Snowflake data platform allows multiple types of data warehouses to be created, as illustrated by this Snowflake melting on the tip of a leaf.

Snowflake Concepts

What Type of Data Warehouse Is Snowflake Data Platform?

With Snowflake, it’s possible to build an enterprise data warehouse (EDW), an operational data store (ODS), or a team-specific data mart.

Graffiti reading “TRUST YOUR STRUGGLE” used to illustrate that product and data teams struggle to work together when building data apps and analytics products.


Why do product and data teams struggle to work together?

Product and data teams struggle to work together because there's a tradeoff in data between flexibility, performance and cost-effectiveness.

Creating charts for data visualization and analytics is difficult by hand, illustrated by this drawing of a line chart on graph paper with a pen and ruler on a wooden table, so we’ve selected our favorite React charting libraries: Recharts, Echarts for React, React ChartJS 2, and VISX.

Data Engineering

Best React Charting Libraries for Data Visualization and Analytics

We've picked Recharts, Echarts, React ChartJS 2, and VISX as the best charting libraries for data visualization and data analytics in React.

Snowflake’s virtual warehouses are used to pay for the processing power you need to run data analytical queries, like would be need to power a virtual dashboard of real-time pricing information, like the one shown in this image.

Snowflake Concepts

What Is the Use of a Virtual Warehouse in Snowflake Analytics?

In Snowflake, you allocate “virtual warehouses” (computing clusters) to execute the SQL database commands that you run on the data platform.

Snowflake’s storage of data for analytics is complicated “under the hood” because it uses columnar storage, as illustrated by this close-up image of a snowflake perched vertically on a block of ice like a column.

Snowflake Concepts

How Does Snowflake Storage Work? (Databases & Schemas)

Databases and schemas ("namespaces") are used to organize data in Snowflake storage, which uses a columnar format internally for analytics.

Snowflake warehouses aren’t exactly multi-processor computing clusters with hundreds of nodes, but it can make sense of thinking of Snowflake credits as analogous to nodes, as illustrated by this image of dozens of snowflakes falling at sunset.

Snowflake Concepts

How Many Nodes Are in a Snowflake Virtual Warehouse?

Snowflake uses credits, which are analogous to CPU nodes, in order to pay for the virtual warehouses that power its analytical query engine.

Snowflake is considered a data warehouse because it’s cloud-based platform is central repository of data that separates storage of the data from the compute resources needed to process that data for analytical queries, as illustrated by this image of a single snowflake on lint-covered fabric.

Snowflake Concepts

Is Snowflake a Data Warehouse for Analytics and Insights?

Snowflake data platform is referred to as a data warehouse or data lake because it separates storage (data) from compute (processing power).

Databases are how you pay for storage while warehouses are how you pay for compute in Snowflake data platform, illustrated by a snow globe filled with realistic Snowflakes.

Snowflake Concepts

What Is the Difference Between a Database and a Warehouse in Snowflake?

Snowflake uses databases for data storage, while a “Snowflake warehouse” is a virtual computing cluster that processes analytical queries.

Propel Data offers a blazing fast GraphQL API for building data apps from data that organizations already have in Snowflake data warehouse, as illustrated by this photograph of a laptop showing a real-time product dashboard with analytical reports and data visualization.


How to build Snowflake data apps with GraphQL

Need to build a Snowflake data app? Here's how to create and query a Metric on top of Snowflake data warehouse using Propel’s GraphQL API.

In-product analytical dashboards, like the one shown in this photograph, typically require data engineers to construct, while data analysts tend to be involved in manual reporting of data analytics. In comparison, data scientists are frequently found working on scientific or machine learning projects.

Data Engineering

What Is the Difference Between a Data Engineer, a Data Scientist, and a Data Analyst?

In the “Big Data” industry, there are big differences among the work responsibilities of data scientists, data engineers, and data analysts.

Snowflake accounts can hold an unlimited number of virtual warehouses, as illustrated by this picture of an office building where the division of the windows looks like hundreds of tiny warehouses.

Snowflake Concepts

How Many Virtual Warehouses Can Snowflake Hold?

Snowflake data platform allows many virtual warehouses in one account, but multi-cluster virtual warehouses are an Enterprise-only feature.

Multi-cluster virtual warehouses in Snowflake are analogous to the server farm pictured, since they allocate additional compute resources compared to a single warehouse or virtual machine.

Snowflake Concepts

What Is a Multi-Cluster Virtual Warehouse in Snowflake Data Platform?

Multi-cluster virtual warehouses auto-scale compute resources based on the demands on the data warehouse. Here’s how they work in Snowflake.

Snowflake's virtual warehouses are the compute engines that process analytics, and they're required to be running when you load data or run analytical queries, like those necessary to power an in-product dashboard like the one shown in this photograph.

Snowflake Concepts

What Are Warehouses in Snowflake Data Analytics Platform?

Snowflake’s virtual warehouses are computing clusters that process the data analytics commands you run on Snowflake data analytics platform.

Propel Data's now supports Snowflake warehouse, meaning developers can build data apps on top of GraphQL APIs powered by their Snowflake data, as illustrated by this single snowflake on a dark blue background consistent with Propel's brand colors.


How to build in-product analytics with Snowflake and GraphQL

Propel Data is excited to announce support for Snowflake. Developers are now able to build on top of GraphQL APIs powered by Snowflake data.

The three co-founders (from left): Mark Roberts, Tyler Wells, and Nico Acosta are thrilled to explain why we built Propel Data, an API Platform for developers to easily build in-product analytics with large-scale data.


Why we built Propel Data

Today, we are thrilled to announce Propel Data – an API Platform for developers to easily build in-product analytics with large-scale data.