In short, while traditional data storage solutions deliver many benefits, they also suffer from:<\/p>\n
- \n
- Limited Storage Capacity:<\/strong> Storage arrays in traditional warehouses also require archiving, causing some data to be unavailable for ongoing analysis.<\/li>\n
- High Cost:<\/strong> Larger warehouses lead to higher disk storage and power processing costs.<\/li>\n
- Limited Processing Capability:<\/strong> Individual queries run quickly in traditional warehouses, but simultaneous queries from multiple users strain single-core machines or otherwise limited infrastructure.<\/li>\n
- No Scalability:<\/strong> As data grows, the systems should be scalable enough to store it. However, conventional databases eventually hit a practical limit.<\/li>\n
- Maintenance:<\/strong> Aging hardware leads to hardware failures.<\/li>\n
- Skilled Admin Dependency:<\/strong> Traditional warehouses need many highly skilled people to maintain indexes, update metadata, choose data distribution, collect \u201cgarbage,\u201d maintain memory and more.<\/li>\n
- Data Security:<\/strong> When users access data directly from numerous points, security problems give hackers a chance to move in.<\/li>\n<\/ul>\n
As a result, instead of more or bigger data warehouses, data managers today need cloud-based data storage solutions that enable diverse data-management architectures. That way, you can spend your time managing and fine-tuning data, rather than worrying about your infrastructure setups, node failures, security, day-to-day maintenance, and bad results \u2014 while meeting your business\u2019s data needs.<\/p>\n
In 2019, Gartner released its report, \u201cMagic Quadrant for Data Management Solutions for Analytics<\/a>,\u201d announcing a solution called Snowflake<\/a> had emerged as a market leader. According to the report, the number of Snowflake customers tripled in 2018, fueling 247 percent year-over-year revenue growth.<\/strong><\/p>\n
Gartner\u2019s report was an eye-opener that caught the imagination of the world. What made Snowflake\u2019s data storage solution so successful? Let\u2019s take a deeper look into the Snowflake architecture to find out.<\/p>\n
Why Snowflake?<\/h2>\n
One key to Snowflake\u2019s success is its multiple purposes: data lake, operational data store, data warehouse, and data mart. With increasing demand, people need a data architecture<\/a> or solution that is more:<\/p>\n
- \n
- Robust<\/li>\n
- Massively scalable<\/li>\n
- Democratic and less prone to error<\/li>\n
- Free of data silos<\/li>\n
- Able to share governed data<\/li>\n
- Capable of executing diverse analytics workloads for big data delivery.<\/li>\n<\/ul>\n
Furthermore, Snowflake\u2019s platform enables enterprises to fully automate core business processes by:<\/p>\n
- \n
- Providing the power of data warehousing:<\/strong> Snowflake collects and aggregates data from one or many sources, delivering a longer view of an organization\u2019s data over time.<\/li>\n
- Providing flexibility of big data platforms:<\/strong> Snowflake supports semi-structured data and can ingest it directly into a relational table using the VARIANT data type. Once loaded, you may query JSON data using SQL, making data pipelines much simpler. Users don\u2019t need to modify code every time they add a new column or parameter, which reduces the DevOps<\/a> operational burden for app developers.<\/li>\n
- Handling elasticity over the cloud:<\/strong> The cloud provides the option to resize your virtual warehouse for additional compute resources at a fraction of the cost of traditional solutions.<\/li>\n<\/ul>\n
However, Snowflake\u2019s cloud solution goes beyond elasticity because it is cloud agnostic. Whether data is stored in Amazon Web Services<\/a> (AWS), Azure or some other cloud solution<\/a>, Snowflake can manage it in multiple clouds. That gives Snowflake an added edge over its competitors.<\/p>\n
Let\u2019s delve deeper and look at Snowflake\u2019s architecture.<\/p>\n
Inside Snowflake Architecture \u2013 High Level<\/h2>\n
Snowflake supports a high-level architecture, as depicted in the below diagram. It has three main components, which together make up the Snowflake data warehouse:<\/p>\n
- Providing flexibility of big data platforms:<\/strong> Snowflake supports semi-structured data and can ingest it directly into a relational table using the VARIANT data type. Once loaded, you may query JSON data using SQL, making data pipelines much simpler. Users don\u2019t need to modify code every time they add a new column or parameter, which reduces the DevOps<\/a> operational burden for app developers.<\/li>\n
- Providing the power of data warehousing:<\/strong> Snowflake collects and aggregates data from one or many sources, delivering a longer view of an organization\u2019s data over time.<\/li>\n
- High Cost:<\/strong> Larger warehouses lead to higher disk storage and power processing costs.<\/li>\n
![\"Snowflake](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2021\/11\/Graphics_SnowflakeArchitectureBlog_11.11.21-1024x596.png\")
<\/a><\/p>\n