It's good practice to initially load data to a transient table, balancing the need for speed, resilience, simplicity, and reduced storage cost. In common with all analytics platforms, the data engineering phases include:ĭata Staging: This involves capturing and storing raw data files on cloud storage, including Amazon S3, Azure Blob or GCP storage.ĭata Loading: This involves loading the data into a Snowflake table, which can be cleaned and transformed. This can enrich existing information with additional externally sourced data without physically copying the data. The Snowflake Data Exchange or Marketplace provides instant access to data across all major cloud platforms (Google, AWS or Microsoft) and global regions. SaaS and Data Applications: This includes existing Software as a Service (SaaS) systems, for example, ServiceNow and Salesforce, which have Snowflake connectors and other cloud-based applications.ĭata Files: Include data provided from either cloud or on-premises systems in various file formats, including CSV, JSON, Parquet, Avro and ORC, which Snowflake can store and query natively.ĭata Sharing: Refers to the ability for Snowflake to expose read-only access to data on other Snowflake accounts seamlessly. This can include data from Internet of Things (IoT) devices, Web Logs, and Social Media sources. Streaming Sources: Unlike on-premises databases, where the data is relatively static, streaming data sources constantly feed in new data. These can (for example) include billing systems and ERP systems used to manage business operations. On-Premises Databases: These include both operational databases, which generate data, and existing on-premise data warehouses, which are in the process of being migrated to Snowflake. Typically, data is stored in S3, Azure or GCP cloud storage in CSV, JSON or Parquet format. The diagram above shows the data sources, which may include:ĭata Lakes: Some Snowflake customers already have an existing cloud-based Data Lake, which acts as an enterprise-wide store of raw historical data that feeds both the data warehouse and machine learning initiatives. There are several components, and you may not use all of them on your project, but they are based on my experience with Snowflake customers over the past five years. The diagram below illustrates the Snowflake ETL data flow to build complex data engineering pipelines. The main steps include data loading, which involves ingesting raw data followed by cleaning, restructuring, enriching the data by combining additional attributes, and finally, preparing it for consumption by end users.ĮTL and Transformation tend to be used interchangeably, although the transformation task is a subset of the overall ETL pipeline. For the purposes of this article, Data Engineering is the process of transforming raw data into useful information to facilitate data-driven business decisions. What are Snowflake Transformation and ETL?ĮTL or ELT (Extract Transform and Load) are often used interchangeably as a short code for data engineering. In this article, we will describe the data Snowflake transformation landscape, explain the steps and the options available, and summarize the data engineering best practices learned from over 50 engagements with Snowflake customers.
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