This article was published as a part of the Data Science Blogathon.
Introduction
The rate of data expansion in this decade is rapid. The requirement to process and store these data has also become problematic. Today, data controls a significant portion of our lives as consumers due to advancements in wireless connectivity, processing power, and the creation of the Internet of Things (IoT) devices. The same is true for businesses using data to improve their offers, procedures, and revenue.
Businesses must figure out how to interpret the vast amounts of data available. The spreading of data across both cloud and on-premise also poses a significant challenge. Many organizations are now facing challenges in managing both systems.
The advantages of Snowflake, the top cloud-agnostic data warehousing platform, will be covered in this article in greater detail. Additionally, we observe how adopting Snowflake enables businesses to manage enormous amounts of data dispersed across several clouds and on-premises, allowing them to concentrate on data analysis and improve their decision-making using their data.
What is a Data Warehouse?
The data warehouse is an organization’s core analytics system aggregating data from different sources. It stores data from several sources in a single, central data repository that is reliable. After that, the data goes for analysis, artificial intelligence (AI), and machine learning purposes.
It helps businesses analyze vast amounts of historical data to make well-informed business decisions.
Traditionally, a data warehouse host on-premises. The need for cloud-based data warehouses is growing as businesses use the cloud more often. Many companies are already using cloud data platforms or are strongly considering doing so as part of a long-term strategic plan to transform themselves into cloud-first, data-driven businesses.
Snowflake has become the most popular choice among several other options because it supports multi-cloud infrastructure environments such as Amazon, Microsoft, and GCP.
Problems with Conventional Data Warehouses
- Performance problems when trying to load and query data simultaneously.
- Inefficiency in integrating multiple data sources.
- The Data recovery method is expensive, time-consuming, and uncomfortable.
- Lack of a single source of truth results in inconsistent, unreliable data and inadequate data exchange.
- Scalability issue in the long run.
What is Snowflake?
Snowflake is the most popular cloud-based Software-as-a-Service (SaaS) tool. It supports the following cloud platforms infrastructure and allows storage and computing to scale independently:
- Amazon Web Services
- Microsoft Azure cloud
- Google Cloud Platform (GCP)
It is a multi-purpose cloud data platform used as a data warehouse, operational data stores, data lakes, and data marts. It enables data processing, storage, and analytic solutions that are easier to use, faster, and more flexible than traditional offerings. Its automatic up-and-down scalability and decoupled Compute and Storage architecture help to balance performance and operational cost.
What distinguishes Snowflake is its design and data-sharing capabilities. Due to the Snowflake architecture’s ability to scale storage and compute independently, customers can pay and use the storage and computation separately. Furthermore, the data sharing capability enables companies to share governed and protected data in real time quickly.
Snowflake Architecture
The Snowflake architecture consists of three layers, and each layer is independently scalable: storage, computing, and services.
Database Storage
Snowflake uses highly scalable and secure cloud storage to store structured and semi-structured data like JSON, AVRO, and Parquet. Tables, schemas, and databases make up the storage layer. Snowflake helps to manage all aspects of data storage, file size, structure, compression, metadata, and statistics. This storage layer operates independently of the computing resources. Multiple encrypted micro partitions that scale automatically are present in the storage layer.
Compute layer (Query Processing)
The compute layer handles the query execution tasks using resources provisioned by a cloud provider. This layer comprises virtual cloud data warehouses and helps you analyze data through requests. Each virtual warehouse of the Snowflake is an independent cluster. They do not compete for computing resources nor affect performance.
Cloud Services
Snowflake uses ANSI SQL for cloud services, enabling customers to manage their infrastructure and optimize their data. Snowflake handles data encryption and security. They continue to have dependable HIPAA and PCI DSS certifications for data warehousing. Services include access control, query processing and optimization, infrastructure management, query authentication, and metadata management.
Snowflake Benefits for Business
A lot of the issues with older hardware-based data warehouses, like restricted scalability, challenges with data transformation, and delays or failures, are addressed with Snowflake, which has been built specifically for the cloud. Here are the benefits of using it:-
Performance
You can scale up or down your virtual warehouse to take advantage of more computational resources if you need to load data more quickly or execute a large number of queries due to the elastic nature of the cloud. After that, you can reduce the virtual warehouse and only charge for the time you spend processing the queries.
Storage
Structured and semi-structured data can be combined for analysis and loaded directly into a cloud database, eliminating the need for conversion or transformation into a rigid relational schema. The data storage and querying processes are automatically optimized using Snowflake.
Concurrency and Accessibility
In a traditional data warehouse, you could encounter concurrency problems (such as delays or failures) if many users or use cases compete for resources.
With its unique multicluster architecture, Snowflake addresses concurrency issues: queries from one virtual warehouse never affect others. Each virtual warehouse may scale up or down as needed without waiting for other loading and processing operations to finish.
Reliability and Availability
With the help of Snowflake, businesses can automate data management, security, governance, availability, and resiliency. As a result, operational efficiency increases along with cost optimization, downtime reduction, and scalability. It automated data replication for quick recovery and high reliability and availability.
Data Sharing
The architecture of Snowflake permits data sharing between Snowflake users. The user interface creates reader accounts that companies can use to share data with any data consumer without concern about whether they are customers of Snowflake or not.
Third-party data integrations
The Snowflake Marketplace is a data exchange that provides access to a growing number of live and ready-to-query datasets from third-party data providers and data service providers for data scientists, analytics, and business intelligence professionals.
With the help of the Snowflake Marketplace, a feature of the Data Cloud, you can improve business analytics by adding new data from third parties or internal data from potential SaaS partners.
Snowflake Pricing
With their flexible pricing structure, you only pay for the cloud storage and computing you use. For Snowflake accounts, they provide a variety of price options, such as per-second pricing on demand with no long-term commitments or pre-purchased Snowflake capacity options. The compute billing is the second basis, with a 60 seconds minimum usage. They provide a no-risk trial period.
Conclusion
This article covers traditional data warehouses and their limitations. Next, we discuss Snowflake, the modern cloud-agnostic data warehouse. Snowflake can help businesses tackle data-related challenges, like saving and processing it.
Key takeaways from this article are:-
- We learn about traditional Data Warehouses and their drawbacks in modern data worlds.
- Snowflake is a modern cloud data warehouse with multiple use cases.
- The Architecture of Snowflake helps to scale up and down as per the requirements that reduce downtime.
- Snowflake has numerous benefits for different types of business requirements.
- Snowflake pricing model and the third-party integration system helps business to scale quickly.
I hope this article helps you to know about Snowflake. If you have any opinions or questions, then comment down below. Connect with me on LinkedIn for further discussion.
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I am a Data Engineer with a proven track record of working in the information technology and services industry. I am skilled in Apache Spark, Hive, SQL, Python, Hadoop, Databricks and Cloud.
