Databricks Data Intelligence Platform
Databricks, Inc.External reviews
763 reviews
from
and
External reviews are not included in the AWS star rating for the product.
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.
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.
Transforms Table Data into Trustworthy Visuals with Helpful Debugging
What do you like best about the product?
I like the concept of transforming data into visuals for each table. Genie Code also helps with debugging and validating the data, which makes it easier to trust what I’m working with.
What do you dislike about the product?
As a proprietary platform built on open-source foundations, it can still introduce vendor lock-in risks, particularly through components such as Unity Catalog and its custom APIs.
What problems is the product solving and how is that benefiting you?
Databricks primarily solves the longstanding challenges of fragmented data architectures by introducing the Lakehouse paradigm. It combines the low-cost, scalable storage of data lakes with the reliability, ACID transactions, and performance of traditional data warehouses. This eliminates data silos, reduces costly ETL duplication, and provides a single unified platform for structured, semi-structured, and unstructured data.
End-to-End Data Management with Databricks
What do you like best about the product?
I like the fact that Databricks helps me manage data end to end, from ingestion to analytics to reporting and even governance. Within the platform, I'm able to build my pipelines to integrate and adjust data. I can also build dashboards, create reports, share them with my stakeholders, and ensure that the right people have access to the correct datasets and reports. The initial setup was pretty easy, and taking some training on the Databricks Academy was really helpful.
What do you dislike about the product?
The layout of the view of the portal could be nicer if it was a bit more colorful.
What problems is the product solving and how is that benefiting you?
Databricks solves a lot of problems by helping me build data pipelines, create a central source of truth, and maintain data security.
All-in-One Powerhouse with Room for Pricing Clarity
What do you like best about the product?
I like that Databricks is an all-in-one powerhouse where I can do multiple works in one place. It's powerful to manage data from multiple sources and have it in a single UC to manage permissions with row-level security. I also appreciate that I can create experiments, run multiple models, and select the best one from logs, which was difficult on other platforms. Once I learned the setup, it's been easy and comfy to work with.
What do you dislike about the product?
I find it difficult to use the calculator to determine CPU serving endpoint prices because the documentation doesn't explicitly explain this. It only mentions 1 concurrency equals 1 DBU on the Azure page, which isn't clear. The pricing calculator has a single option for serving endpoints, labeled as medium with four DBU, but lacks separate options for GPU or CPU and their concurrency, making it hard to understand how it works properly. Initially, I also felt it was very tough to learn Databricks and manage deployments of workspaces, although it became easier over time.
What problems is the product solving and how is that benefiting you?
Databricks consolidates multiple tools into one platform, making it powerful and convenient. I can manage permissions with row-level security and easily run experiments to select the best models, all in one place.
Unified Data Engineering, Science, and Analytics in One Collaborative Platform
What do you like best about the product?
What I appreciate most about Databricks is its ability to unify data engineering, data science, and analytics on a single platform. The collaborative environment—especially the notebooks and integrated workflows—makes it much easier for teams with different skill levels to work together without constant context-switching.
Another highlight is the integration with popular tools and cloud services that are widely used in the market today, which makes it easier to move data between them. The performance monitoring and job scheduling features help maintain visibility over pipelines, and the Delta Lake support for reliable data management has also been very useful.
Another highlight is the integration with popular tools and cloud services that are widely used in the market today, which makes it easier to move data between them. The performance monitoring and job scheduling features help maintain visibility over pipelines, and the Delta Lake support for reliable data management has also been very useful.
What do you dislike about the product?
Cost management is one area that could be improved. While Databricks offers autoscaling and flexible cluster options, it’s easy for resource usage to escalate unexpectedly, especially with large datasets and long-running jobs. Keeping costs predictable often requires careful oversight and a solid understanding of the platform’s pricing model.
Additionally, some of the more advanced features—such as fine-grained access controls and more complex job orchestration—can feel less intuitive. The documentation is extensive, but it occasionally leaves gaps that end up requiring trial and error.
Additionally, some of the more advanced features—such as fine-grained access controls and more complex job orchestration—can feel less intuitive. The documentation is extensive, but it occasionally leaves gaps that end up requiring trial and error.
