Snowflake is a cloud data platform that offers data storage, processing, and analytics services. It is optimized for the cloud and aims to provide a fast, scalable, and secure way to work with large amounts of data in the cloud.
One of the key capabilities that Snowflake provides is the ability to load and transform data, which is similar to some of the core functions of an extract, transform, load (ETL) tool. However, there are some important differences between Snowflake and traditional ETL tools that are worth examining to understand if Snowflake can serve as a full replacement for ETL.
What is ETL?
ETL stands for extract, transform, load. It is the process of:
- Extracting data from source systems
- Transforming the data (cleaning, applying business rules, joining etc.)
- Loading the transformed data into a target database or data warehouse
Traditional ETL tools provide graphical interfaces and pre-built connectivity to help with the extract and load steps. More importantly, they provide the ability to visually map data flows to define complex transformation logic.
Some examples of traditional on-premise ETL tools include Informatica PowerCenter, Oracle Data Integrator (ODI), and IBM InfoSphere DataStage.
Key ETL Capabilities
Some of the key capabilities expected from an ETL tool are:
- Connectivity to a wide variety of data sources such as relational databases, NoSQL databases, file shares, SaaS applications etc.
- Data transformation – Ability to join data, aggregate, de-normalize, apply business rules
- Workflow orchestration and management – Ability to define and manage ETL jobs and workflows
- Logging and error handling – Logs to track data lineage and debug errors
- Scalability and parallelism – Ability to distribute workloads across servers and process data in parallel
Snowflake’s ETL-like Capabilities
Snowflake provides some of the core ETL capabilities within its platform:
Data Loading
Snowflake can load data from a variety of sources such as AWS S3, Azure blob storage, data warehouses, cloud applications etc. It provides support for semi-structured data formats like JSON, Avro, Parquet.
The COPY command helps load data quickly into Snowflake tables. Automatic parallelism during loads helps scale for large data volumes.
Transformations
Snowflake allows data transformation operations like selecting columns, filtering rows, joining data, aggregations, pivots etc. to happen using standard SQL.
In addition, Snowflake also provides change data capture, data masking, and other data cleansing functions to help with data transformation.
Orchestration
Snowflake has tasks that allow SQL statements to be bundled together into a job and executed sequentially. These tasks can be scheduled using Snowflake’s scheduler.
In addition, tasks can be defined in Snowflake stored procedures and executed from external schedulers.
Logging & Error Handling
Snowflake provides history and monitoring of load and transformation operations. Queries, data loads, errors etc. are logged into tables that can be analyzed.
Failed loads and transformations cause errors that can be handled through object tags and exception handling in stored procedures.
Missing ETL Capabilities in Snowflake
While Snowflake does have some overlapping ETL features, there are some key ETL capabilities missing:
Limited Data Source Connectivity
Snowflake provides more limited data source connectivity compared to traditional ETL tools. While it can work with cloud storage and some SaaS applications, connecting to on-premise sources like ERPs directly may be challenging.
No Visual Workflow Designer
There is no graphical interface to design data flows and transformation logic in Snowflake. All ETL work needs to be coded through SQL and stored procedures.
Minimal Pre-Built Transformations
While SQL can handle many transformations, ETL tools have pre-built connectors, functions, and components for common ETL operations. For e.g. SCD Type 2 handling, slowly changing dimensions, de-duplication, masking etc.
No Centralized Job Monitoring
Snowflake does not have a centralized workflow monitor and scheduler to easily manage dependencies and operational governance of ETL across multiple tasks and procedures.
When Can Snowflake Replace ETL?
For simple cloud ETL use cases like loading data from S3 into Snowflake, cleansing it, and putting it into analytical tables, Snowflake may provide sufficient ETL capabilities.
However, for more complex on-premise sources, handling enterprise grade data volumes, orchestrating end-to-end workflows, and managing operational SLAs, a dedicated ETL tool would be better equipped.
Snowflake is optimal when:
- Most data sources are cloud-based
- Transformation logic is not too complex
- Data volumes are in 100s of GBs or low TBs
- Only simple orchestration of a few sequential tasks is needed
Complementing ETL Tools
A good strategy can be to leverage the strengths of both tools:
- Use ETL tool to handle complex transformation orchestration, error handling, lineage tracking
- Use Snowflake for scalable cloud data loading, storage, and core transformations
- ETL tools can leverage Snowflake for transformation processing power and avoiding data movement
Conclusion
In summary:
- Snowflake has some ETL-like capabilities but lacks key features of full-fledged ETL tools
- It works best for simple cloud ETL use cases with smaller data volumes
- For more complex on-premise sources or data flows, dedicated ETL tools would be better equipped
- Snowflake can complement ETL tools by providing fast data processing and minimizing data movement
While Snowflake handles some basic data integration tasks, it is best positioned as a cloud data platform, rather than a complete replacement for enterprise-grade ETL tools. The right strategy is to use the strengths of both technologies to build a high-performance, scalable and robust data integration pipeline.