Automated Data Ingestion and Preprocessing with Azure Data Factory
Question
Your task is to gather data from several company data sources, so that it could be available for machine learning models.
Data is coming from disparate sources and you need to find a solution to create standardized flows for ingesting and preprocessing in an automated way.
The transformations you have to incorporate in the ETL process are typically rather complicated, long-running (sometimes over 30 mins) processes.
You decide to use Azure Data Factory, due to its versatility in supported methods.
Which combination of tools would support your task in a most cost-effective way?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect because the serverless solution of Azure Functions are excellent means for short-running (less than 15 mins) processes.
Since your transformation processes are “complicated and long-running”, this is not an option in this case.
Option B is CORRECT because the most effective (and also cost-effective) way of solving the problem is to add a Custom Activity to your ADF pipeline and add the transformation logic in the format of a custom Python script.
Option C is incorrect because while being very powerful, creation of the Databricks infrastructure takes up time, and using it can be expensive.
It is a perfect choice for distributed data processing at scale but with high cost implications.
Option D is incorrect because Data Factory's Data Flow activity is very useful in cases when simple transformations (like mapping) are needed.
For complicated, more sophisticated transformation scenarios enhance ADF flows with custom code extensions.
Diagram - Azure Data Factory with Custom Activity.
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Reference:
Based on the requirements outlined in the question, the most suitable tool for automating the ETL process and preprocessing of data from disparate sources in Azure is Azure Data Factory (ADF). ADF provides a versatile and cost-effective way to build scalable and reliable data integration pipelines that can transform and move data between various sources and destinations.
The question offers four different options for incorporating the required transformations in the ETL process: Azure Function Activity, Custom Activity, Azure Databricks Notebook Activity, and Data Flow Activity. Let's take a closer look at each option and how they might support the task at hand:
A. ADF with Azure Function Activity Azure Function Activity is a serverless compute option provided by Azure that enables users to run small pieces of code or scripts that can be triggered by an event. The code can be written in different languages, such as C#, Java, or Python.
In this scenario, we could use Azure Function Activity in ADF to perform specific transformation tasks, such as data normalization or data aggregation. However, it may not be the best fit for more complicated, long-running transformation processes. Additionally, using Azure Function Activity may require additional development work and maintenance, as well as the need to consider the costs of running the compute resources for each execution of the function.
B. ADF with Custom Activity Custom Activity is another feature provided by ADF that allows for custom code or scripts to be executed as part of a pipeline. It can be used to integrate with third-party APIs, execute scripts or commands, or run custom code.
This option could be a viable solution for the ETL process as it allows for customized transformations to be executed, but it also requires a higher level of development effort to create and maintain the custom code or scripts. This option may not be the most cost-effective due to the increased development and maintenance costs, as well as the need to provision and manage compute resources.
C. ADF with Azure Databricks Notebook Activity Azure Databricks Notebook Activity is an ADF feature that allows for the execution of Azure Databricks notebooks, which are interactive documents that can contain code, visualizations, and narrative text.
This option provides a more streamlined approach to transformation tasks and may be a good fit for the long-running, complex transformation processes required in the ETL process. It also provides a managed compute environment that is scalable and cost-effective. However, it may require additional setup and configuration to integrate with ADF and may involve a learning curve to work with Azure Databricks and its notebooks.
D. ADF with Data Flow Activity Data Flow Activity is a new feature in ADF that enables the creation of visually designed data transformation pipelines with a drag-and-drop interface. Data Flow Activity provides a low-code option for building complex data transformations and allows for the integration of data from multiple sources.
This option is the most suitable for the given scenario as it provides a cost-effective, scalable, and efficient approach to automate the ETL process and perform complex data transformations. With Data Flow Activity, users can create custom data transformations using a visual interface without the need for coding or custom development work. Data Flow Activity also provides native integration with Azure services such as Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Database.
In conclusion, the most cost-effective option for automating the ETL process and preprocessing data from multiple sources using Azure Data Factory is option D: ADF with Data Flow Activity. This option provides a user-friendly interface for designing complex data transformations and provides native integration with Azure services, making it the most suitable option for the given scenario.