From the course: Intro to Snowflake for Devs, Data Scientists, Data Engineers

Intro and course overview - Snowflake Tutorial

From the course: Intro to Snowflake for Devs, Data Scientists, Data Engineers

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Intro and course overview

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(upbeat music) - Welcome to Intro to Snowflake for Data Engineers, Data Scientists, and App Developers. My name is Peter Olson. I'm a developer advocate at Snowflake, and I'm so excited to be part of your Snowflake journey. This is an extremely hands-on course, so we want to get into the product right away, and then only introduce theory if it feels really relevant to practice. But before we hop into using the Snowflake platform, I want to say a few sentences about what Snowflake is, and then I want to take a moment to explain who this course is for and what you can expect to get out of it. Then, I'll mention what we'll cover in this first part of the course on Snowflake's core objects and architecture. So, what is Snowflake? Snowflake is the Data Cloud, a global network where organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations have a unified view of data so they can discover and share, govern data, and execute many kinds of analytics workloads. The Data Cloud has two parts, platform and data. Snowflake's platform is the engine that powers the Data Cloud, and we'll spend a lot of time in this course learning about how to use this platform. This definition probably sounds abstract, it did to me, at first, but that's partly because Snowflake lets you do a lot with your data, and it's really hard to capture in a few words. Also, this definition of Snowflake focuses a lot on an ambitious vision of organizations making data and apps available to each other, and it's mind-bending to think of not just connecting all parts of a company, but also enabling data connections across companies. At the end of this course, we'll return to this idea of Snowflake as the Data Cloud, and I'm hopeful this description will feel a lot more tangible, then. Okay, so who is this course for? We designed it for a wide range of data practitioners, so data engineers, data scientists, data app developers, ML engineers, and more, who share the common goal of gaining foundational knowledge about Snowflake. Maybe you're an aspiring data engineer who wants to advance your career, and you see in job descriptions that companies are looking for people with Snowflake skills. Maybe you're a data scientist using Snowflake, but you know it has many capabilities you aren't making use of, and you want to figure out how to make better use of it. At Snowflake, we call these people builders, because if you're a data engineer, data scientist, data app developer, or ML engineer, that means you are building pipelines or building statistical models or building ML models or building data apps. When I first came to Snowflake, I wondered if the builder term was just marketing speak. I used to be a data scientist, and I didn't think of my job as having that much to do with, say, app development. But I've come to realize that the builder term reflects a pretty significant conviction on Snowflake's part that the lines are blurring between data roles. For data science and data engineering, those lines have long been blurry. Data scientists often do data engineering tasks, and vice versa. But more and more, we think this will also be true in other areas, for example, with app development, as Snowflake makes it easier to deploy data projects as data apps. You don't need to have prior experience with Snowflake to take this course, though if you already have some, you'll still benefit. We do assume that those taking this course are somewhat familiar with SQL and Python. For example, you should be able to run basic SQL queries, think SELECT * FROM with the ORDER BY and GROUP BY keywords in there, and you should be able to import Python libraries, create and use a Python function, and do basic data manipulation with dataframes. If this doesn't sound like you, I recommend you do some introductory coursework on SQL and Python first. Okay, so now we've covered who this course is for, data practitioners with some SQL and Python experience who want to gain foundational Snowflake knowledge, likely for career advancement. Now, let's talk more about what you should expect to get out of it. We have two main goals for this course. First, if this is the only course you take on Snowflake, we want you to come away knowing four things. One, Snowflake's core objects and how to use them. Two, Snowflake's architecture. Three, fundamental Snowflake features, like time travel, user-defined functions, et cetera, and how to use them. And four, much of what Snowflake now lets users do with data engineering, applications, AI, and ML. We want you to emerge from this course with a foundational mental map of what's possible with Snowflake and a bunch of reps getting stuff done. And I don't know how you're feeling right now. You might be feeling uncertain or even a little anxious. But we also want you to emerge feeling confident and excited about your ability to use Snowflake. Second, if this course is part of a longer Snowflake learning journey for you, you should expect to come away equipped to take a bunch of other Snowflake coursework. We designed this to be the single point of entry for a longer Snowflake learning journey. For those who want to go deeper on, say, data engineering with Snowflake, GenAI with Snowflake, applications with Snowflake, and more. In this course, we're going to talk about lots of Snowflake products and features, including some that are still in preview as of the time of filming. It's challenging to make a course like this because Snowflake's functionality is changing and improving all the time. So, if you ever have questions about any of these products or features, or their availability, you can always refer to Snowflake's product documentation at snowflake.com. Last thing, what we will cover in this first part of the course. In this first part of the course, we'll learn about Snowflake's core objects and architecture. Specifically, we'll learn about virtual warehouses, stages, databases, schemas, tables, views, semi-structured data, and the different Snowflake architectural layers. Okay, that's enough preamble. Let's dive in. (upbeat music)

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