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Dr. Derek Austin

Developer Advocate

Dr. Derek Austin is Developer Advocate for Propel, and he loves educating developers working on in-product analytics and data apps.

As a self-taught developer for over 20 years, Dr. Derek Austin loves learning new technologies. A graduate of Virginia Commonwealth University with a BS and MS in Bioinformatics and Doctor of Physical Therapy, Derek has been writing about web development on Medium since 2019. He currently works as Developer Advocate for Propel Data, where he helps developers build in-product analytics.

Articles by

Dr. Derek Austin

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 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.

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).

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.

Snowflake’s multi-cluster virtual warehouses have many benefits, most especially the ability to scale the number of clusters automatically, as illustrated by this photograph of the night sky with multiple blue clusters from the Pleiades cluster visible clearly in the center of the frame.

Snowflake Concepts

What Are the Benefits of a Multi-Cluster Warehouse in Snowflake?

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

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.

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.

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.

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 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.

Data lakes support unstructured content but can be complex to navigate, while some data warehouses are more user-friendly and suitable for use by less technical users in an enterprise, as illustrated by this photograph of data engineers diagramming a data pipeline based on event data.

Data Engineering

What’s the Difference Between a Data Warehouse and a Data Lake?

The main difference between data lakes and data warehouses is data lakes allow unstructured data, but data warehouses need structured data.

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