Snowflake Tutorial
The world of cloud data warehouse management can be explored well with Snowflake. This cloud-based platform presents many significant benefits for organizations. It helps in extracting insight from data quickly and efficiently. This Snowflake tutorial is a step towards understanding this platform, its uses and architecture.
What is a Data Warehouse?
Before moving on to understand 'what is Snowflake', let's understand 'what is a data warehouse'. This is important to better understand Snowflake as a platform.
A data warehouse refers to a centralized repository. It accumulates gigantic quantities of structured as well as organized data from multiple sources for an organization. Different employees in a company utilize the data within for different insights.
What is Snowflake?
No Snowflake tutorial is complete without understanding what this platform is about. It pertains to an exceptionally popular data warehouse management platform that's cloud-based. It flaunts the ability to manage large-scale workloads and data efficiently and rapidly. Its unique architecture is behind its superior performance. Separate storage and compute layers are used to facilitate flexibility and scalability. It also natively integrates with various cloud providers.
Explore igmGuru's Snowflake course to become master in Cloud-based data warehousing platform.
Why Use Snowflake?
Why use Snowflake is an important piece of knowledge. This data warehouse serves over 8900 customers globally and processes 4+ billion queries every single day. Such a huge number does not come to just about anyone. Here is why this platform oozes so much appeal-
- Cloud-based Architecture- This platform works in the cloud and allows companies to scale resources as per the demand without any worries about physical infrastructure/hardware. It handles routine maintenance tasks like hardware management, performance tuning and software updates. Burden of overhead maintenance is relieved.
- Concurrency & Performance- This data warehouse handles high concurrency easily. In short, multiple users can query and access the data without any performance loss.
- Elasticity & Scalability- It separates compute and storage layers. Consequently, users can independently scale computing resources for storage needs. This elasticity promotes efficient handling of varied workloads with prime performance but without unnecessary costs.
- Time Travel- A snapshot is taken every time a change is made to the database. Users can thus access historical data at different points in time.
- Data Sharing- Its top notch security safeguards promote data sharing across internal departments, other organizations, customers, stakeholders and external partners. Complex data transfers are not needed.
- Cost Efficiency- Its pay-as-you-go model is highly cost efficient. Users only pay for what they're using, saving them tons of money.
Snowflake Architecture
Understanding the Snowflake architecture is crucial for best utilization. It is designed to ensure faster analytical queries. It also comes from its distinction of the compute and storage layers. The three key layers here are-
Storage Layer
The storage layer here is a highly crucial component. It stores data in a scalable and efficient manner. Some key features of the storage layer are-
- Columnar Format- It comprises data in a columnar format and optimized for analytical queries. The columnar format is apt for data aggregation, as opposed to the traditional row-based formats employed by tools such as Postgres. Queries access only certain needed specified columns in columnar storage which makes it more efficient.
- Micro-partitioning- Micro-partitioning is a technique used by this platform for storing tables in memory in little chunks or segments. Every chunk is immutable and is only a few megabytes in size. This makes query execution and optimization much faster.
- Zero-copy Cloning- Zero-copy cloning is an exceptionally unique feature that enables this platform to create virtual clones for this data. Cloning is instantaneous, which means that it does not consume any additional memory unless a change is made to the new copy.
- Scale & Elasticity- This layer scales horizontally. In short, it has the ability to handle increasing data volumes just by inserting more servers for distribution of the load. Scaling also happens independently of compute resources. This is apt for businesses looking to store gigantic volumes of data but only analyzing a small fraction.
Compute Layer
The compute layer is the second layer and also the engine that executes all the queries. It operates in conjunction with the previous (storage) layer for processing the data and performing multiple computational tasks. Here are some details about how it operates-
- Virtual Warehouses- Virtual Warehouses can be thought of as teams of computers or compute nodes. These are curated to seamlessly handle query processing. Every member of the team manages a distinct aspect of the query. This makes execution impressively parallel and fast. This data warehouse also presents Virtual Warehouses in distinct sizes as well as different prices.
- Multi-cluster + Multi-node Architecture- This particular layer utilizes multiple clusters along with multiple nodes. This helps in guaranteed high concurrency. It also allows various users to query and access the data at the same time.
- Automatic Query Optimization- This platform's amazing system analyzes all the queries as well as identifies patterns to optimize via historical data. Some common optimizations here encompass using metadata, opting for the most efficient execution path and pruning unnecessary data.
- Results Cache- This layer also encompasses a cache. This cache stores the results derived from frequently executed queries. The results are returned almost in an instant upon running the same query again.
Cloud Services Layer
In this Snowflake tutorial, the final layer here in this platform is the cloud services layer. There are a lot of details on its operation as this layer gets integrated into every single component of Snowflake's architecture. It fulfills the following responsibilities-
- Security & Access Control- The cloud services layer enforces major security measures. This includes authorization, encryption and authentication. Administrators employ Role-Based Access Control or RBAC for defining and managing user permissions and roles.
- Data Sharing- This layer also implements various secure data sharing protocols throughout the different accounts as well as even third-party organizations. Data consumers are able to access the data without getting involved in any data movement. This promotes data monetization and collaboration.
- Semi-structured Data Support- This platform has the ability to easily handle semi-structured data like JSON and Parquet. This is while it is a data warehouse management platform. Semi-structured data can be easily queried and the results can be integrated with the existing tables. This flexibility is not witnessed in other RDBMS tools.
Wrap-Up For Snowflake Tutorial
This Snowflake tutorial is an apt read for gaining an in-depth understanding of this platform. It is gaining more popularity as data becomes more widespread. More companies are using data and thus, driving the need for platforms like this one. Learning it is a great way to stay current and important to hiring managers.
Snowflake Tutorial FAQs
Q1. Is Snowflake easy to learn?
Snowflake can be considered easy to learn. Those having prior data warehousing and SQL knowledge have an edge.
Q2. Is Snowflake better than AWS?
Both Snowflake and AWS offer exceptionally robust features and perks. Which one is better depends upon what the business' goals are.
Q3. Is Snowflake Tutorial right to learn?
Yes, with Snowflake tutorial you can learn SF.
Course Schedule
