My main use case for Databricks is running SQL queries. I use Databricks in my day-to-day work by doing SQL queries directly in Databricks using the Genius platform to better correct the queries instead of doing queries on another platform.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
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Web-based SQL workflows have become more secure and have saved significant query time
What is our primary use case?
What is most valuable?
The best features offered by Databricks include the fact that it is on the web, that it does not depend on installing any software, and most importantly, the security that prevents connection to anyone else who is not logged in.
Regarding the security and web access I mentioned, I have noticed concrete benefits related to collaboration and data protection within my team, such as it being very secure and the fact that every time we enter the platform, it does the same credential verification.
The features of Databricks have impacted my organization positively, as it has done so very efficiently since we switched from several platforms to using this one. After implementing Databricks in my organization, I have observed that it has been more efficient with my team.
What needs improvement?
I think the aspects of Databricks that should be improved are that it could be faster and that I would like to be able to run direct queries from the server. I have not seen any other improvements that I think are needed in Databricks.
What do I think about the stability of the solution?
Databricks is stable.
What do I think about the scalability of the solution?
I rate the scalability of Databricks as excellent.
How are customer service and support?
Databricks customer support is very good. I would give Databricks customer support a rating of ten.
Which solution did I use previously and why did I switch?
I did not use any other solution before Databricks.
What was our ROI?
I have seen a return on investment, as time is greatly saved and processes are faster.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, implementation costs, and licensing is that it is very efficient and very fast.
What other advice do I have?
My advice to others considering using Databricks is that it is the best platform with artificial intelligence. I give this review an overall rating of ten.
Databricks Unifies Teams with Strong IaC, Streaming, and Git Integration…!!!
What do you like best about the product?
I like Databricks since it has improved collaboration between our data science and data engineering teams by bringing their workflows onto one platform.Its also the best since it offers us with a complete Terraform provider for managing infrastructure as code makes streaming data processing straightforward and integrates with multiple Git providers with a built-in merge assistant to simplify version control.
What do you dislike about the product?
I have no complain regarding Databricks.
What problems is the product solving and how is that benefiting you?
Databricks streamlines data processing and analytics by unifying them on a single platform.
All-in-One Delta Lake Platform That Makes ETL Fast and Cost-Efficient
What do you like best about the product?
Delta Lake + Workflows + Unity Catalog in one platform eliminated the need for stitching together separate ingestion, transformation, and governance tools. As a data engineer, I spend more time building pipelines and less time managing infrastructure. The notebook experience and cluster auto-scaling make iterating on complex ETL fast and cost-efficient.
What do you dislike about the product?
Cluster spin-up times and cost predictability are still the biggest friction points for me. Cold starts can really slow down ad-hoc work, and DBU costs need close monitoring to avoid unpleasant surprises. The Workflows UI has improved a lot over time, but it still doesn’t feel as flexible as dedicated orchestrators when you’re dealing with more complex DAGs. Even so, I see these as mostly polish items—the platform’s core value easily outweighs them.
What problems is the product solving and how is that benefiting you?
Databricks addresses a major fragmentation problem in our data engineering stack. Previously, we relied on separate tools for ingestion, transformation, orchestration, and governance—each with its own learning curve, maintenance overhead, and potential failure points. Now, it’s consolidated into a single platform.
In practice, it helps us run large-scale ETL pipelines that process millions of records daily, with Delta Lake improving reliability through ACID transactions, schema enforcement, and time travel for debugging. It also closes the collaboration gap between data engineers and data scientists: we build the pipelines, and they can consume the same tables directly in notebooks without data duplication or sync issues.
Unity Catalog resolved a long-standing governance headache by centralizing access control across workspaces. Overall, the result is faster pipeline development, fewer production incidents tied to data quality problems, and far less glue code to maintain. What used to take weeks to build and stabilize now takes days.
In practice, it helps us run large-scale ETL pipelines that process millions of records daily, with Delta Lake improving reliability through ACID transactions, schema enforcement, and time travel for debugging. It also closes the collaboration gap between data engineers and data scientists: we build the pipelines, and they can consume the same tables directly in notebooks without data duplication or sync issues.
Unity Catalog resolved a long-standing governance headache by centralizing access control across workspaces. Overall, the result is faster pipeline development, fewer production incidents tied to data quality problems, and far less glue code to maintain. What used to take weeks to build and stabilize now takes days.
User-Friendly, Affordable Data Processing at Scale with Fast Support
What do you like best about the product?
