The idea of data warehousing was first explored in detail back in 1988 by a team of researchers at IBM. In their whitepaper, Barry Devlin and Paul Murphy went into detail about the environments in which companies were maintaining their database instances. They highlighted the growing need at IBM for further integration to simplify access and improve data consistency for end-users. This was during a time where “big data” was unheard of, with businesses managing much smaller datasets.
At present, over 2.5 quintillion bytes of data are generated per day, with an exponential trajectory into the future as more people gain access to the internet. Our requirements for consistent, well-formatted datasets are growing at the same rate. By failing to keep pace, we risk hindering business growth and security compliance.
That’s where Snowflake steps in. We have partnered with Snowflake to deliver Software-as-a-Service (SaaS) data warehousing and analytics to our clients. Let us explore Snowflake’s offering in more detail.
Snowflake is a data warehousing and analytics platform, offered in the form of SaaS. The platform aims to improve database querying performance, data accessibility, and improved flexibility with their fork of the popular structured query language (SQL), called SnowSQL.
Here are some of the key concepts of Snowflake’s data warehousing approach:
Serverless Computing - With new operational paradigms like serverless computing, you can offload the management of server hardware and OS configuration to Snowflake. This can net you immense financial savings, while concurrently improving your enterprise compliance standards.
Instead of a single traditional cloud database server, Snowflake uses compute clusters that are specifically configured to perform massively parallel processing (MPP) for your data warehouse. Your staff would interface with a master node, which then feeds instructions to a parallel execution node. This execution node then schedules work between tiny compute clusters, before receiving an output from each individual compute node and feeding it back to you through the master node. This operational abstraction layer negates the need for server management, as you are only interacting with your dedicated master node. Snowflake manages the background processing, allowing you to offload much of the operational liability and achieve full GDPR, CCPA, and PCI-DSS compliance with ease.
You simply choose your serverless computing platform (AWS, Azure, or GCP), and then Snowflake will use your total data volume and compute resource utilization to generate a transparent, personalized monthly bill. This is based solely on the computing resources you actually use, minimizing financial waste.
SnowSQL – Snowflake has developed its own fork of the popular structured query language (SQL), with specific hooks and integrations that allow for seamless communication across their platform.
This is a command-line interface (CLI) that allows you to interface with both databases and their master infrastructure nodes. With SnowSQL, you can execute SQL queries, perform all data definition language (DDL) and data manipulation language (DML) functions. You also gain access to a set of external connectors, such as their Python, Spark, and Node.js drivers and connectors.
Snowpipe - For smaller influxes of data, you can use Snowpipe, a serverless compute model that was built to manage load capacity and provision just enough resources to meet these micro-demands. Snowpipe can interface with both Amazon S3 instances and Azure Blob storage instances.
Trianz is a leading data warehousing consulting firm, with decades of experience helping our clients build an efficient and compliant database infrastructure. We’ve partnered with Snowflake, offering industry-leading assessment and implementation services that fully comply with their MSP requirements.
Get in touch with our data warehousing team and move to a serverless future with Snowflake and Trianz today!
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