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.
Data Science Platforms empower data analysts to develop, evaluate, and deploy analytical models efficiently. They integrate data exploration, visualization, and predictive modeling in one cohesive environment.These platforms serve as indispensable tools for data-driven decision-making, providing intuitive interfaces and scalable computing power. They enable seamless collaboration between data scientists and business stakeholders, allowing actionable insights to drive strategic initiatives...
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.