Azure Batch vs. Azure Databricks

To better serve their consumers, businesses that often interact with those clients who rely on Microsoft's software products may consider migrating to Azure. This program would be useful in any installation of a Microsoft product or suite that necessitates a test of the target environment. It is simple to maintain and implement, making it an ideal IT backbone. If a client doesn't have any use for this particular instrument, it's not going to be of any benefit to them.

Incentivized

Suppose you have multiple data sources and you want to bring the data into one place, transform it and make it into a data model. Azure Databricks is a perfectly suited solution for this. Leverage spark JDBC or any external cloud based tool (ADG, AWS Glue) to bring the data into a cloud storage. From there, Azure Databricks can handle everything. The data can be ingested by Azure Databricks into a 3 Layer architecture based on the delta lake tables. The first layer, raw layer, has the raw as is data from source. The enrich layer, acts as the cleaning and filtering layer to clean the data at an individual table level. The gold layer, is the final layer responsible for a data model. This acts as the serving layer for BI For BI needs, if you need simple dashboards, you can leverage Azure Databricks BI to create them with a simple click! For complex dashboards, just like any sql db, you can hook it with a simple JDBC string to any external BI tool.

Incentivized

• Managing the users
• Having multiple environments
• Creating multiple groups

Incentivized

• SQL
• Data management
• Data access

Incentivized

• The user interface, in my opinion, might need further clarification.
• Any situation where a user's password has to be reset would benefit from this feature.
• Any accounts that were accidentally established more than once may be transferred over quickly and easily.

Incentivized

• Their pipeline workflow orchestration is pretty primitive. Lacks some common features
• Workspace UI and navigation requires steep learning curve
• Personally, I am not fond of their autosave feature. Its dangerous for production level notebooks scripts

Incentivized

Based on my extensive use of Azure Databricks for the past 3.5 years, it has evolved into a beautiful amalgamation of all the data domains and needs. From a data analyst, to a data engineer, to a data scientist, it jas got them all! Being language agnostic and focused on easy to use UI based control, it is a dream to use for every Data related personnel across all experience levels!

Incentivized

They both are great tools and provide the services they have implemented. They are two competing companies that have different cultures and forward mission agendas. I would say Azure is a little easier to support through their user interface for the IT support side of things. Both tools are useful and have their own strength and weakness. If you're a dynamic company with a multitude of customers then both are a required tool to have.

Incentivized

Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!" Far ahead of the competition, the delta lakehouse platform also fares better than it counterparts of Iceberg implementation or a loosely bound Delta Lake implementation of Synapse

Incentivized

• After initial setup, we now have significantly less time spent processing data.
• The automation of data formatting and display after processing is exciting because it frees us to focus on the data itself.
• Since using Batch, we have significantly decreased the number of items we previously utilized.

Incentivized

• Helped reduce time for collecting data
• Reduced cost in maintaining multiple data sources
• Access for multiple users and management of users/data in a single platform

Incentivized