Snowflake - Introduction

Snowflake is a cloud-based advanced data platform system, provided as Software-as-a-Service (SaaS). Snowflake provides features of data storage from AWS S3, Azure, Google Cloud, processing complex queries and different analytic solutions. The analytic solutions provided by Snowflake are faster, easy to use and more flexible than traditional databases and their analytics features. Snowflake stores and provide data near time not in actual real time.

Snowflake is advanced solution for OLAP (Online Analytical Processing) technology. OLAP is also known as online data retrieving and data analysis system using historical data. It processes complex and aggregated queries with low number of transactions. For Ex: Getting number of orders, sales amount in last month for a company, number of new users list in the company in last quarter etc. Snowflake is not used as OLTP (Online Transactional Processing) database. OLTP database usually contains real time data with a high volume of small data transactions. For Ex: Inserting customer's order detail, register a new customer, tracking order delivery status etc.

Why Use Snowflake?

Snowflake provides Data Platform as a Cloud Service.

  • There is no hardware neither virtual nor physical to select, install, configure or manage from client side.

  • There is no software to install, configure or manage to access it.

  • All ongoing maintenance, management, upgrades and patching are owned by Snowflake itself.

Traditional databases for analytics solutions are complex in architecture, costly and constrained while Snowflake is rich in concept of Data Engineering, Data Lake concept, data warehouse, Data Science, Data Application and Data Exchange or sharing. It is easy to access and use without having constraint of data size and storage capacity. User must administrate only their data; all data platform related administrations are done by Snowflake itself.

Apart of these, Snowflake also has the following features −

  • Simple, reliable data pipelines in multi languages like Java, Python, PHP, Spark, Ruby etc.

  • Secured access, very good performance and security of data lake.

  • Zero administration for tool, data storage and data size.

  • Simple data preparation for modeling with any framework.

  • No operation burden to build data intensive applications.

  • Share and collaborate live data across company's ecosystem.

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