Databricks Data Intelligence Platform
Databricks, Inc.External reviews
672 reviews
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Outstanding Analytics and Seamless Integration Across Platforms
What do you like best about the product?
This tool can be implemented on nearly any platform and provides a wide array of integrated analytical features. It offers a thorough set of analytical functions that make operations easier and deliver a comprehensive perspective on the technology. Its performance in big data analytics is outstanding, as it is built to handle and process vast quantities of information efficiently.
What do you dislike about the product?
It requires a bit more time for a database administrator and is somewhat difficult to configure when migrating infrastructure. Sometimes some adjustments are necessary to find the exact solution.
What problems is the product solving and how is that benefiting you?
I have used this tool for marketing analysis, and it has significantly simplified working with large datasets. It offers support for various natural language processing tasks and includes a fast query processing engine. From my experience, it stands out as the best tool for high-performance analytics work.
User-Friendly Platform with Outstanding Support
What do you like best about the product?
I have used approx. number of Data Analytics platform but the user friendly enviornment and features of Databricks gives always reliable and satisfactory experience to me that gives a confidence to handle large data sets without any problem.
What do you dislike about the product?
Never have any issue with their services instead they gives a best user support to us to manage all our AIML integrations and new AI implementations.
What problems is the product solving and how is that benefiting you?
We have mainly use Databricks for our data warehousing and analytical use cases and also their multiple features like AIML and ETL integration gives best infrastructure to manage all data related task at one place without any issue and confusion.
Outstanding All-in-One Analytics with Intuitive UI and Impressive Speed
What do you like best about the product?
I like that it's fast, and excellent all-in-one analytics solution, with excellent scalability that reduces time to market. The user interface is intuitive and perfect for users with varying skill levels. I also like that the database has the ability to terminate or time out instances, which helps us manage costs.
What do you dislike about the product?
I have no complaints about this tool; it allows us to run code efficiently without getting bogged down by infrastructure or optimization concerns. Additionally, there is a wealth of helpful training resources available for both developers and data scientists.
What problems is the product solving and how is that benefiting you?
Databricks serves as our main data platform, allowing us to collect, standardize, clean, transform, and refine our various data sources. Its workflow capabilities have enabled us to automate repetitive tasks and build internal applications using reusable workflows.
Powerful Unified Platform for Data and AI, but Complex Setup and Costly for Continuous Use
What do you like best about the product?
I like that Databricks provides a unified environment for data engineering, analytics, and machine learning. The platform makes it easy to collaborate across teams, manage large-scale data efficiently, and build advanced AI models using the same infrastructure. The integration with major cloud providers and the Lakehouse architecture make data management both flexible and scalable.
What do you dislike about the product?
While Databricks is a powerful and flexible platform, it can be complex to set up and manage, especially for teams without strong data engineering expertise. The cost structure can also become expensive for continuous workloads, and performance tuning sometimes requires deep knowledge of Spark and cluster optimization. Additionally, the user interface could be more intuitive for non-technical users.
What problems is the product solving and how is that benefiting you?
Databricks helps centralize and manage large volumes of data from different sources in a single, scalable platform. It simplifies data processing, analytics, and machine learning workflows, allowing teams to collaborate efficiently and deliver insights faster. By integrating data engineering, analytics, and AI capabilities, it reduces infrastructure complexity and accelerates the development of data-driven solutions.
Effortless Data Management and Fast Analytics Set This Tool Apart
What do you like best about the product?
Managing large amounts of data while working in coordination with other teams for systematic data uploads, updates, and troubleshooting is seamless, without concerns about limitations. Additionally, the speed at which analytics and geometric machine learning are processed truly sets it apart.
What do you dislike about the product?
beginners complexity in handling the tools in a test or trial phase
What problems is the product solving and how is that benefiting you?
It is so far has the capability of handling data in large quantity, being company's presence across several states it makes the data compilation easy for the department to systematically get the update
one of leaders in data
What do you like best about the product?
What I really like about the Databricks Data Intelligence Platform is how it brings everything together in one place. Instead of juggling different tools for data engineering, analytics, machine learning, and governance, you can do it all in a single environment.
What do you dislike about the product?
Honestly, what I find a bit frustrating about the Databricks Data Intelligence Platform is that while it’s incredibly powerful, it can also feel overwhelming at times. There’s a steep learning curve, especially for teams who are just getting started and don’t have much experience with Spark or distributed systems.
What problems is the product solving and how is that benefiting you?
The big problem it solves is breaking down data silos. Instead of having separate systems for raw data, analytics, and machine learning, Databricks gives you one platform where everything connects. That means less time moving data around and more time actually using it.