What problems is the product solving and how is that benefiting you?
Databricks addresses several key challenges in modern data workflows, particularly around scalability, data reliability, and collaborative analytics. One major problem it solves is managing and processing large-scale datasets efficiently. By leveraging Apache Spark’s distributed computing framework, Databricks enables parallelized ETL pipelines and large-scale data transformations that would be impractical on traditional infrastructure.
Another challenge is ensuring data consistency and reliability across pipelines. With Delta Lake, Databricks provides ACID-compliant storage, versioned tables, and schema enforcement, which reduces data errors and simplifies data governance. This is especially beneficial when multiple teams are working on different stages of data pipelines at the same time.
Databricks also helps solve the problem of fragmented workflows for data scientists and engineers. Its unified environment supports multiple languages (Python, SQL, R, Scala) and includes integrated machine learning with MLFlow, making it easier to collaborate and move from data preparation to analytics and ML in one place.
Another challenge is ensuring data consistency and reliability across pipelines. With Delta Lake, Databricks provides ACID-compliant storage, versioned tables, and schema enforcement, which reduces data errors and simplifies data governance. This is especially beneficial when multiple teams are working on different stages of data pipelines at the same time.
Databricks also helps solve the problem of fragmented workflows for data scientists and engineers. Its unified environment supports multiple languages (Python, SQL, R, Scala) and includes integrated machine learning with MLFlow, making it easier to collaborate and move from data preparation to analytics and ML in one place.
Scalable Power with Manageable Trade-offs
What do you like best about the product?
The collaborative notebooks are hands-down my favorite part of Databricks. I love being able to jump into a notebook with my team, tweak Spark SQL queries live on those massive shipment datasets, and watch everything sync instantly—without any version-control.
It beats emailing notebooks back and forth or wrestling with merge conflicts; it feels like pair programming, but for data pipelines. And when you pair that with Delta Lake’s reliability for keeping my ETL jobs rock-solid on intermodal lane data, it ends up being a huge workflow saver.
Top notebook perks for me are the real-time editing and sharing that keeps everyone aligned during debugging, the built-in version history that lets me roll back mistakes quickly, and the seamless Spark integration so I’m not constantly context-switching when doing big data transforms.
It beats emailing notebooks back and forth or wrestling with merge conflicts; it feels like pair programming, but for data pipelines. And when you pair that with Delta Lake’s reliability for keeping my ETL jobs rock-solid on intermodal lane data, it ends up being a huge workflow saver.
Top notebook perks for me are the real-time editing and sharing that keeps everyone aligned during debugging, the built-in version history that lets me roll back mistakes quickly, and the seamless Spark integration so I’m not constantly context-switching when doing big data transforms.
What do you dislike about the product?
One key drawback is the cost management—charges can accumulate rapidly if clusters are left running, requiring careful monitoring of DBU usage and auto-termination settings.
Debugging intricate Spark job failures in notebooks often involves sifting through extensive log output, which extends resolution time considerably. Additionally, the UI experiences occasional performance delays under high workloads, impacting efficiency when responsiveness is essential.
Debugging intricate Spark job failures in notebooks often involves sifting through extensive log output, which extends resolution time considerably. Additionally, the UI experiences occasional performance delays under high workloads, impacting efficiency when responsiveness is essential.
What problems is the product solving and how is that benefiting you?
Databricks addresses core challenges in managing large-scale data processing, such as scalability limitations in traditional databases and the complexity of integrating disparate tools for ETL workflows. It enables distributed Spark processing across clusters to handle massive datasets efficiently, while Delta Lake provides ACID-compliant storage to ensure data integrity amid evolving schemas or concurrent updates.
This benefits me by streamlining pipelines that feed BI tools, reducing processing times from days to hours and minimizing manual infrastructure oversight. Collaborative notebooks further enhance team productivity through real-time editing, eliminating version control issues and accelerating development cycles.
This benefits me by streamlining pipelines that feed BI tools, reducing processing times from days to hours and minimizing manual infrastructure oversight. Collaborative notebooks further enhance team productivity through real-time editing, eliminating version control issues and accelerating development cycles.
showing 11 - 20