Azure Databricks is our primary platform for building scalable data engineering and analytics pipelines for enterprise customers. We use it to inject, transform, and process large volumes of structured and unstructured data, then prepare curated data sheets for BI reporting, machine learning, and real analytics. One project that stands out involved building a centralized data platform for a retail customer where we ingested POS, e-commerce, and inventory data into Azure Databricks. Using Spark and Delta tables, we transformed the data into curated data sheets that powered Power BI dashboards and demand forecasting models, reducing data processing time from several hours to under an hour.
My main use case for Azure Databricks is integrating different types of sources such as Oracle ERP, SAP ERP, API, SQL server, and Oracle databases, focusing on an ingestion platform, integrating and ingesting data into the raw layer, transforming that into a silver layer, and then creating a golden layer for the transformed data to be consumed by Power BI. When I open Azure Databricks, the first thing I typically do is determine the services or applications to create a first end-to-end architecture platform using Azure services, decide which services are best for ingestion and transformation into the silver and golden layers, and then build a proof of concept to implement an end-to-end framework, which will guide the successful delivery of the entire project in a regular stream. 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.
I think this is related to our internal business, as we have a data warehouse and data lakes that we use Azure Databricks for. We work for Bosch, and we have the solution internally. We purchase everything through a big IT organization.
Senior Business Intelligence Consultant at Stellar Consulting Group
MSP
Top 20
Mar 3, 2026
The primary use cases for me are the reportings I have to do, so I need to ingest data from the file and create reports. I do not utilize it for real-time data processing. I have not integrated Azure Databricks with other Azure services, just Excel mainly; I put data in Excel, load it, ingest it in Azure Databricks and then use it.
I have constructed the Lakehouse on Azure Databricks for ETL, machine learning, deep learning, and all data science purposes. Azure Databricks is a very powerful tool when it comes to handling all these applications, and the Spark framework stands out significantly. I utilize Azure Databricks for real-time data processing. Regarding the collaborative features of Azure Databricks, they are working on it regularly. At this point in time, it is collaborative, and spinning up your own cluster is also easier. Comparatively, Snowflake is doing a little bit better based on my experience. I cannot judge on other people's experiences, but I feel Snowflake does a little bit better compared to Azure Databricks at this point in time. However, Azure Databricks itself has good enough collaboration. I have integrated Azure Databricks with Power BI. With integration of Azure Databricks, we use Power BI, Azure ML Studio, and Azure Data Factory. There are multiple other tools which we integrate as well. Additionally, with external tools outside Microsoft, such as Tableau, you can integrate. There are quite a lot of things you can do on top of Azure Databricks. We use Azure Databricks mostly for ETL purposes. We use programming languages such as both SQL and Python. Scala is also possible, but I have never experienced Scala at this point in time. This is what its power is; it is mostly a code-based platform. There is something called agents, and also you have a capability recently developed called Agent Bricks, which is the GenAI capability that they are bringing up.
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...
Azure Databricks is our primary platform for building scalable data engineering and analytics pipelines for enterprise customers. We use it to inject, transform, and process large volumes of structured and unstructured data, then prepare curated data sheets for BI reporting, machine learning, and real analytics. One project that stands out involved building a centralized data platform for a retail customer where we ingested POS, e-commerce, and inventory data into Azure Databricks. Using Spark and Delta tables, we transformed the data into curated data sheets that powered Power BI dashboards and demand forecasting models, reducing data processing time from several hours to under an hour.
My main use case for Azure Databricks is integrating different types of sources such as Oracle ERP, SAP ERP, API, SQL server, and Oracle databases, focusing on an ingestion platform, integrating and ingesting data into the raw layer, transforming that into a silver layer, and then creating a golden layer for the transformed data to be consumed by Power BI. When I open Azure Databricks, the first thing I typically do is determine the services or applications to create a first end-to-end architecture platform using Azure services, decide which services are best for ingestion and transformation into the silver and golden layers, and then build a proof of concept to implement an end-to-end framework, which will guide the successful delivery of the entire project in a regular stream. 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.
I think this is related to our internal business, as we have a data warehouse and data lakes that we use Azure Databricks for. We work for Bosch, and we have the solution internally. We purchase everything through a big IT organization.
The primary use cases for me are the reportings I have to do, so I need to ingest data from the file and create reports. I do not utilize it for real-time data processing. I have not integrated Azure Databricks with other Azure services, just Excel mainly; I put data in Excel, load it, ingest it in Azure Databricks and then use it.
I have constructed the Lakehouse on Azure Databricks for ETL, machine learning, deep learning, and all data science purposes. Azure Databricks is a very powerful tool when it comes to handling all these applications, and the Spark framework stands out significantly. I utilize Azure Databricks for real-time data processing. Regarding the collaborative features of Azure Databricks, they are working on it regularly. At this point in time, it is collaborative, and spinning up your own cluster is also easier. Comparatively, Snowflake is doing a little bit better based on my experience. I cannot judge on other people's experiences, but I feel Snowflake does a little bit better compared to Azure Databricks at this point in time. However, Azure Databricks itself has good enough collaboration. I have integrated Azure Databricks with Power BI. With integration of Azure Databricks, we use Power BI, Azure ML Studio, and Azure Data Factory. There are multiple other tools which we integrate as well. Additionally, with external tools outside Microsoft, such as Tableau, you can integrate. There are quite a lot of things you can do on top of Azure Databricks. We use Azure Databricks mostly for ETL purposes. We use programming languages such as both SQL and Python. Scala is also possible, but I have never experienced Scala at this point in time. This is what its power is; it is mostly a code-based platform. There is something called agents, and also you have a capability recently developed called Agent Bricks, which is the GenAI capability that they are bringing up.