refining the logic. It shifted my focus from syntax to decisions.

Integrations are another strength. Connecting Databricks to S3, SQL Server, and especially Power BI has been seamless. Publishing Delta tables directly to BI models removed the need for brittle extracts and sped up refreshes. Unity Catalog made everything even cleaner with consistent permissions and lineage.

Performance is consistently strong when it matters — heavy joins, window functions, multi‑stage pipelines, or streaming workloads. Serverless compute starts instantly, and workloads scale predictably even under pressure.

Finally, onboarding surprised me. Features like serverless compute, natural‑language queries, AI‑generated code suggestions, and automatic comments make Databricks intuitive even for engineers new to Spark. It feels like the platform actively helps you learn.

In short: Databricks lets me work faster, recover instantly, integrate seamlessly, and scale confidently — all in one place. It’s the rare platform that improves both speed and reliability at the same time. Review collected by and hosted on G2.com.

What I dislike most about Databricks is the cost visibility and predictability.

Even as an experienced engineer, it can be difficult to get a straight, real‑time view of what a workflow will cost before running it. Photon vs. standard runtime, autoscaling behaviour, shuffle-heavy operations, DBUs—these can stack up quickly, and cost surprises happen unless you actively monitor and tune everything. A simple pipeline misconfiguration can quietly double your spend.

Another challenge is the rapid pace of new features and changes.

Databricks innovates incredibly fast, which is great, but it also means features may land before documentation, best practices, or governance patterns are fully mature. Sometimes functionality behaves differently across runtimes or cloud providers, and staying on top of everything requires continuous learning and refactoring. This can create team friction and technical debt.

In short: Databricks is exceptional, but the cost model isn’t always transparent, and the rapid feature rollout can introduce operational complexity that teams must actively manage. Review collected by and hosted on G2.com.

1) In our implementation, Genie Space is actively used to enable NLQ-based access across multiple data products like Finance, HR, Marketing, Sales, and Supply Chain (inventory, demand planning, and replenishment), reducing dependency on data teams for ad-hoc queries.

2) We designed separate Genie Spaces for each BU/team/data product, ensuring domain-level isolation while still supporting cross-functional querying where required (e.g., Finance + Sales joins).

Each Genie Space is carefully configured with curated data tables, business-level instructions, and semantic context, which significantly improves the accuracy of SQL generation.

3) We provide few-shot examples, guided prompts, and sample business questions tailored to each domain, helping Genie understand real business intent instead of generic query patterns.

4) In Chat Mode, business users directly ask questions in natural language, and Genie translates them into SQL and returns results, which has improved self-service analytics adoption.

5) In Agent Mode, Genie goes beyond SQL generation by creating a logical execution plan, breaking down complex queries into multiple steps before querying the underlying data.

6) We built a dedicated Anomaly Detector Genie Space, where users ask questions about cluster cost, performance issues, and inefficient workloads.

This anomaly-focused Genie analyzes long-running jobs, inefficient queries, and cluster utilization patterns, using historical workload data to identify optimization opportunities.

7) A key implementation is notebook-level analysis, where Genie highlights code issues, shows before vs after optimization, categorizes problems (performance, cost, inefficiency), and explains improvements clearly.

8) Genie also provides quantified recommendations, including expected cost savings (e.g., idle cluster reduction, query tuning impact) and workload-based optimization strategies, making it highly actionable for engineering teams.

9) We extended Genie into Genie Code integrated with Databricks AI Assistant, enabling an agentic development experience directly within our data engineering workflows.

Our team defined custom skills in Markdown (MD files) such as Coder, Tester, Mapper, and Data Generator, which are attached to Genie Code to modularize capabilities.

These skills are used to support end-to-end SDLC activities, including code generation, transformation logic creation, test case design, and synthetic data generation.

10) Genie Code operates by first creating a structured execution plan, outlining all required steps before starting any development activity.

It then breaks the plan into a detailed to-do list, executing each step sequentially (e.g., create notebook → write transformation → validate logic → optimize code).

11) During execution, Genie Code follows a human-in-the-loop model, asking for approvals at every step with options like allow once, always allow, or read-only execution.

The behavior of Genie Code is controlled through project-specific guidelines and instructions, ensuring it aligns with our coding standards, architecture patterns, and governance rules.

12) It acts as a co-developer within the workspace, assisting engineers in writing optimized code, validating logic, and ensuring best practices are followed consistently.

We are leveraging it for proactive development workflows, where Genie not only executes tasks but also suggests improvements and optimization opportunities during development itself.

This approach has enabled a “vibe coding” style of development, where engineers focus on intent while Genie handles structured execution, resulting in faster delivery, reduced manual effort, and improved overall code quality. Review collected by and hosted on G2.com.

Context limitation across Genie Spaces, also number of tables can be attached is 30 if i remember

Agent Mode reasoning depth is good but not fully autonomous

Need improvements in performance efficiency and reduce the latency Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Databricks collaborative notebooks are really useful and let me work in whatever language I need to meet my requirements effectively. The ability to mix Python, SQL and even Scala within a dashboard makes collaboration and teamwork much smoothet. I also appreciate how easily it integrates with other tools and cloud platforms, so it fits into my existing workflows without very little friction. Review collected by and hosted on G2.com.

I like their customer support and the frequent updates are a big reason this has become my favorite for data management, I also appreciate how well it integrates with external tools like Power BI for reporting its really good. Review collected by and hosted on G2.com.

Everything is centralized is a single dashboard spark jobs, notebooks and data pipelines. Autoscaling and auto termination genuinely help keep costs under control, and we could was a pleasant surprise that both run smoothly without any noticable lag. Sharing notebooks with the team is straightforward and cuts down on alot of back and forth. Review collected by and hosted on G2.com.

Finding older queries is really paunful. Anything beyond a few weeks becomes hard to track down, which makes it difficult to keep my data to day work flowing smoothly and to continue working without constant interruptions. Review collected by and hosted on G2.com.