Azure Databricks vs. Azure OpenAI Service

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

If you're looking for a managed OpenAI API service, then Azure OpenAI Service is a good choice.

It's fully compatible with OpenAI API, has lots of models to choose from, lots of parameters to configure to suite your needs.

The documents are well maintained, with examples to get started.

You can also setup firewall to restrict access to the API to certain IP addresses, like those of your VPCs.

Incentivized

• SQL
• Data management
• Data access

Incentivized

• Generating SQL
• Generating Images
• Sensible Response

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

• The user interface is similar to the Azure, so it's bit laggy, they should improve it.
• The chat models sometimes return nothing, I guess they are still testing them.
• The models we use have quota limitation on tokens and number of requests.

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

I think it's a good product and appreciate the addition secure guard rails that running it in Azure provide. However, I still struggle at times to get to the right resources for support and region-based capacity can also be a challenge.

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

1. Open AI is best at giving accurate answers. 2. It is secure and more trustworthy 3. Most of our client using Azure cloud so it becomes go to choice for them. 4. Scalable as it handles 1000s of request per minute. 5. SDKs are easy to use and well documented.

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

• Our test group of 100 people created more than 200 conversations within the first day
• People were excited & asked to be included to the test group once words spread that we have an internal ChatGPT portal
• People started to create & share their own assistants (prompt engineering) for various purposes like code review, marketing, email, etc.

Incentivized