A game changer for handling large data
What do you like best about the product?
Databricks has made working with massive datasets so much easier for our team. The collaborative notebooks help us share ideas and troubleshoot together, and the platform’s ability to scale means we don’t have to worry about hitting limits. It’s sped up our analytics and machine learning projects, and connecting to different data sources is a breeze.
What do you dislike about the product?
The initial setup was a bit confusing, and some of the advanced features could use better documentation. Figuring out the pricing took some time, but once we got going, the benefits were clear.
What problems is the product solving and how is that benefiting you?
Databricks is helping me tackle the challenge of working with huge amounts of data for analytics and machine learning. Before using the platform, processing and distributing big datasets was slow and complicated. Now, I can run large-scale data science experiments and build models much faster. The platform’s tools make it easier to collaborate, share results, and turn data into insights that actually help my team make better decisions.
Best Collaborative platform for data engineer, analyst and scientists
What do you like best about the product?
Easy to use, it provides one under umbrella platfrom where different teams collborate their work together, which is very helpful for development and data sharing.
What do you dislike about the product?
as of now i dont find any issues, but we can improve on unity catalog side.
What problems is the product solving and how is that benefiting you?
We have different pipelines in databricks, we are utlisinf it for getting spark benifts and colloborative developement and data sharing between teams.
Worth the effort
What do you like best about the product?
Databricks excels at unifying data engineering, analytics, and machine learning into one seamless platform. What I like best is how effortlessly it handles massive data volumes while enabling collaborative development through notebooks. The integration with Apache Spark and the ability to run scalable workloads with ML, SQL, and Python side-by-side makes it a powerhouse for data-driven teams. Its governance and Delta Lake architecture also ensure reliability and security across the data pipeline.
What do you dislike about the product?
While Databricks is incredibly powerful, the learning curve can be steep for non-technical users or teams new to distributed computing. The UI, though functional, can sometimes feel a bit clunky compared to more modern data platforms. Additionally, managing costs in a multi-user environment requires careful governance, especially for teams running large-scale compute-heavy jobs.
What problems is the product solving and how is that benefiting you?
Databricks is helping us break down data silos by centralizing data engineering, analytics, and machine learning into a unified environment. It simplifies handling large datasets, automates ETL processes, and enables real-time analytics and AI-driven insights. As a result, we’ve significantly improved our data pipeline efficiency, reduced time to insights, and empowered both data scientists and analysts to collaborate more effectively using a single platform.
Unlocking Scalable Data Insights with Databricks
What do you like best about the product?
Databricks excels in unifying data engineering, analytics, and machine learning in a collaborative, cloud-based environment. Its support for multiple programming languages (Python, SQL, Scala, R) makes it incredibly flexible. The Lakehouse architecture simplifies data management by combining the best of data lakes and data warehouses. The auto-scaling compute clusters, tight integration with tools like MLflow, and powerful notebooks streamline experimentation and production deployment. I also appreciate the frequent product updates and commitment to open-source technologies like Apache Spark and Delta Lake.
What do you dislike about the product?
While powerful, Databricks has a learning curve—especially for non-technical users or those new to Spark-based architectures. Pricing can escalate quickly if not closely monitored, particularly with always-on clusters. The UI, although improving, still feels unintuitive in certain areas (like managing jobs or cluster permissions). Some integrations, especially with on-premise systems, require additional effort or custom workarounds.
What problems is the product solving and how is that benefiting you?
Databricks addresses the fragmentation between data engineering, data science, and analytics by offering a unified platform. Previously, we struggled with maintaining multiple disconnected tools for ETL, machine learning, and BI. Databricks' Lakehouse architecture allows us to manage structured and unstructured data in a single place, simplifying our data pipelines and reducing operational overhead.
It also improves collaboration across teams—data engineers, analysts, and data scientists can work together in shared notebooks with version control and built-in visualizations. With Delta Lake, we now have ACID-compliant data reliability and time-travel capabilities, which help ensure data quality and reproducibility.
As a result, project delivery times have decreased, and our ability to iterate quickly on models and reports has improved significantly—leading to faster business insights and better data-driven decision-making.
It also improves collaboration across teams—data engineers, analysts, and data scientists can work together in shared notebooks with version control and built-in visualizations. With Delta Lake, we now have ACID-compliant data reliability and time-travel capabilities, which help ensure data quality and reproducibility.
As a result, project delivery times have decreased, and our ability to iterate quickly on models and reports has improved significantly—leading to faster business insights and better data-driven decision-making.
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