Are you preparing for an interview as an ETL developer using the Microsoft Data Platform? In this article, we will discuss some of the technical questions you can expect and explore important concepts and best practices related to ETL development in SQL Server.

What is ETL?

ETL stands for Extract, Transform, and Load. It is a process where data is extracted from one or more sources, transformed according to specific requirements, and loaded into a destination. The transformation step can involve cleaning up data, removing duplicates or NULL values, or applying business logic to the data. SQL Server and Azure SQL Database are commonly used as destination data stores for ETL processes, but other options like Azure Data Lake Storage or Delta Lake in Azure Databricks can also be utilized. ETL is essential in data warehouse projects and data migration/integration projects.

What is ELT and when would you use it?

ELT, which stands for Extract, Load, and Transform, is a variation of the traditional ETL process. In ELT, data is stored as-is from the source in a persistence layer, and the transformations are performed using compute resources. ELT is particularly popular in cloud scenarios as it offers better scalability compared to ETL. For example, SQL-based transformations often perform better than transformations in an SSIS data flow due to memory constraints. Tools like SSIS, Azure Data Factory (ADF), and Azure Databricks can be used to implement ELT data pipelines.

Full load or incremental load?

When designing an ETL process, you need to decide whether to load data incrementally or perform a full load each time. Incremental loading involves processing only the changed or added data, making it faster and suitable for real-time or near-real-time ETL processes. However, it can be more complex to implement and debug. On the other hand, full loads are easier to implement and troubleshoot but may encounter performance issues when dealing with large datasets. Modern data platforms can handle multi-million row inserts efficiently, making performance less of a concern for small and medium-sized datasets. It is advisable to consider both options and prioritize based on project requirements.

Cost-saving strategies for cloud-based ETL

When building ETL processes in the cloud, there are several cost-saving strategies you can consider:

• Compare prices between different Azure regions to choose the most cost-effective option.
• Check if you are eligible for the Azure hybrid benefit, which allows you to reuse your existing SQL Server license in Azure, resulting in significant cost savings.
• Evaluate whether you actually need the Enterprise edition of SSIS or if the Standard edition is sufficient for your use cases.
• Pause or scale down cloud services when they are not actively processing data to save costs. For example, you can pause Azure Synapse Analytics Dedicated SQL Pools or scale down an Azure SQL DB.
• Consider storing data in a data lake instead of a database, as storage costs are generally lower. Choose the most cost-effective compute options for your data processing needs.
• Optimize the usage of Azure Data Factory by adjusting the DIU size of the Copy Activity for small or medium-sized datasets, resulting in cost savings.

By following these guidelines, you can optimize costs while building ETL processes in the cloud.

In conclusion, understanding the concepts and best practices related to ETL development in SQL Server is crucial for success in ETL-related roles. By familiarizing yourself with ETL, ELT, load strategies, and cost-saving techniques, you will be well-prepared for interviews and equipped to build efficient and cost-effective ETL processes.

Article Last Updated: 2022-07-20