ETL developers specialize in the data extraction, transformation, and loading process, focusing on preparing data for analysis. Data Engineers, on the other hand, work on a broader scope that includes designing and maintaining the overall data architecture, including ETL processes.
Python is increasingly important for ETL developers due to its versatility in handling data manipulation, automation of ETL pipelines, and integration with data processing frameworks.
Yes, modern ETL tools and frameworks are equipped to handle real-time data integration, enabling businesses to process and analyze data as it is generated.
Key considerations include the tool's compatibility with existing systems, its ability to handle the required data volume and complexity, ease of use, performance, and cost.
Cloud computing offers scalable and flexible resources for ETL processes, allowing developers to manage larger datasets more efficiently and cost-effectively, and to leverage advanced analytics and AI services.