From the course: Learning SnowflakeDB

Understand SnowflakeDB data platform - Snowflake Tutorial

From the course: Learning SnowflakeDB

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Understand SnowflakeDB data platform

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- [Narrator] So as we learn about Snowflake's capabilities, we need to think about data platforms and then understand what features are available in Snowflake DB, so we can understand if it's a fit for our use cases. So platforms in general will have services that work as I say before and after you are actually having the data in the platform, and this is often expressed as some kind of a pipeline. So data platforms will have input connectors, and Snowflake has these, they'll have streaming capabilities, output connectors, and visualization. So, Snowflake has strong offerings across all these different vectors which makes it a compelling choice for end to end data solutions. Now, within Snowflake itself in addition to a highly optimized storage and SQL engine there are additional capabilities that make it compelling. These include user defined functions, very rich metadata, and as of this recording they have started to integrate machine learning through capabilities that allow the use of data frames from Apache Spark. And this is an area that I'm really watching, and it's very interesting set of developments. It's early as of this recording, but again Snowflake DB team has a history of introducing features and quickly optimizing them. So, interesting feature set that they're working on. Now, what that looks like on Snowflake we'll just pull aside from their architecture to discuss. Their ecosystem supports a wide variety of data sources. So, OLTP or transactional data sources, enterprise applications, third-party web, and log data. They're particularly strong, it's often the format of JSON. It's called unstructured or file based data, but they have support for other file formats too, CSV, XML, others, but JSON in particular is a particularly strong point in terms of ingest and query. IoT scenarios which is again usually event or message based. And they support traditional ETL, extract, transform, and load via pipelining and also streaming. So they've got good feature set on the ingest side. Of course they run on your cloud of choice which is a very compelling part of their offering, and then their core engine itself is highly flexible. It supports data warehouse, data lake which we'll dive into in a subsequent movie, data engineering, exchange, applications, and as I just mentioned, starting to get into data science. Data consumers, again the output they have a marketplace for monetization which we'll be looking at in this course or just sharing data. If it's public data, for example, health data, COVID data, we're going to look at operational reporting, ad hoc analysis. They have great partner story there and real-time analytics. So, the end-to-end story is part of the value prop of Snowflake. What comes in and what goes out and how you connect to build those pipelines. And it is something that is important to understand when you're looking at Snowflake ecosystem.

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