For a deep-dive into how you can build a fully automated data integration process in Snowflake on Azure, schedule a Snowflake whiteboarding session with our team of data architects.
Snowflake has been making waves this year – and with good reason: it’s the first enterprise-ready, cloud-first data warehouse. Built from the ground up with the scalability of public cloud in mind, Snowflake is a no-hassle, worry-free database for your analytics workloads. Now with its latest release on the Azure platform, the robustness of the Microsoft ecosystem, and Azure Data Factory for cloud ETL, Snowflake’s cloud data warehouse can be integrated natively into the heart of your organization’s data solution.
Existing or soon-to-become Azure customers are in for a treat. Businesses built on Microsoft SQL Server and other Azure native offerings (such as Azure Databricks) can integrate that existing investment and immediately reap the benefits of Snowflake’s lightning-fast analytics capabilities. Azure Data Factory, Microsoft’s answer to data integration and ETL orchestration in the cloud (and future successor to SSIS), is perfectly positioned for getting your data into Snowflake – here’s how.
If you’re first hearing about Snowflake today, it’s important to understand that Snowflake’s architecture differs significantly from that of SQL Server or Redshift. Snowflake on Azure stores data physically in Azure Blob Storage (or S3 on AWS) and separates its compute from the storage. This allows for instant scaling of compute resources for your workload. On top of this, it’s fully ANSI SQL compatible, requires no additional learning curve, needs little-to-no management, and supports semi-structured data. Getting raw data into Snowflake is made easy by loading it into Azure Blob storage and calling SnowSQL commands to create the metadata pointers back to your data.
We have spent the past few years investing heavily into the Aptitive ETL Framework. Originally built for SSIS and Informatica, our framework has now expanded into Azure’s PaaS offerings: SQL Server’s Machine Learning, Azure ML, and, as of this post, Snowflake on Azure.
The Aptitive ETL Framework harnesses the advantages of the Azure environment to enhance the native benefits of Snowflake. We combine the built-in connectors, flow control and parameterization capabilities of Azure Data Factory with the utility of Azure App Services to integrate data through a chain of Blob storage containers, capturing snapshots of your data through each step of the workflow. The end result is a robust data warehousing solution that is accessible, restartable, version-controlled, and 100% platform as a service – which means less code for you to manage and point-and-click scalability.
Whether you’re looking to augment your existing Azure data ecosystem, migrating from a legacy data warehouse, or starting from scratch, Snowflake on Azure is a compelling choice in the search for a modern data platform. Azure Data Factory, while complex and feature-rich, has matured to the point where it’s ready for enterprise integration. Our consultants at Aptitive have already done the hard work of building out the architecture, auditing, and services needed for this solution. With the core integration services already in place, we can start you on the road to success by focusing on solving the more complex problems required of any analytics project.
Interested in how Snowflake on Azure with Data Factory can help your business? Contact us for a complimentary Snowflake on Azure planning and whiteboarding session.