Data lakes solve a significant challenge facing today’s data professionals: analyzing data at scale in a way that’s both efficient and cost effective. Snowflake empowers businesses to make the most of their data, providing a cost-effective and efficient means for storing and working with data in its native format. Let’s explore why data lakes are a popular data management architecture and how Azure Data Lake users are getting more from their data with Snowflake.
Why Data Lakes?
A data lake is a repository for storing data as files in a variety of structures and formats. As such, a data lake can support all types of data. To analyze data in a data lake, files are scanned and aggregated based on specific criteria from a query, and returned to the user for analysis. Storing data in its raw format gives data professionals more flexibility with advanced analytics applications.
Snowflake on Azure for Data Lakes
Microsoft Azure users can gain value from their data lake either by ingesting to Snowflake for the best performance, security, and automatic management, or query in place and still benefit from Snowflake’s elastic engine, native governance, and collaboration capabilities. Azure Data Factory (ADF) is an end-to-end data integration tool you can use to bring data from Azure Blob Storage or Azure Data Lake Storage into Snowflake for more-efficient workloads. The native Snowflake connector for ADF currently supports three primary activities:
The Copy activity is the primary player in an ADF pipeline. It is used to copy data from one data source (called a source) to another data source (called a sink). This activity provides over 90 different connectors to data sources, one of them being Snowflake. In the Copy activity, Snowflake can serve as both a source or a sink. This makes it simple to ingest data from almost all data sources directly into Snowflake.
The Lookup activity is another useful feature of ADF. Using this activity, users can retrieve a small number of records from any data source ADF supports. The main benefit of the Lookup activity is to read metadata from configuration files and tables. Once created, these can be used to create dynamic, metadata-driven pipelines. Although you can call a stored procedure from the Lookup activity, Microsoft discourages the use of the Lookup activity to call a stored procedure to modify data. Instead, users attempting to execute a stored procedure to modify data should consider the Script activity covered in the next section.
The Script activity allows users to run a series of SQL commands against Snowflake. This capability makes it possible to execute data manipulation language (DML) statements and data definition language (DDL) statements. This activity also enables the execution of stored procedures. Users now have the flexibility to transform the data loaded into Snowflake’s optimized storage while pushing all the compute down to Snowflake’s elastic engine. The Script activity enables the creation of end-to-end pipelines with Snowflake, unlocking exciting possibilities for Azure users to boost their data lake performance.
Why Snowflake for Data Lakes
With a cloud-built architecture, Snowflake enables organizations to strengthen their data lake with various architectural patterns. Snowflake makes it simple to mix and match the components of data lake design patterns to unlock the full value of your data. Here’s why Snowflake is ideal for data lakes.
Exceptional Query Performance
With support for a virtually unlimited number of concurrent users and queries with near-unlimited, dedicated compute resources, Snowflake reduces time to insight, empowering organizations to use their data to meet business objectives. With virtually all of your organization’s data available to a near-unlimited number of users, data can be quickly deployed to solve complex business problems. Snowflake enables efficient data exploration, with instant and near-infinite scalability and concurrency.
Integrated and Extensible Data Pipelines
Optimize performance with streamlined data pipeline development. Snowflake users can depend on their data pipelines scaling when needed, in real time, to seamlessly accommodate heavy data workloads and extensible data transformations. With Snowpark, data engineers can seamlessly build pipelines in their preferred language: SQL, Python, Scala, or Java.
Secure, Governed Collaboration
Organizations rely on Snowflake to meet the governance and security standards required for collaborative data preparation, exploration, and analytics regardless of where your data resides. Our robust security and governance tools ensure that sensitive data maintained by your organization remains protected from unauthorized access and tampering, helping you achieve and maintain regulatory compliance.
Snowflake on Azure for Data Lakes
Using Snowflake helps businesses on Azure maximize the value of the modern data lake architecture, even across clouds. Whether for the data lake or the data warehouse, Snowflake on Azure allows you to unite your technology stack in a single platform to support a variety of data workloads, while also enabling cross-cloud collaboration in Snowflake’s Data Cloud. Thanks to Snowflake’s fully managed service for storage allocation, capacity planning, and concurrency, organizations can focus on gaining value from their data, not managing it. With near-infinite data storage capacity and dynamically scalable compute power, you’ll have access to the resources you need, when you need them.