Best Snowflake Performance Tuning Tactics

on Data Automation, Data Integration, Data Warehouse, ETL, Snowflake • September 24th, 2021 • Write for Hevo

In recent years, businesses worldwide have scaled up their Data Collection operations, leading to the term ‘Big Data.’ Today, companies collect information from various sources, including Business Transactions, Industrial Equipment, Social Media, and more. Accordingly, these organizations need an efficient way of storing and analyzing this information. A decade ago, this would be a rather expensive affair as they would have to acquire the hardware for this purpose at a hefty price. Luckily, with the advent of the cloud, firms no longer have to go through this hassle as SaaS (Software-as-a-Service) offers Data Storage service.

The Cloud has led to the development of various efficient Data Warehousing solutions such as Snowflake. Snowflake has cemented its place as a top player in the world of Big Data due to its numerous features and flexibility. In this post, we will dive into the top Snowflake performance tuning tactics for you to get the best out of the platform.

Table of Contents

Introduction to Snowflake

Snowflake Performance Tuning: Logo
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In simple terms, Snowflake is a SaaS-based Data Warehouse platform built over AWS infrastructure. One of the features behind this software’s popularity with businesses worldwide is its scalability, making it cost-effective. The architecture involves virtual compute instances and efficient storage buckets that run solely on the cloud.  The Data Architecture uses the scalable, elastic Azure Blobs Storage as the internal storage engine and Azure Data Lake for storing unstructured, structured, and on-premise data ingested via the Azure Data Factory. 

Snowflake Performance Tuning: Data Architecture
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Snowflake provides security and protection of data using Amazon S3 Policy Controls, SSO, Azure SAS Tokens, and Google Cloud Storage access permissions. As mentioned, Snowflake is known for scalability, you can also scale your storage depending on your storage needs. Thus, you can enjoy scalability, data security, among many other benefits while using a Snowflake Data Warehouse to store your data.

Key Features of Snowflake 

Below are some of the key features of Snowflake:

  • Standard and Extended SQL Support: Since Snowflake is an SQL-based platform, it supports all standard and extended SQL commands. 
  • Web-Based Graphical User Interface: Snowflake offers users an interactive dashboard to connect with the Cloud. Using the tool, you can monitor system usage and query data. 
  • Command-Line Client: This comes as a separate downloadable tool you can install for querying data and other functions. It is built using Python and is a great way to interact with the Data Warehouse. 
  • Extensive Integration Features: Snowflake supports integration with a wide array of third-party tools such as Google Cloud.

Now that you’re familiar with Snowflake, let’s discuss a few of the best Snowflake performance tuning techniques used across industries.

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Top Snowflake Performance Tuning Tactics

Now that you have a rough idea of Snowflake and its functionality, let’s get to the core purpose of this post. Snowflake was designed to offer the utmost efficiency for the clients with little or no tuning need. However, there are several measures you can employ to get the most out of the platform without having to worry about the cost. Read along to find out the top Snowflake performance tuning tactics.

Data Loading 

Snowflake Performance Tuning: Data Loading
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When it comes to data loading, you can employ the following practice for Snowflake performance tuning, as outlined below:

Split Files during the Data Loading process: Snowflake employs a multi-cluster and multi-threading architecture. This means that data, once loaded, is processed by several nodes in the software. Therefore, it is best to break down your file into several small chunks for them to be handled by different nodes, which will boost speed and efficiency. However, if you choose to upload one large file, you will only be using one node regardless of having a large warehouse. The same case applies to unloading data as well. 

Data Design

This primarily applies to Data Storage. You can adopt the following recommendations for Snowflake performance tuning to boost Snowflake’s performance efficiency. 

Semi-Structured Data: When storing regular data that uses native types such as strings and integers, it is best to load them into a VARIANT column since it contains the storage and query requirements.

Date/Time Data Types: When defining columns for “date/time data types”, it is best to select a date or time timestamp data type instead of characters. This is because Snowflake stores the former data types more efficiently than the latter. 

Clustering Keys: Specifying a “cluster key” is not recommended for most tables since Snowflake does automatic tuning using the optimization engine. However, for data sets greater than 1 TB, it is best to select a cluster key. The same case applies when the query profile shows a significant amount of time is spent scanning. 

Transient Tables: Snowflake supports the creation of “transient tables” but does not keep a record of their history. This is a benefit since it saves on storage costs. Therefore, you can use transient tables as needed. 

Data Querying 

Snowflake Performance Tuning: Data Querying
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For querying, there are several strategies you can use to improve overall performance, as shown below: 

Scale Up: While this doesn’t sound like an overly sensible idea, it is advantageous to improve query performance. How? By scaling up, you get additional servers that spread the load, which boosts Warehouse Cache size. 

Fetching Attributes: When fetching required attributes, avoid using the SELECT statement as it conveys all the attributes from the storage to the Warehouse Cache. This slows down the process and fills the Warehouse Cache with unwanted data.

Employing these Snowflake performance tuning measures, you can very easily boost its efficiency and overall performance.

Conclusion

Snowflake Data Warehouse is a secure, scalable, and popular cloud data warehousing solution. With the tips laid out above, you now stand at a better chance of reaping the most out of Snowflake. A common mistake people make with Snowflake is naturally assuming that the solution to all problems is scaling up to a bigger Data Warehouse. In a real sense, the solutions are multifaceted and depend on various factors such as end-user-queries and transformation. This does not mean that scaling up is not a good strategy. It is suitable when you intend on improving query performance. However, before settling on scaling up, it is best to find the issue that might be affecting the performance of your Data Warehouse.

Hence it is essential to be aware of Snowflake performance tuning tactics that you can employ to boost your company’s efficiency. This article introduced you to Snowflake and its benefits as a Data Warehouse. You further learned about the best practices employed by companies for Snowflake performance tuning. However, employing these techniques manually can be a little daunting task, you can further explore automated solutions like Hevo Data for Snowflake performance tuning.

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Hevo Data, with its strong integration with 100+ Sources & BI tools, allows you to not only export data from sources & load data in the destinations such as Snowflake, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools. You can easily extract data from a source of your choice using Hevo and load it into Snowflake to further track the performance of your business via BI tools.

Give Hevo Data a try and sign up for a 14-day free trial today. Hevo offers plans & pricing for different use cases and business needs, check them out!

Do you use Snowflake? Share your experience of working with Snowflake performance tuning tactics in the comments section below.

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