We rely on Delta Lake most because it provides ACID transactions, schema enforcement, and reliable data versioning for production data pipelines in Azure Databricks. It has significantly reduced data quality issues and makes it much easier for multiple teams to build and maintain reliable ETL workflows while supporting rollback and audit requirements. A great feature is the tight integration between Azure Databricks and the Azure ecosystem. Connecting with Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID, and Power BI is straightforward and simplifies end-to-end data platform management. We also find cluster auto-scaling very valuable because it optimizes resource usage during peak processing while helping control infrastructure costs. We measured the 50 to 60% reduction in processing time by comparing end-to-end ETL execution times before and after migrating several customer data pipelines to Azure Databricks. For example, workflows that previously took four to five hours on traditional ETL platforms consistently completed in around two hours after optimizing with Spark, Delta Lake, and auto-scaling clusters. Similar improvements were observed across multiple customer projects and not just a single deployment. The AI capabilities in Azure Databricks are reliable when the underlying data is well-governed and the models are properly trained. Azure Databricks delivers consistent results for ML workloads, and its support for versioning, reproducibility, and experiment tracking helps us maintain accuracy across production deployments. Azure Databricks provides strong governance and security through role-based access control, Unity Catalog, data lineage, and integration with Microsoft Entra ID. These capabilities help us enforce consistent access policies, protect sensitive customer data, and meet enterprise compliance requirements for AI and analytics workloads. Many of the needed improvements have already been discussed. I would rate this product an 8 out of 10 overall.
When I open Azure Databricks, my day-to-day workflow begins with validating the proof of concept framework, choosing the right service for the ingestion pattern, and then building or running notebooks for raw, silver, and gold layer transformations. I involve myself in building the data architecture, coordinating with my senior tech leads or COE groups if I have any questions to ensure we understand the required solutions, and after presenting the architecture to the customer, we progress on development and testing in the deliverables. Although Delta Live Tables and streaming services are features I discussed and planned to use during implementation, I have utilized them very minimally for my applications. My advice for someone considering Azure Databricks with a similar workflow is to ensure their solution can operate independently of cloud services, making it possible to work across Azure, GCP, or AWS, and to implement a plug-in/plug-out feature architecture that does not disrupt the overall framework. I would rate this product a seven out of ten.
Senior Business Intelligence Consultant at Stellar Consulting Group
MSP
Top 20
Mar 3, 2026
I do not use the multi-language support in Azure Databricks. I do not use any metrics as such because these are small files; I am not loading millions of records, it has just some thousands of records. The pricing of Azure Databricks is handled by the infrastructure team, so I am not involved in that; I am mainly a developer. If I have to use a warehouse which is more powerful, I need to make a request because that costs, otherwise I just do my work. I think the interface is intuitive enough. They provide online training; online training is available. I have given the product a rating of six out of ten just because I do not use all of the functionalities, and I see some direction for improvement as well; also, every product has something to improve, and I have not used many features in this product.
Azure Databricks is an advanced analytics platform combining the best of Microsoft's Azure and Apache Spark. It provides a powerful solution for big data processing, machine learning, and collaborative data projects, designed to help organizations unlock insights and foster innovation.Azure Databricks integrates seamlessly with Azure services, offering end-to-end data solutions for enterprises. Its collaborative environment supports data engineers and scientists, facilitating faster data...
We rely on Delta Lake most because it provides ACID transactions, schema enforcement, and reliable data versioning for production data pipelines in Azure Databricks. It has significantly reduced data quality issues and makes it much easier for multiple teams to build and maintain reliable ETL workflows while supporting rollback and audit requirements. A great feature is the tight integration between Azure Databricks and the Azure ecosystem. Connecting with Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID, and Power BI is straightforward and simplifies end-to-end data platform management. We also find cluster auto-scaling very valuable because it optimizes resource usage during peak processing while helping control infrastructure costs. We measured the 50 to 60% reduction in processing time by comparing end-to-end ETL execution times before and after migrating several customer data pipelines to Azure Databricks. For example, workflows that previously took four to five hours on traditional ETL platforms consistently completed in around two hours after optimizing with Spark, Delta Lake, and auto-scaling clusters. Similar improvements were observed across multiple customer projects and not just a single deployment. The AI capabilities in Azure Databricks are reliable when the underlying data is well-governed and the models are properly trained. Azure Databricks delivers consistent results for ML workloads, and its support for versioning, reproducibility, and experiment tracking helps us maintain accuracy across production deployments. Azure Databricks provides strong governance and security through role-based access control, Unity Catalog, data lineage, and integration with Microsoft Entra ID. These capabilities help us enforce consistent access policies, protect sensitive customer data, and meet enterprise compliance requirements for AI and analytics workloads. Many of the needed improvements have already been discussed. I would rate this product an 8 out of 10 overall.
When I open Azure Databricks, my day-to-day workflow begins with validating the proof of concept framework, choosing the right service for the ingestion pattern, and then building or running notebooks for raw, silver, and gold layer transformations. I involve myself in building the data architecture, coordinating with my senior tech leads or COE groups if I have any questions to ensure we understand the required solutions, and after presenting the architecture to the customer, we progress on development and testing in the deliverables. Although Delta Live Tables and streaming services are features I discussed and planned to use during implementation, I have utilized them very minimally for my applications. My advice for someone considering Azure Databricks with a similar workflow is to ensure their solution can operate independently of cloud services, making it possible to work across Azure, GCP, or AWS, and to implement a plug-in/plug-out feature architecture that does not disrupt the overall framework. I would rate this product a seven out of ten.
I gave this product a rating of eight out of ten.
I do not use the multi-language support in Azure Databricks. I do not use any metrics as such because these are small files; I am not loading millions of records, it has just some thousands of records. The pricing of Azure Databricks is handled by the infrastructure team, so I am not involved in that; I am mainly a developer. If I have to use a warehouse which is more powerful, I need to make a request because that costs, otherwise I just do my work. I think the interface is intuitive enough. They provide online training; online training is available. I have given the product a rating of six out of ten just because I do not use all of the functionalities, and I see some direction for improvement as well; also, every product has something to improve, and I have not used many features in this product.
I think Azure Databricks is a very good product. I would rate it a nine out of ten.