TrustRadius Insights for Databricks Data Intelligence Platform are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
User-Friendly SQL: Users have found the SQL in Databricks to be user-friendly, allowing them to easily write and execute queries. Several reviewers have praised the intuitive nature of the SQL interface, making it accessible for users of different skill levels.
Enhanced Collaboration: The enhanced collaboration between data science and data engineering teams is seen as a positive feature by many users. They appreciate how Databricks facilitates seamless communication and knowledge sharing among team members, ultimately leading to improved productivity and efficiency.
Versatile Integration: The integration with multiple Git providers and the merge assistant is highly valued by users. This feature allows for smooth version control and simplifies the collaborative development process. With this capability, developers can easily manage their codebase, track changes, resolve conflicts, and ensure a streamlined workflow.
We use Databricks Lakehouse Platform to transform IoT data and build data models for BI tools. It is being used by engineering and IT teams. We use it with a data lake platform, read the raw data and transform it to a suitable format for analytics tools. We run daily/hourly jobs to create BI models and save the resulting models back to data lake or SQL tables.
Pros
Ready-2-use Spark environment with zero configuration required
Interactive analysis with notebook-style coding
Variety of language options (R, Scala, Python, SQL, Java)
Scheduled jobs
Cons
Random task failures
Hard to debug code
Hard to profile code
Likelihood to Recommend
It is great for both ad-hoc analyzes and scheduled jobs. It supports most of the cloud storage technologies and provides an easy to use API to connect with them. Clusters can be auto scaled with the load, and you can also create temporary clusters for job runs, which cost less compared to all purpose clusters.
Production Environment Customer Facing Analytic Services
Pros
Collaborative Development Environment using Notebooks.
Stable and Secure Cloud Development Environment requiring minimum DevOPs support
Fast with excellent scalability reduces time to market
Open source library support
Cons
Automation of Machine Learning Development
Optimization of GPU usage
Likelihood to Recommend
Great end to end analytics solution on AWS or Azure. Databricks continues to grow based on customer feedback. Just like everyone in the industry, they are focused on Machine Learning, but they also understand a complete solution is needed.
VU
Verified User
Strategist in Engineering (Computer Hardware company, 10,001+ employees)