608 Databricks Data Intelligence Platform Reviews
Databricks excels at unifying data engineering, analytics, and AI/ML on a single platform. The Lakehouse architecture bridges the gap between data lakes and warehouses, making it incredibly efficient for managing structured and unstructured data. I especially appreciate the seamless integration with Apache Spark, robust notebook support for collaborative development, and the simplicity of Delta Lake for versioned data storage. Features like AutoML and Unity Catalog bring governance and intelligence together, making it easier to scale analytics securely and reliably. Review collected by and hosted on G2.com.
While powerful, the platform has a learning curve—especially for teams unfamiliar with Spark or distributed computing. Some features (like Unity Catalog or serverless compute) can be region-specific or limited by cloud provider compatibility. Additionally, job debugging and cluster cost management can be challenging without careful monitoring and tagging, particularly in enterprise-scale projects. Review collected by and hosted on G2.com.
What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments. Review collected by and hosted on G2.com.
One downside is that the initial setup and networking configuration can be complex and require technical expertise. Also, the cost can scale up quickly depending on usage, so cost monitoring is essential. Additionally, the lack of comprehensive documentation in some languages like Japanese can be a limitation. Review collected by and hosted on G2.com.
I like it because it stands out for its ability to unify data science, data engineering, and business analytics into a single interface. I also appreciate the seamless integration with collaborative notebooks and the ability to seamlessly with Delta Lake is also a big plus, ensuring reliability and performance when managing large-scale data. Review collected by and hosted on G2.com.
Sometimes, the web interface can take a while to load active clusters. Id also like more visual tools to monitor resource usage in real time. Review collected by and hosted on G2.com.
The Databricks Data Intelligence Platform allows us to have a single source of development capabilities for IT developers and business analysts. This allows for easier implementation of capabilities and consolidation of toolsets across the environment. Users are given the freedom to develop the data products they need in the time frames they need them. It makes implementation and productionalization of these projects much easier to integrate for downstream usage. Review collected by and hosted on G2.com.
The one "dislike" I have is that it's difficult to keep up with all of the improvements and enhancements in the platform. We always see new features to implement and want to make sure we're doing the best we can for our end users. Review collected by and hosted on G2.com.
Databricks is very dependable, flexible, and helps our company to create innovative analytical solutions and every week in our technical meetings, we cover a range of subjects like bugs, best practices, new features, and more using it. Also every member of the support crew in Databricks responds fast and is rather helpful. Review collected by and hosted on G2.com.
When using creative features like AI, cost control and estimation might prove difficult. And although lakeview SQL is still not yet developed, Databricks is actively pushing their utilization nonetheless. Also every now and then unannounced feature activation in my office surprises me. Review collected by and hosted on G2.com.
Working on the Databricks where we can easily analyze huge datasets and integrate our platform or website to create an insight from our internal dataset. They have number of features that help us to manage all analytical views with pre define templates. Review collected by and hosted on G2.com.
Implementation was so quick and easy that help us to manage all data without any issues. I like their customer support services and their frequent use make them my favorite platform for data management. Review collected by and hosted on G2.com.
The Unity Catalog bundles useful data types together like Tables (delta), Models, Views, Functions and Volumes (for unstructured data). Additionally, the Workflows tab streamlines efficiency to run jobs and pipelines updating and engaging with big data. Review collected by and hosted on G2.com.
The worst functionality comes with the compute modes, and the restricted abilities. For example, Dedicated single-user access mode is essential to utilize ML runtimes and some Spark context access; however, Standard shared access mode is key for the latest UC functionality such as engaging with Shallow Clones properly. Review collected by and hosted on G2.com.
Databricks’ platform is a powerful collaborative tool which allows teams to work together seamlessly on data projects. The integrated environment for data processing and analytics, along with its user-friendly interface, makes it easy to visualize insights and share findings in real time. Review collected by and hosted on G2.com.
The occasional complexity in managing multiple clusters and environments, which can lead to confusion regarding resource allocation. Additionally, the learning curve for new users can be steep, making it a challenge for organizational adoption. Review collected by and hosted on G2.com.
I attended to explore how Databricks unifies data engineering, analytics, and AI. The platform’s integration with BI tools and support for Delta Lake worked well. Performance and collaboration features stood out. However, the learning curve and some UI complexities could be improved for newer users transitioning from traditional platforms. Review collected by and hosted on G2.com.
I attended the Databricks Data Intelligence Platform session but found it rather unremarkable. The features and performance were neither outstanding nor disappointing. It felt generic, with vague improvements and minimal innovation. Overall, I am uncertain about its distinct advantages, rendering my review neither particularly informative nor actionable for prospective users. Review collected by and hosted on G2.com.
What I appreciate most about Databricks is its unified approach to data engineering and data science. The platform eliminates the traditional silos between our data engineers and data scientists by providing a collaborative workspace where both teams can work on the same datasets using their preferred tools - whether that's Spark, Python, R, or SQL. The Delta Lake technology has been particularly valuable for ensuring data quality and reliability in our pipelines. The auto-scaling clusters mean we don't have to worry about infrastructure management, and the notebook interface makes it easy to document and share our work. MLflow integration for experiment tracking and model deployment has streamlined our machine learning lifecycle significantly Review collected by and hosted on G2.com.
The main challenges we've encountered are around the learning curve and cost management. For team members coming from traditional SQL backgrounds, the transition to Spark-based analytics requires significant upskilling. The pricing model can be complex to predict, especially with auto-scaling clusters, and costs can escalate quickly if not monitored carefully. The UI, while functional, can feel overwhelming for new users with so many features and options. We've also experienced occasional performance inconsistencies during peak usage times, and some of the more advanced features require deep technical knowledge to implement effectively. Documentation, while comprehensive, can be dense and assumes a high level of technical expertise. Review collected by and hosted on G2.com.
