Databricks Data Intelligence Platform
Databricks, Inc.External reviews
763 reviews
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Databricks: Unified Lakehouse Platform with Powerful Spark Performance
What do you like best about the product?
i am working as a Data management specialist and using databricks regularly for handling data pipelines, large scale data processing, and governance tasks, i like most is that databricks provides a single unified platform for data engineering , analytics and AI , instead of using multiple tools. everything is available in one place, the lakehouse architecture is very useful because it combines data warehouse and data lake capabilities, so we can manage both structured and unstructured data efficiently. performance is very strong, especially with apache spark, it can process very large datasets quickly. i also like the collaborative notebooks where teams can work together using SQL, python or scala.
What do you dislike about the product?
one issue is that it has a steep learning curve, especially for new users who are not familiar with spark or distributed systems. cost management can also be challenging , it clustered are not optimized properly it can become expensive, sometimes too many features and configuration can makes it complex to manage for smaller teams. sometimes the platform feel complex. with many feature and configuration which can be difficult for smaller teams to manages. it it a powerful platform, but complexity and cost control are the main challenges in daily use.
What problems is the product solving and how is that benefiting you?
databricks solves the problem of managing large scale data processing and multiple data tools in a single platform, before using databricks data was spread across different system. and we has to use separate tools for ETL, storage and analytics, this made workflow complex and difficult to manage, databricks brings everything together in one place, so we can build data pipeline , process large datasets, and run analytics without switching tools. it also handles big data efficiently using distributed processing, which reduces processing time and improves performance, for me it has made data workflows more organized, reduces manual effort, and improved data reliability. it helps in faster data processing, better collaboration and more efficient data management.
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
Streamlines Data Engineering with Ease
What do you like best about the product?
I really appreciate Databricks for its manageability. The cluster management, unified workspace, optimization, and versioning are all aspects I find incredibly valuable. The console has all the tools readily available, which is super convenient for our large scale data engineering projects. Also, the initial setup was super easy, making it a smooth transition into using the platform.
What do you dislike about the product?
norhing much
What problems is the product solving and how is that benefiting you?
I use Databricks for large scale data analysis, processing, and machine learning. It makes cluster management, workspace unification, optimization, and versioning easy with all tools handy in the console.
Databricks as a Hands On Data Engineer: Solving Real World ETL, Governance, and Lakehouse Challenges
What do you like best about the product?
I believe the most attractive thing about Databricks lies in its all-in-one nature, which makes data management easier. Previously, when I used several tools for data-related activities, the experience was not great but here everything seems to be interconnected and straightforward.
The ability to utilize notebooks, especially when working with PySpark, is another advantage of Databricks that i like the core. The tool allows quickly executing changes and modifications without excessive preparation. It also positively impacts the process of collaboration among my team who can simultaneously work on their projects and monitor the overall progress. However, version control can sometimes appear a bit unclear in my view.
In performance, Databricks seem efficient for me at handling big data and operating smoothly without delays. Cluster scaling occurs automatically, allowing me and my team to save time on the infrastructure level. Therefore,it is easy as no additional planning and adjustments are required.
There are minor issues with the UI, which sometime work slowly. but at overall due to is super other aspects like easy methods in implementing and integrating things it encourages me to utilize Databricks frequently.
The ability to utilize notebooks, especially when working with PySpark, is another advantage of Databricks that i like the core. The tool allows quickly executing changes and modifications without excessive preparation. It also positively impacts the process of collaboration among my team who can simultaneously work on their projects and monitor the overall progress. However, version control can sometimes appear a bit unclear in my view.
In performance, Databricks seem efficient for me at handling big data and operating smoothly without delays. Cluster scaling occurs automatically, allowing me and my team to save time on the infrastructure level. Therefore,it is easy as no additional planning and adjustments are required.
There are minor issues with the UI, which sometime work slowly. but at overall due to is super other aspects like easy methods in implementing and integrating things it encourages me to utilize Databricks frequently.
What do you dislike about the product?
One aspect of Databricks that i dislike is its UI. As you spend longer in using the tool, moving between notebooks and clusters becomes annoying at times.
The other problem is the costs that can faster sum up when we are not cautious. Unnecessary clusters may be running for a longer period than required and without the me or my teams knowledge, thereby increasing up the costs in our projects.
There is also complexity of debugging the errors, which are difficult at times as it involves spending extra effort trying to find out where things might have been wrong mainly when dealing with complex pipelines.
At times, there are some discrepancies with regards to customer service which takes us somewhere where we need not to be.
The other problem is the costs that can faster sum up when we are not cautious. Unnecessary clusters may be running for a longer period than required and without the me or my teams knowledge, thereby increasing up the costs in our projects.
There is also complexity of debugging the errors, which are difficult at times as it involves spending extra effort trying to find out where things might have been wrong mainly when dealing with complex pipelines.
At times, there are some discrepancies with regards to customer service which takes us somewhere where we need not to be.
What problems is the product solving and how is that benefiting you?
The most important issue that Databricks resolves is the issue of working with large volumes of data and maintaining consistency. Previously, there were separate processes for data engineering, analytics, and machine learning operations, requiring separate tools and made it difficult for me to handle but now these all are in one place, another one critical issue solved by Databricks is the issue of processing large data volumes. Utilizing the Spark, and distributed computing allows it to perform the tasks that were extremely slow on legacy systems I worked with. This has helped speed up my pipeline, although some time the delays occur.Collaboration is also another problem that Databricks addresses. Multiple users can collaborate on the same notebook or data sets. Collaboration previously was confusing, and now it is easy and good and easy and easly understandable and mainly easy sharing notebooks and assets.Scalability is another issue resolved by Databricks; there is no need to pay attention to infrastructure management. Cluster scaling depends on user requirements, saving time. Previously, it was necessary to pay more attention to the configuration of the infrastructure.
Reliable data platform with powerful pipeline support
What do you like best about the product?
What I like best about Databricks is how it brings data engineering, analytics, and machine learning together in one clean workspace. It saves time, makes collaboration easier, and helps teams move faster with large data.
What do you dislike about the product?
What I dislike about Databricks is that Auto Loader can become frustrating when source data changes frequently, especially if column names or datatypes shift without warning.
For example, a field like customer_id may suddenly come in as cust_id, or a column that was previously a string may start arriving as an integer, which can cause schema drift and break downstream processing.
I also find it inconvenient when schema inference is not fully accurate, such as when nested JSON or semi-structured data is read incorrectly, because it then requires extra manual fixes and maintenance to keep pipelines running smoothly.
For example, a field like customer_id may suddenly come in as cust_id, or a column that was previously a string may start arriving as an integer, which can cause schema drift and break downstream processing.
I also find it inconvenient when schema inference is not fully accurate, such as when nested JSON or semi-structured data is read incorrectly, because it then requires extra manual fixes and maintenance to keep pipelines running smoothly.
What problems is the product solving and how is that benefiting you?
Databricks is solving the problem of building and managing data pipelines at scale without so much manual effort. It helps with reliable ingestion, schema evolution, and orchestration, so teams can process data faster and keep pipelines more stable even when source files change.
For me, that means less time spent fixing broken jobs and more time focusing on transforming and using the data. It also benefits me by making batch and streaming workflows easier to manage in one platform, which is especially useful when data keeps changing.
For me, that means less time spent fixing broken jobs and more time focusing on transforming and using the data. It also benefits me by making batch and streaming workflows easier to manage in one platform, which is especially useful when data keeps changing.
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