When researching what tools and technologies can be combined with the Snowflake Data Cloud in a data stack, you undoubtedly will end up asking yourself the question,
“Will these technologies work together?”
This is a very important question to answer before data engineers can begin building pipelines or data scientists can utilize any business intelligence tools.
In this blog, we’ll help explain how some technologies work with, or within Snowflake’s data platform to create the right data stack for your needs.
To understand how the tools you are investigating might work with Snowflake, let’s talk a little about Snowflake itself.
What is Snowflake Used For?
The Snowflake data platform is used to build data pipelines to easily extract, load, and transform (ELT) your data into a data model that business intelligence (BI) applications can use for data visualization, data analysis, and machine learning.
The underlying architecture of Snowflake (known as a data warehouse) is a true SaaS architecture, meaning you will never have to upgrade hardware, update software, or most importantly pay for resources when they are not in use. Snowflake is a full-bodied and complete data platform solution that runs on its own cloud infrastructure.
Platforms and technologies commonly connect to Snowflake in a few different ways:
- Connect to data models created using Snowflake data pipelines – A common use case for this would be a BI tool creating a visualization using data from Snowflake-created models.
- Work within Snowflake data pipelines to perform a particular task – For example, a data tool that works inside of Snowflake for transforming data in the ELT workflow.
- Utilize Snowflake’s SaaS architecture – Many technologies offer similar ELT capabilities at Snowflake, but do not offer an underlying platform for computing those data interactions. In these situations, Snowflake’s premiere cloud cluster infrastructure can be used for performing its computational needs. In Snowflake, computational resources are allocated simply by defining a scalable, maintenance-free data warehouse that only consumes resources when there are computational needs (e.g., performing a query.) This abstracted approach to allocating server resources makes Snowflake data warehouses attractive to both users and technology partners.
What Tools Are We Going Over in This Blog?
The tools and platforms covered in this blog are by no means an exhaustive list of what tools and platforms can be combined with Snowflake in a data stack (not even close!).
The technologies we’ll be covering are representative of what we at phData have the most experience with. To view a comprehensive list, check out Snowflake’s article on “All Partners & Technologies”.
Alteryx’s intuitive analytics platform combines data cleansing, analytics engineering, and machine learning capabilities into a single, easy-to-use BI solution.
Alteryx can help wrangle and model your data by both reading from and writing to Snowflake using Alteryx’s easy-to-configure connectors, enabling you to provide clean data to business users and other visualization tools.
Combining Alteryx with Snowflake provides you with a complete and practical data stack.
Dataiku is a well-liked data science platform that provides an intuitive graphical interface for building machine learning and business analytic flows. You can use Snowflake to perform ELT into a data model that Dataiku can reference.
Additionally, you can configure your data pipelines directly in Dataiku and leverage Snowflake’s powerful data warehouse for your data stack’s computational needs.
Looking to dive deeper into Dataiku and Snowflake? Be sure to check our comprehensive guide to Getting Started with Dataiku and Snowflake.
Snowflake manages your data, but what manages Snowflake? The DataOps platform is built to do just that. DataOps manages the entire orchestration of your Snowflake data stack, including both Snowflake and the technologies that connect to it.
Additionally, using DataOps to manage your data stack enables automated testing, CI/CD, and code management of your data stack in its entirety.
If you are looking for a tool to quickly and effectively deploy applications and data products on the Snowflake platform, DataOps is the tool for you.
In a nutshell, dbt is a tool that provides easy data transformation (the T in ELT) simply by using select statements. It also provides advanced features like Git-enables version control, automated testing, self-generating documentation, CI/CD, and an in-browser IDE for development.
When paired with Snowflake, dbt can be a powerful tool for transforming data in a Snowflake data pipeline while providing you with features commonly found in software engineering (e.g., source control.)
We recommend exploring our Beginners Guide to Using dbt with Snowflake for more in-depth information.
Fivetran is a low-latency, complete data centralization platform centering around the idea of easy-to-use data ELT.
Since Snowflake’s architecture separates storage and computing resources, you can use Fivetran to build your data pipelines while taking advantage of using multiple Snowflake data warehouses to transform and load data.
Free? Open source? We couldn’t be talking about a data analytics platform, right? But sure enough, we are! We actually have a whole article talking about what KNIME is, which you can read here.
In short, “KNIME is a low-code data science and data preparation platform that makes understanding data and designing analytic workflows accessible to everyone.”
If you want to learn more about how KNIME and Snowflake work together, check out our article titled, “How to Connect KNIME to Snowflake.”
Matillion’s intuitive, cloud-based ELT tool easily connects to data sources, transforms data in a low-code GUI, and prepares data for BI consumption.
Like other ELT tools in this blog, Matillion can be paired with Snowflake to take advantage of Snowflake’s dynamic data warehouse.
While Microsoft’s Power BI is a powerful analytics tool for visualizing data, you will need a way to organize all the underlying data. That is where Snowflake steps in! You can use Snowflake’s data platform to ELT all of your data into a model that Power BI can natively connect to.
If you are interested in learning more about connecting Power BI to Snowflake, you can check out our blog on How to Connect Power BI to Snowflake.
While most BI tools are for building data visualizations, Sigma is designed to work with data through spreadsheets. You can view data, write functions, and combine columns from multiple tables into a spreadsheet— all through Sigma’s intuitive web GUI.
Sigma is a great BI tool for actively analyzing data and is commonly paired with other BI tools for generating data visualizations.
How does Sigma and Snowflake work together in a data stack? The Snowflake data platform’s ELT and data warehousing capabilities provide data models that Sigma can connect to. You can even use Snowflake warehouses to perform functions running in Sigma.
Want to learn more about how to connect Sigma to Snowflake? See our blog post on How to Connect to Snowflake in Sigma.
Tableau is an extremely popular and powerful BI tool for building visualizations and analyzing data.
Like other BI tools, Tableau utilizes Snowflake’s data platform to ELT data into a model which Tableau uses to feed visualizations and perform real-time analytics
Deciding on what technologies to incorporate into your data stack is an important process that can be made much easier (and more effective) by integrating with Snowflake.
Including Snowflake in your data stack is simple, whether you use Snowflake for processes like building data pipelines, ETL data, or taking advantage of Snowflake’s premiere cloud cluster infrastructure.
We here at phData have utilized each of the technologies from this article in conjunction with Snowflake. Honestly, we can say no matter how you decide to use Snowflake, you can be assured you are incorporating a powerful, intuitive, and profoundly supported data platform into your data stack.
Interested in learning more about each of these tools and how they pair with Snowflake to solve real business challenges?
Reach out to phData today for questions, best practices, or example use cases, we love helping businesses succeed with Snowflake.