Azure Databricks: A Data Consultant's Dream
October 07, 2024

Azure Databricks: A Data Consultant's Dream

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Azure Databricks

As a Big Data Consultant. Azure Databricks is my favorite tool in the house!
The biggest problems with data consulting is a plethora of programming languages it deals in, from SQL, Scala,R, Python, Java etc.
That is exactly where Azure Databricks excels! It supports all languages in a single notebook with an equivalent performance for all! Club that with a visually pleasing UI, features that integrate the entire data lifecycle, and an architecture that gets the best of spark and you have one of the best data tools in your hand!

Pros

  • Data Processing and Transformations based on Spark
  • Delta Lakehouse when clubbed with an external cloud storage
  • Governance using Unity Catalog to unify IAM
  • Delta Live Tables is a product, which although relatively newer, has a great potential with the visuals of a pipeline.

Cons

  • The new UI is a bit clunky compared to the old UI. It also adds new elements in the sidebar which are not relevant to the workspace. Can be worked upon
  • Delta Live Tables, although powerful, has a lot of things that can be improved, including error debugging, support for new things
  • Concurrent requests need some more optimisation and work in the delta lake tables.
  • The support team is amazing, they help you at every stage of the projects, from sales to delivery.
  • On a framework level, it has had an amazing impact and has reduced the clients overall data platform costs by a staggering 65%
  • There has been a 40% Manual work requirement on average for the clients when they move to Azure Databricks Data Platform
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!
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

Do you think Azure Databricks delivers good value for the price?

Yes

Are you happy with Azure Databricks's feature set?

Yes

Did Azure Databricks live up to sales and marketing promises?

Yes

Did implementation of Azure Databricks go as expected?

Yes

Would you buy Azure Databricks again?

Yes

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.

Azure Databricks Feature Ratings

8.0
Connect to Multiple Data Sources
6
Extend Existing Data Sources
9
Automatic Data Format Detection
9
MDM Integration
8
6.5
Visualization
6
Interactive Data Analysis
7
8.0
Interactive Data Cleaning and Enrichment
7
Data Transformations
9
Data Encryption
9
Built-in Processors
7
8.3
Multiple Model Development Languages and Tools
8
Automated Machine Learning
9
Single platform for multiple model development
8
Self-Service Model Delivery
8
8.5
Flexible Model Publishing Options
8
Security, Governance, and Cost Controls
9

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