Snowflake: How the Cloud-agnostic Data Warehouse Solution is Winning the Race in Data Management?

Traditionally, data warehouses consolidated business data from in-house applications and databases along with SaaS (Software as a Service) platforms to serve a single repository for an organization to consult and make decisions regarding data and business intelligence. Data warehouses enabled analytics reporting and analytical processing that transactional databases of the past did not offer. It offloaded analytics processing of data and offered a faster alternative using a columnar data store allowing business users quick access to relevant data elements.

But increasingly, on-premises data warehouses are becoming unwieldy and expensive. Although these offer total control and responsibility, database administrators and analysts, system engineers and administrators alongside security specialists must design, procure, and install these systems. All the handle moves, adds and changes – administration and maintenance of hardware and software is a responsibility of the data managers. Additionally, on-premises data warehouses cannot accommodate bursts of data activity that require memory and more computing power. Organizations must also revamp the size of its data warehouse to handle peak load even if that level of usage occurs only intermittently. Scaling up to meet changing needs is often cumbersome and requires replacing old systems. Cloud data warehouses are now becoming more popular wherein a database stored or a managed service in a public cloud environment can be optimized for scalable analytics and business intelligence. Through cloud data warehousing, IT teams are no longer confined by physical data centers and users can alter databases for catering different projects and business requirements. With its shared pool of compute resources, cloud data warehouses offer flexibility in every shape, form, and scale.

As workforces go remote and organizations scale up their business units, bigger data warehouses and expanded data storage capacities are the need of the hour. Unwanted hard drives and physical additions to storage hardware need space, energy, and high costs. Cloud data warehouses can enable organizations to scale up and down as per subscription packages and there is no need of configuration changes. Seamless connectivity and high latency of data transactions outside local networks accessed from multiple locations warrant the need of cloud data warehouses.

Cloud based solutions like Snowflake also prioritize a security-first approach in data centric transactions offering IT teams and others even more flexibility. There are a range of features in this software useful for storage, data management and consolidation that can acquire and curate data from different environments. It is a SaaS-based cloud agnostic platform that helps teams allocate compute resources of various cloud vendors to the same database for querying and loading purposes. It enables data managers to transform data and find and remove duplications and assure consistency in data management and storage. Snowflake provides a single and optimized platform that provides a cloud-built only data storage facility. It offers a faster and safer managed access into the entire network of data and has a core architecture that can support a wide range of workloads including a single integrated platform for developing modern data applications.

Founded in 2014, Snowflake offers a reasonable pricing for a data warehouse tool. This flagship solution operates on a multi-cloud environment on leading clouds like Amazon Web Services and Azure. Its system is based on its own workload demands and has the robust ability to handle a heterogeneous data infrastructure. It also has a wide array of formats like Parquet and Optimized Row Columnar. It has rapid and highly flexible scalability for organizations that rely on cost effective data warehouse solutions. Its multi-tenant design allows for the real-time data exchange throughout the organization and there is no need to relocate data. To lower costs, Snowflake features auto-scaling and auto-suspend wherein data clusters can be set as inactive for a set duration.

Query processing is a key aspect in Snowflake implantation. The compute layer of Snowflake is made of a virtual cloud data warehouse that lets you analyze data through requests. Each Snowflake virtual warehouse is an independent cluster and they do not compete for computing resources nor affecting the performance of each other. Workload concurrency is therefore never a problem. Snowflake enables organizations to not just automate data management, but also investigates storage, security, governance, availability, and data resiliency. This drives scalability, optimizes costs, reduces downtime, and helps improve operational efficiency. It automates data replication for fast recovery. It eliminates extract, transfer, and load (ETL) and data silos with a seamless cross-cloud and cross-region connections and data sharing. With a single shared data source, teams across the enterprise and the business ecosystem can be sure they are working from a single source of truth enabling remote collaboration and decision making easy and fast. Additionally, Snowflake Data Marketplace offers third party data and lets you connect with Snowflake customers to extend workflows with data services and third-party applications.

With a performant and elastic architecture, you can scale Snowflake up and down on the fly or automatically with per-second pricing. You can get a single experience across major public clouds without batting an eyelid about maintenance and administration worries. Also, critically, Snowflake empowers individual users to perform their own targeted, self-service data manipulation irrespective of the skill level as there is no need to write scripts or ask data teams for specialized assistance. They can finish this task quickly and simply by using familiar languages like SQL. Snowflake enables disparate functional teams to work in cohesion without depending much on the data team for basic tasks. Priority tasks such as setting and enforcing data access policies can be undertaken by the data team instead.  According to Shrikant Mani Bhaskar, Head of Data Management practice at Datazymes, "Several big Pharma companies have already adopted Snowflake as their Datawarehouse and soon it will evolve as big Data Exchange Hub for several companies which will speed up the development and implementation process of Data warehousing for big companies with improved data quality."