It has a user-friendly interface and integrates with other clouds easily. We can process TBs of data without much effort. Compared to other data-processing tools, its price is lower. It also includes Ginee AI, and by using that we can handle data processing much more easily. If we face any issues, they solve the problem in less time.
What do you dislike about the product?
it's is very difficult to use for new users
What problems is the product solving and how is that benefiting you?
i'm a data engineer so i'm using this for process the TB's of the data. for ML Flows. integrate with data science, analytics team
Databricks Boosts Productivity with a Unified Workspace and AI-Assisted Development
What do you like best about the product?
As an ADE, what I like most about Databricks is that it removes infrastructure friction, so I can focus purely on data engineering logic. I also really appreciate the unified workspace: I can write PySpark for data extraction and transformation, switch to SQL for exploratory analysis, and review data lineage, all within a single browser tab is a huge productivity boost. On top of that, the built-in AI features have been incredibly helpful because they let me worry less about syntax and spend more time on the logic itself. Finally, with the seamless integrations through Lakehouse Federation and the straightforward onboarding, my work has become much smoother.
What do you dislike about the product?
While the platform is excellent for development, the DBU consumption model and cluster management can feel a bit daunting at my level. As a beginner, I spent a lot of time testing different bits of logic, and it was easy to forget to terminate the all-purpose cluster afterward, which led to minimal but still unnecessary credit consumption. Thankfully, auto-termination exists and helped keep credits from disappearing. Still, a more aggressive auto-termination setting or a smarter pause feature would make it easier to avoid any credit loss in the first place.
What problems is the product solving and how is that benefiting you?
Databricks helps solve the local environment hell-hole that often slows down junior engineers. By providing a ready-to-use Lakebase architecture, it lets me practice enterprise-level data engineering without needing to connect to VPNs or deal with complex Docker setups. In my project, it addressed the full flow: ingesting raw data, transforming it, and serving it for analytical queries. This also benefits my team, because I can onboard onto real data pipelines much faster and start contributing sooner. At the same time, I’m learning how to build production-ready ETL workflows without my senior teammates having to spend hours helping me troubleshoot my local Python/Spark environment. An unexpected benefit was how seamless collaboration is. Because the notebooks are cloud-based and ties to the workspace, sharing my project with senior engineers for code reviews was as simple as sending a link. Additionally, the way Databricks handles metadata made me realize early in my career how important data governance is.
Centralized Governance, Powerful Migration Tool
What do you like best about the product?
I like the Unity Catalog as a single governance layer which centralizes access control and offers fine-grained permissions across data assets. The workspace API and automation features are valuable for streamlining operations. I appreciate that volumes replace mounts, improving security with credential-free access. The Lakehouse Federation simplifies cost consolidation and reduces data movement costs. Having Photon and ML Runtime on the same platform enhances operational efficiency. The initial setup was user-friendly, thanks to the guidance from the Databricks portal.
What do you dislike about the product?
* Migration tooling is manual and fragmented * Mount-to-Volume path conversion has no automated path * Cluster security mode NONE still exists * Hive metastore and UC coexist awkwardly * Custom WHL libraries on mount lack a clean upgrade path
What problems is the product solving and how is that benefiting you?
Databricks provides centralized access control with fine-grained permissions, identity-based access without exposing storage credentials, unified data discovery and lineage, and reduces operational overhead by consolidating platforms and managing data more efficiently.
Powerful Low-Latency Telemetry Pipelines with Streaming Tables & Materialized Views
What do you like best about the product?
In a telco environment handling massive data volumes from fixed and mobile networks (GPON, 4g/5g Core, and RAN) ingesting unstructured or semi-structured frequency telemetry incrementally from our virtualized functions like vEPC, vCPE or VHGW) with minimal setup.
My team works closely with virtualized network functions and Multi-access Edge Computing. Features like Streaming Tables and Materialized Views help us to build low-latency pipelines that process network performance metrics near real-time, helping us monitor network KPIs and QoS efficiency.
Because my team's core experties lies in network deisgn and system virtualization rather than database administration, Predictive Opimization and Liquid Clustering are highly beneficial. Tehy autonomously handle table maintenance, file compaction, and data layout optimization freeing up our resources to focus on network architecture.
My team works closely with virtualized network functions and Multi-access Edge Computing. Features like Streaming Tables and Materialized Views help us to build low-latency pipelines that process network performance metrics near real-time, helping us monitor network KPIs and QoS efficiency.
