Databricks Data Intelligence Platform
Databricks, Inc.External reviews
792 reviews
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Databricks Unifies Engineering and Analytics for Scalable Spark Pipelines
What do you like best about the product?
What I like best about Databricks is that it brings data engineering, processing, and analytics into one platform.
From my perspective, it makes it much easier to build and manage scalable pipelines with Spark without worrying too much about infrastructure.
From my perspective, it makes it much easier to build and manage scalable pipelines with Spark without worrying too much about infrastructure.
What do you dislike about the product?
What I dislike about Databricks is that cost control can get tricky if clusters are not managed properly.
Also, debugging distributed jobs is not always straightforward, and sometimes the UI feels a bit heavy when you just want quick insights
Also, debugging distributed jobs is not always straightforward, and sometimes the UI feels a bit heavy when you just want quick insights
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of handling large scale data processing and fragmented tools.
For me, it brings ETL, streaming, and analytics into one place, which reduces pipeline complexity and speeds up development and troubleshooting.
For me, it brings ETL, streaming, and analytics into one place, which reduces pipeline complexity and speeds up development and troubleshooting.
Powerful Unified Analytics with Seamless Governance and Effortless Scaling
What do you like best about the product?
What I like best about Databricks is its powerful and unified analytics ecosystem. Features like Unity Catalog and Metastore make data governance and access control seamless, while the Lakehouse architecture combines the best of data lakes and warehouses. PySpark support, dbutils, and collaborative workspaces make development efficient, and serverless compute simplifies scaling without infrastructure overhead.
What do you dislike about the product?
What I dislike about Databricks is the slow startup time of all-purpose clusters, which can interrupt workflow and reduce productivity. Additionally, Git integration can feel a bit sluggish at times, especially during commits or syncing, making version control less seamless than expected.
What problems is the product solving and how is that benefiting you?
Databricks solves the challenge of managing end-to-end data workflows by providing a unified platform for data engineering, data science, and analytics. It allows seamless data processing, transformation, and model development within a single environment.
This benefits me by simplifying my workflow as both a data engineer and data scientist, reducing the need to switch between tools. Additionally, its integration with Azure Data Factory enables smooth job orchestration and triggering for higher environments, making deployments more efficient and reliable.
This benefits me by simplifying my workflow as both a data engineer and data scientist, reducing the need to switch between tools. Additionally, its integration with Azure Data Factory enables smooth job orchestration and triggering for higher environments, making deployments more efficient and reliable.
Unified Data Platform, Minor Cost and Complexity Challenges
What do you like best about the product?
I like that Databricks provides a unified platform for data engineering and data science, eliminating friction across teams and enhancing the ability to accelerate development and deployments. It works especially well for end-to-end CICD pipelines.
What do you dislike about the product?
Well, in terms of what can be improved, I think, perhaps the cost management. If this can be looked into to make it more cost efficient for users, it will go a long way. And in addition to that, operational complexity sometimes presents a complex platform for new users to navigate easily. So if this can be addressed, then I think it should be a lot easier for engineers to work with.
What problems is the product solving and how is that benefiting you?
I use Databricks for scalable workflows across multi-cloud environments, solving data silo unification and minimizing bottlenecks in complex data processing. It optimizes cost and governance while providing a collaborative workspace, real time data ingestion, and enhanced system reliability and performance.
Unified Data Workflows with Databricks
What do you like best about the product?
I really like Databricks for its collaborative lake house environment, which has been key in unifying our data engineering and machine learning workflows. It bridges the gap between our engineering and analytics teams, allowing us to run BI and AI on a single platform. Additionally, the initial setup was surprisingly fast from a workspace perspective, especially with the native integration in Azure.
What do you dislike about the product?
The learning curve is quite steep for non-engineers. We've also had to be very diligent with cost monitoring as auto scaling clusters quickly lead to unexpected expenses if not managed strictly.
What problems is the product solving and how is that benefiting you?
Databricks solved our data stack fragmentation by unifying storage lakes and warehouses. It bridged the gap between engineering and analytics, letting us run BI and AI on a single platform.
Seamless, Collaborative Platform That Scales for Data Engineering and ML
What do you like best about the product?
Databricks' ability to seamlessly integrate everything is what I find most appealing. When working on actual projects, it really makes a big difference that you don't have to switch between several tools for data engineering, analysis, and machine learning.
The collaborative element is very noteworthy. Teams may easily collaborate without things becoming messy thanks to the notebooks' fluid and dynamic feel. For significant data work, it resembles Google Docs almost exactly.
I also really like how efficiently it manages large amounts of data without making it seem difficult. Even when working with large datasets, the platform feels user-friendly and can be scaled up when necessary.
Additionally, it makes perfect sense from an AI/ML standpoint. You are able to construct,
The collaborative element is very noteworthy. Teams may easily collaborate without things becoming messy thanks to the notebooks' fluid and dynamic feel. For significant data work, it resembles Google Docs almost exactly.
I also really like how efficiently it manages large amounts of data without making it seem difficult. Even when working with large datasets, the platform feels user-friendly and can be scaled up when necessary.
Additionally, it makes perfect sense from an AI/ML standpoint. You are able to construct,
What do you dislike about the product?
Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together.
Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things.
Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together.
Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things.
Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things.
Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together.
Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things.
What problems is the product solving and how is that benefiting you?
The fragmentation issue in the data and AI workflow is primarily resolved by Databricks. In the past, data storage, processing, analysis, and machine learning were usually done using different tools, and getting them all to cooperate was frequently difficult and time-consuming. Databricks eliminates a lot of the friction by combining all of it into a single platform.
That makes the developing process much more seamless for me. I don't have to worry about compatibility problems or waste time switching between environments. I can perform transformations, clean data, and create models all in one location, which reduces setup time and maintains organization.
It also addresses the difficulty of handling massive amounts of data.
I can rely on its distributed computing capabilities to manage demanding workloads rather than worrying about infrastructure or performance optimization from scratch. This allows me to concentrate less on resource management and more on finding a solution to the real issue.
Collaboration is another major issue it resolves. Sharing code, findings, and experiments can get disorganized in team environments. Because everything is consolidated with Databricks, it's simpler to work together, monitor changes, and maintain alignment.
All things considered, it helps me by cutting down on complexity, saving time, and allowing me to concentrate more on developing solutions—whether they be analytics, machine learning models, or data pipelines—instead of handling the overhead of maintaining numerous tools and platforms.
That makes the developing process much more seamless for me. I don't have to worry about compatibility problems or waste time switching between environments. I can perform transformations, clean data, and create models all in one location, which reduces setup time and maintains organization.
It also addresses the difficulty of handling massive amounts of data.
I can rely on its distributed computing capabilities to manage demanding workloads rather than worrying about infrastructure or performance optimization from scratch. This allows me to concentrate less on resource management and more on finding a solution to the real issue.
Collaboration is another major issue it resolves. Sharing code, findings, and experiments can get disorganized in team environments. Because everything is consolidated with Databricks, it's simpler to work together, monitor changes, and maintain alignment.
All things considered, it helps me by cutting down on complexity, saving time, and allowing me to concentrate more on developing solutions—whether they be analytics, machine learning models, or data pipelines—instead of handling the overhead of maintaining numerous tools and platforms.
Helpful for various data sources
What do you like best about the product?
It's been helping our team create content quicker.
What do you dislike about the product?
It's taken a long time for our company to review products and approve for use.
What problems is the product solving and how is that benefiting you?
Coalesce our data sources together to allow data scientists to focus on their tasks
Seamless Big Data Processing with Robust Access Control
What do you like best about the product?
I use Databricks for big data processing and data engineering with PySpark. It helps me process terabytes of data seamlessly using Spark architecture. I love the Unity catalog and its access framework, which allows me to share data across the organization without much trouble and control access like Select, View, and others on delta tables based on roles or teams. The initial setup was seamless, and I appreciate how it integrates with Microsoft Fabric.
What do you dislike about the product?
I believe the billing experience can be improved; I use Databricks through Azure.
What problems is the product solving and how is that benefiting you?
I use Databricks to process terabytes of data seamlessly using Spark architecture. The Unity Catalog helps me share data across the organization effortlessly, controlling access to delta tables based on roles or teams.
Unified Databricks Workspace That Streamlines Collaboration and Complex Data Workflows
What do you like best about the product?
What I like best about Databricks is how it brings data engineering, analytics, and machine learning into one unified workspace. I find collaboration much easier with shared notebooks, and the seamless integration with big data tools saves me time. It simplifies complex workflows while still offering powerful capabilities when I need them.
What do you dislike about the product?
One thing I dislike about Databricks is that it can feel expensive, especially for smaller projects or teams. I also find cluster configuration and cost management a bit complex at times. The interface, while powerful, can be overwhelming for beginners, and debugging distributed jobs isn’t always as straightforward as I’d like.
What problems is the product solving and how is that benefiting you?
Databricks solves the challenge of handling large-scale data processing, analytics, and machine learning in one place. For me, it removes the hassle of managing separate tools and infrastructure. I benefit by working more efficiently, collaborating easily with my team, and turning complex data into useful insights faster, with less operational overhead overall.
Efficient Unified Platform for Scalable Data Processing
What do you like best about the product?
I like how Databricks simplifies big data processing with a unified platform for data engineering, analytics, and machine learning. Its seamless integration with Spark and scalability makes handling large datasets much more efficient.
What do you dislike about the product?
The cost can become quite high with heavy usage, especially if clusters aren’t optimized. Also, debugging and monitoring jobs can sometimes feel less intuitive compared to traditional tools.
What problems is the product solving and how is that benefiting you?
Databricks solves the challenge of processing and managing large-scale data efficiently by providing a unified platform for ETL, analytics, and machine learning. It benefits me by simplifying pipeline development, improving performance with Spark, and reducing the need to manage multiple tools.
Powerful Lakehouse Platform with Strong Collaboration
What do you like best about the product?
Databricks is a powerful data lakehouse platform brings data engineering, AI/ML, and SQL analytics together in one collaborative workspace.
What do you dislike about the product?
The downside of Databricks is that it can be costly, especially with frequent cluster usage and poorly optimized workloads
What problems is the product solving and how is that benefiting you?
Databricks helps solve the challenge of working with large volumes of data by bring data engineering, analytics, and AI/ML into one unified platform
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