AWS Glue vs. Azure Databricks

One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.

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

• It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
• As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
• It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.

Incentivized

• SQL
• Data management
• Data access

Incentivized

• In-Stream schema registries feature people can not use this more efficiently
• in Connections feature they can add more connectors as well
• The crucial problem with AWS Glue is that it only works with AWS.

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

We give 7 rating because of usefulness in AWS world without worrying about infrastructure and services interaction, it’s pretty out of the box gives us the flexibility to interact with them and use them. we take the data source in s3 from external system and then transform it using other AWS services and putting it back for other external services to consume in S3 form.

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

Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.

Incentivized

AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.

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

• It had a positive impact on the way we build our data lake.
• It is the single source of truth for data structure (schemas/tables/views).

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