Databricks Data Intelligence Platform Reviews & Product Details

I am love to working on the databricks environment because their reliable and scalable platform always provide best analysis dashboard and number of integration that help us on our big data analysis. Review collected by and hosted on G2.com.

Their easy to use system and hassle free implementation process make work more easy and frequent for us. They provide 24x7 customer support that manage our all requirement need. Review collected by and hosted on G2.com.

We have build a fraud detection system for a European finntech product using Databricks Data Intelligence Platform. The project required ingesting large volume of transaction data, cleaning it and training multiple machine, learning models using historical fraud patterns. Feature like tight integration with ML flow, alone helped us avoid the usual mess of managing models across Juypter notebooks and cloud storage. It’s collaborative environment allowed our ML engineers and data scientists to work together in Databricks notebooks in the same interface. Additionally, the ability to schedule retraining jobs made it easier to put a model into production with minimum effort. Review collected by and hosted on G2.com.

While MLflow is great, the UI for comparing runs can feel a bit outdated and lacks advanced filtering options. Managing features stores also felt slightly inefficient without more granular access control for different user roles. Review collected by and hosted on G2.com.

In-memory processing offering a major advantage: it offers a wide range of layering capabilities in terms of blocks. It's excellent because of its distributed data structures. I like that ti can be configured to perform automatic backups. Review collected by and hosted on G2.com.

The search options for in-memory data structures are poor, and the documentation isn't as good as that of other platforms. Implementing the tool requires changes to the application. Review collected by and hosted on G2.com.

I used the Databricks Data Intelligence Platform to build a real-time vehicle processing pipeline for a client in the logistics sector. The project involved collecting sensor data from hundreds of delivery trucks and processing it to detect anomalies and trigger alerts in real time. What I like best was how easily Databricks integrated with Azure Event Hubs because it allows me to stream data in and start processing instantly. I also used Delta Lake for storing clean data, which became the foundation of our analytic dashboard. Additionally, the Databricks rest API allowed us to trigger jobs from our monitoring systems, which enhances the automation to a great extend. Review collected by and hosted on G2.com.

Although the product is powerful, the learning curve for structured streaming was quite steep for new team members. I also encountered some integration limitations while sending alerts directly to external APIa from within notebooks. But we found workaround which involves using Azure Functions outside Databricks, which added some extra complexity. Review collected by and hosted on G2.com.

I really liked how easy it is to collaborate on shared notebooks, along with the native support for Spark and integration with MLflow. It allows you to work with large volumes of data without worrying about the underlying infrastructure. Review collected by and hosted on G2.com.

It could be said that the learning curve might be better for new users or those who have no previous experience with distributed environments. Review collected by and hosted on G2.com.

The single word I can say about Data bricks is they have really top class services in the field of data management. Their platform have power to handle huge data with very simple modules without any extra integrations. The services we are using from Data bricks gives freedom to handle data on every single steps. Review collected by and hosted on G2.com.

Whenever we need to store data and analyze the data we use Data bricks analytical views for that and their really gives best insights from the data. Review collected by and hosted on G2.com.

Databricks is an excellent choice if you’re looking to unify data engineering, machine learning, and analytics on a single platform. It’s especially powerful for collaborative data science with built-in support for notebooks, scalable Spark processing, and seamless integration with tools like Delta Lake and MLflow. Highly recommended for teams working with big data or AI at scale. Review collected by and hosted on G2.com.

Nothing, it’s a great platform that delivers fast Solutions Review collected by and hosted on G2.com.

Data access is high-speed, and it has highly qualified resources. I like the ability to manage large amounts of business data, its user interface is very useful, and data analysis is a faster process. I like the ability to host modules for so many different parts. Review collected by and hosted on G2.com.

They require extensive experience to configure, and the quality module could be improved with better features. They require specific administration skills, making it difficult to manage application users. Review collected by and hosted on G2.com.

They have user friendly interface and ease to integrate option that gives very effective and user friendly experience to work on databricks to manage and analyze our company huge datasets. Review collected by and hosted on G2.com.

I love their customer support because whenever I need and help regarding integration their team help me to fix my issues. Review collected by and hosted on G2.com.

It accelerates development cycles, improves scalability, supports real-time insights, and empowers users to harness the full potential of big data and AI. It's a powerful tool for turning raw data into actionable insights quickly. Review collected by and hosted on G2.com.

It takes some time to learn all the available features. Review collected by and hosted on G2.com.