Data engineers are critical for companies using cloud-based data warehousing platforms like Snowflake. As data volume and complexity increases, it becomes even more vital for data engineers to understand Snowflake architecture, performance characteristics, and best practices.
To fully utilize Snowflake's capabilities, a Snowflake data engineer should follow these eight simple yet effective tips to optimize performance and manage its data efficiently. These tips range from developing a deep understanding of Snowflake architecture to leveraging unique features for the storage and retrieval of data. Whether you are an experienced or new Snowflake data engineer, following these tips will help maximize the value of Snowflake for any organization.
A Snowflake data engineer needs to develop a deep understanding of Snowflake architecture and features to make the best use of the platform. Snowflake is an efficient alternative to traditional data warehouse architecture. Understanding its architecture can help data engineers plan, create, and maintain data architectures that are aligned with business needs.
Data engineers must collect, transform, and deliver data to different lines of business while keeping up with the latest technological innovations. Efficient data pipelines can be the difference between an architecture that provides up-to-the-moment insight and one that falls behind in business demands.
Snowflake's Data Cloud is powered by a feature-rich, advanced data platform provided as a self-managed service. Understanding its key concepts and features can help data engineers build efficient pipelines for ingesting structured, semi-structured, and unstructured data.
Snowflake architecture allows organizations to store a wide variety of data formats and data types. Using appropriate data types can help data engineers create insight-rich data, utilizing a data preparation platform like Designer Cloud.
With certain restrictions or provisos, Snowflake supports most basic SQL data types for use in columns, local variables, expressions, parameters, and other suitable locations. In some cases, data engineers can convert data from one type to another. However, some conversions might result in a loss of information. Using appropriate data types can help minimize information loss.
Using appropriate data types and sizes can help a Snowflake data engineer create more efficient and reliable pipelines for ingesting, transforming, and delivering data.
For efficient use of the Snowflake platform, it is vital for data engineers to learn to leverage Snowflake’s automatic clustering and partitioning features. Clustering helps improve query performance by avoiding unnecessary scanning of micro-partitions during querying. This process significantly improves data scanning efficiency.
All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage. Each micro-partition contains between 50 MB and 500 MB of uncompressed data, with the actual size of the data being smaller due to Snowflake always storing data in compressed form.
Snowflake’s Automatic Clustering service seamlessly and continually manages all re-clustering, as needed, of clustered tables. Suppose you enable or resume Automatic Clustering on a clustered table. In that case, if the last re-clustering instance happened a while ago, you may experience new re-clustering activity as Snowflake brings the table to an optimal state. Credit charges can also apply accordingly.
Leveraging Snowflake’s automatic clustering and partitioning features can help data engineers improve query performance by avoiding unnecessary scanning of micro-partitions during querying.
Another essential resource for data engineers is Snowflake’s Time Travel feature. Time Travel enables access of historical data, or data that has been deleted or transformed in any way, at any point within a defined period. This is a powerful tool for restoring data-related objects, including tables, schemas, and databases, that might have been accidentally or intentionally deleted.
A primary component of Snowflake Time Travel is the period of data retention. When any data in a table is modified, including the deletion of data or the dropping of an object containing data, Snowflake preserves the state of the data before the update.
Snowflake’s Time Travel feature can help data engineers access historical data and restore accidentally or intentionally deleted objects. This can be a powerful tool for ensuring that valuable business insights are not lost due to accidental deletions or mistakes.
Data engineers can utilize Snowflake’s Zero Copy Clone feature for a variety of use cases. This feature allows you to take a snapshot of any table, schema, or database at any time and generate a reference to an underlying partition that originally shares the underlying storage until you make a change.
Zero Copy Clone provides low-cost test environments that better match source systems. It allows data engineers to create copies of tables, schemas, or databases and use them for testing and development without incurring additional storage costs.
Snowflake's Zero Copy Clone feature can help data engineers create low-cost test environments and quickly create copies of tables, schemas, or databases without incurring additional storage costs. This can be a powerful tool for ensuring that changes are thoroughly tested before being deployed to production environments.
Also read: Data Engineer - Top 31 Interview Questions
A Snowflake data engineer plays a crucial role in managing costs and preventing unexpected credit usage caused by running warehouses. They do this by using Snowflake's resource monitors. A virtual warehouse, by nature, consumes Snowflake credits while it runs. A resource monitor can help data engineers keep a handle on credit usage by virtual warehouses and the cloud services needed to support those warehouses.
Resource monitors assist in cost management and prevent unforeseen credit usage caused by operating warehouses. They issue alarm alerts and help stop user-managed warehouses when certain limits are reached or approaching.
A Snowflake data engineer is also expected to have intimate knowledge about optimizing the retrieval of data and developing dashboards, reports, and other visual representations. A Snowflake data engineer also communicates data trends to business executives as part of job responsibility.
Taking advantage of Snowflake's automatic query optimization is vital for Snowflake data engineer. Snowflake automatically optimizes the data storage and querying process. Instead of worrying about performance and optimization, data engineers can focus on building the best possible model.
The cloud services layer of Snowflake does all the query planning and query optimization based on data profiles that are collected automatically as the data is loaded. Snowflake automatically optimizes these queries by returning results from the Results Cache, with results available for 24 hours after each query execution.
Snowflake’s automatic query optimization allows for high-speed analytic queries without manual tuning. Taking advantage of automatic query optimization allows a Snowflake data engineer to focus on building the best possible model while optimizing data retrieval.
Implementing proper data governance and security measures is crucial for every Snowflake data engineer. Data governance is an organization's management of its data availability, usability, consistency, integrity, and security. Effective data governance translates into consistent, trustworthy, and secure data.
Snowflake's cloud data platform provides the proper foundation for data governance programs. Snowflake helps companies break down data silos and has features that enable companies to achieve compliance as well as better decision-making using secured, governed data. While many data governance controls require manual auditing and validation, Snowflake provides functionality to reduce the number of controls needed. In some instances, Snowflake can also automate data governance controls.
In summary, implementing proper data governance and security measures is an important responsibility of every Snowflake data engineer. They can use Snowflake's features to achieve compliance and better decision-making using secured, governed data.
Protecto offers a large selection of data management and security solutions for data engineers and organizations using Snowflake. Most importantly, Protecto has a direct partnership with Snowflake that allows customers to use Protecto's modern AI-driven privacy engineering and intelligent data tokenization solutions on Snowflake datastore.
Data engineers leveraging Snowflake can significantly reduce or eliminate the time spent managing infrastructure, including tasks such as concurrency handling and capacity planning. This allows them to shift their focus towards efficiently delivering data to the appropriate stakeholders.
A Snowflake data engineer plays a crucial role in the efficient management and optimization of Snowflake's cloud data platform. Their primary responsibility involves designing, implementing, and maintaining the data infrastructure within Snowflake architecture to ensure its smooth operation and high-performance capabilities. This includes tasks such as data modeling, schema design, and creating data pipelines for seamless data ingestion and integration.
Snowflake provides robust security features like encryption, access controls, and multi-factor authentication. Data engineers can configure security settings and collaborate with security teams to enforce compliance.
Snowflake data engineers design and implement data pipelines, perform data modeling, optimize query performance, manage data ingestion, and collaborate with data analysts and scientists to ensure data availability.