Because my team's core experties lies in network deisgn and system virtualization rather than database administration, Predictive Opimization and Liquid Clustering are highly beneficial. Tehy autonomously handle table maintenance, file compaction, and data layout optimization freeing up our resources to focus on network architecture.
What do you dislike about the product?
Virtualized network functions, routers, and disaggregated hardware frequently undergo software updagrades, which often introduce sublte changes in telemetry output schemas. When using structured streaming or auto loader these schema drifts cause our streaming queries to fail, requiring a manual restart of the stream to re-plan the schema.
When we need to update the logic of a complex network KPI defined within a materialized view, any change to the query triggers a full recomputation of the view. Given the massive scale of telecom transaction datasets, this can result in noticeable compute costs.
We rely on a variety of data tools within our ICT ecosystem, not all solutions featured in Partner Connect natively support Unity Catalog. This can crete integration and governance hurdles when we try to connect certain third-party analytics and data preperation tools to our secured data lake.
When we need to update the logic of a complex network KPI defined within a materialized view, any change to the query triggers a full recomputation of the view. Given the massive scale of telecom transaction datasets, this can result in noticeable compute costs.
We rely on a variety of data tools within our ICT ecosystem, not all solutions featured in Partner Connect natively support Unity Catalog. This can crete integration and governance hurdles when we try to connect certain third-party analytics and data preperation tools to our secured data lake.
What problems is the product solving and how is that benefiting you?
We ingest continous streas of performance data from virualized network functions and traditional transport layers. By building streaming pipelines, we can monitor virtualized cpres and routers to identify anomalies or degredations in network traffic.
Aligning with my interest in Network AI and Machine learning, our data scientists use the patform to develop predictive models. We train models on historical GPON/DSL line failures, mobile cell tower loads, and customer usage patterns to predict network congestion, schedule proactive maintenance and mitigate customer chirn across customer segments.
As an evangelist for tech evolution, I use the platform to bridge the gap between our core network engineering teams and business units. By connecting business semantics and establisihng secure Delta Sharing protocols, we provide business analysts and decision makers with giverned, self service access to network insights without risking security compliance.
Aligning with my interest in Network AI and Machine learning, our data scientists use the patform to develop predictive models. We train models on historical GPON/DSL line failures, mobile cell tower loads, and customer usage patterns to predict network congestion, schedule proactive maintenance and mitigate customer chirn across customer segments.
As an evangelist for tech evolution, I use the platform to bridge the gap between our core network engineering teams and business units. By connecting business semantics and establisihng secure Delta Sharing protocols, we provide business analysts and decision makers with giverned, self service access to network insights without risking security compliance.
Love the Databricks and its Features and Unity Catalog for Streamlined Governance
What do you like best about the product?
In Databricks, I really like the newer features such as Gennie, the Databricks Assistant, agents, and the event-trigger mechanism.
Also, the Unity Catalog feature is amazing. Having one place for all sources makes things much easier, and UC helps with governing tables in a more organized way.
Also, the Unity Catalog feature is amazing. Having one place for all sources makes things much easier, and UC helps with governing tables in a more organized way.
What do you dislike about the product?
Nothing special to dislike, but there’s a feature to jump to a particular command. The feature itself is fine, but it’s placed right next to the notebook, which makes it easy to click accidentally, and that breaks my workflow.
What problems is the product solving and how is that benefiting you?
I am using it in my project for data processing and data quality analysis. With Databricks and its functionality, I am building agents in Genie space. Using UC, I am managing all managed and external tables in one place.
All-in-One Platform for Data Engineering, ML, and GenAI
What do you like best about the product?
On one platform, we’re getting everything we need, including data engineering, machine learning, and GenAI.
What do you dislike about the product?
As of now, I don’t see any dislikes that impact my work.
What problems is the product solving and how is that benefiting you?
An end-to-end platform for deploying ML projects in one place.
Lakehouse Unifies Ad and CRM Data for Governed, SQL-Powered ML Segmentation
What do you like best about the product?
The lakehouse approach allows our team to bring together the ad platform data along with the CRM information in a single governed space. This saves on switching between tools while analyzing and building audience segments using ML by running SQL queries.
What do you dislike about the product?
The technical complexity involved in setting up the clusters may pose a challenge for non-technical marketers.
What problems is the product solving and how is that benefiting you?
It eliminates manual reporting via spreadsheets and allows me to gain insights on paid social ROAS in real time and helps in making quick decisions on reallocating budgets for Meta and LinkedIn campaigns.
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