Data Engineering: Using ETL as Data Integration Strategy

Let’s look at the conceptualization of ETL in the context of an architecture that combines Azure Data Factory, Snowflake and SnowPipe.

ETL is a structured workflow. It involves gathering data from multiple sources, harmonizing it into a standardized format, and subsequently loading it into a lake/warehouse i.e. our target system, which, in our case, is Snowflake.

The ETL process can be systematically dissected into three primary stages:

1. Extract: ETL tools, like Azure Data Factory, play a pivotal role in this stage by extracting data while preserving its integrity and preparing it for downstream processing. ADF acts as the orchestration hub for this extraction, ensuring the seamless collection of data from diverse sources.
2. Transform: Transformation encompasses operations such as data cleansing, validation, enrichment, and mapping. In this context, ADF, coupled with SnowPipe (Snowflake’s data ingestion service), can be employed to process, enrich, and standardize data as it moves into Snowflake. SnowPipe’s continuous data loading capabilities align well with Azure Data Factory’s transformation capabilities, ensuring that data is not only made consistent but also optimized for analytics within Snowflake.
3. Load: The transformed and harmonized data…