This blog helps in understanding a thorough comprehension of Snowflake architecture, data management & storage processes, and conceptual fragmentation notions. Also, you will understand how Snowflake architecture differs from all the cloud-based websites of the massively parallel processing databases.
What is a Data Warehouse?
Businesses accumulate a lot of data from diverse sources. Capturing, processing, and storing huge or complex data sets becomes more and more challenging as a result of data explosion. In order to make informed decisions, businesses must therefore maintain a Central Archive where all data is safely stored and can be re-analyzed. That’s when the Data warehouse comes to the light.
The Data Warehouse is a Central Database that serves the functions of Data Analytics and Business Intelligence. It is frequently referred to as the "One Source of Truth" (BI). Data warehouses are used to consolidate enormous amounts of data from various sources for analysis and questioning in order to enhance operational performance. Because of its analytical strength, firms can extract crucial business information for informed decision-making.
What is the Snowflake Data Warehouse?
A cloud-based data warehouse solution, Snowflake, is offered as SaaS (Software-as-a-Service) and fully supports ANSI SQL. It also features a distinctive structure that enables users to create tables quickly and begin data queries, requiring little management or DBA tasks.
Features of Snowflake Data Warehouse
Let's discuss some of the great features hosted by Snowflake data warehouse:
1. Data security and protection: The Snowflake data repository offers multi-factor authentication (MFA), single sign-on (SSO) for government users, and OAuth. TLS protects communication between the client and server.
2. Complete SQL Support: Snowflake Data Repository supports multiple DDL and SQL DML commands. Additionally, it enables lateral views, transactions, advanced DML, saved processes, etc.
3. Connectivity: Snowflake Database offers a wide range of client and driver connectors, including those for Python, Spark, Node.js,.NET, and other programming languages.
4. Secure Data Sharing: You can share your data securely with other Snowflake accounts.
Types of Data Warehouse Architecture
Below listed are the three ways to types of Data Warehouse Architecture:
Single-Tier Architecture: By extracting data, this form of architecture aims to reduce data storage.
Two-tiered Architecture: This design separates the Database from the actual Data Sources. As a result, Data Warehouse may grow and accommodate numerous-end users.
Three-tiered Architecture: There are three phases in this form of architecture. Data Warehouse Server Databases, an intermediate section of the Online Analytical Processing (OLAP) Server gives an unclear perspective of websites. Data extraction tools and APIs are included in the Advanced Client Framework.
Components of a Data Warehouse
Here are the four components of the Data Warehouse:
1. Database Warehouse Database: The website is a crucial component of the Database. The database enables easy access and corporate data storage. Azure SQL and Amazon Redshift are common examples of cloud-based Database services.
2. Extraction, Transform, and Load (ETL) Tools: This category includes all tasks related to extracting converting, and uploading (ETL) data from a warehouse. Data is extracted from various sources, transformed into a usable format, and then uploaded to a data warehouse using traditional ETL technologies.
3. Metadata: Metadata provides an architecture and definitions of data, enabling users to create, store, manage, and optimize data.
4. Database Access Tools: Users can obtain useful and business-friendly information from the Data Warehouse with the help of accessibility tools. These warehouse tools include OLAP, Data Mining, Application Development, Data Reporting, and Data Inquiry tools.
Snowflake Architecture
The Snowflake data architecture is combined with shared-nothing and shared-disk data architectures. Similar to shared-disk systems, Snowflake has a central data repository that is available from all compute nodes in the platform. However, Snowflake uses massively parallel processing (MPP) compute clusters to carry out queries. Similar to shared-nothing systems, each node in the cluster stores a portion of the complete data set locally. This approach combines the simplicity of data management offered by shared-disk designs with the effectiveness and scale-out benefits of shared-nothing architectures.
The Snowflake data warehouse architecture contains three layers as listed below:
Database Storage Layer
Query Processing Layer
Cloud Services Layer
Database Storage Layer: The data is split up into multiple tiny pieces by Snowflake and internally optimized and compressed. A Scalable cloud blob storage type is available in Snowflake for storing structured and semi-structured data (including JSON, AVRO, and Parquet). Snowflake is a shared-disk technique to store and manage data in the cloud, simplifying data management. Users need not be concerned about data distribution across multiple nodes thanks to the shared-nothing architecture. User data items are hidden by Snowflake and only accessible through the compute layer via SQL queries.
Query Processing Layer: Snowflake makes advantage of the Virtual Warehouse to run queries. In the Snowflake data architecture, the disk storage layer and the query processing layer are two different layers. Run queries in this layer using the data from the storage layer. Virtual Warehouses are MPP compute clusters hosted by Snowflake that have a lot of nodes with CPU and Memory. Based on workloads, you can create a variety of Virtual Warehouses in Snowflake to meet your needs. A single storage layer can be utilized by each virtual warehouse. Most often, a virtual warehouse operates independently of other virtual warehouses and has its own compute cluster. Virtual Warehouses are easily extendable, have an auto-scaling factor, and may be automatically resumed and suspended (when defined).
Cloud Services Layer: This layer comprises all of the operational processes that coordinate throughout Snowflake including data authentication, metadata management, security, and query optimization. A cloud service is a stateless computing resource that works across many availability zones and makes use of readily available and employable data. A SQL client interface is provided by the service layer for data operations like DDL and DML.