Optimizing Business Efficiency with Snowflake


Decision-makers in finance, healthcare, retail, education and other verticals rely on the accurate processing and analyzing of data to direct corporate strategy. The need to analyze Big Data more efficiently becomes increasingly significant as the volume of data grows. Here’s when Snowflake helped bring that efficiency.

Snowflake’s computing and storage tiers are entirely independent of one another, and the platform’s two tiers are almost instantly flexible. With Snowflake, there is no longer any need for in-depth resource planning, agonizing over workload schedules, or forbidding the addition of new workloads out of concern for disc and CPU capacity restrictions.

The Power of Cloud

snowflake cloud
Source Credit: Snowflake

Snowflake can almost instantaneously scale as a cloud data platform to accommodate anticipated, unforeseen, or planned growth. This means that as your needs change over time, you pay for a growing and shrinking quantity of storage and computation rather than a fixed, limited amount.

The cloud creates access to near-infinite, low-cost storage, the capability to scale up/down, outsourcing the challenging operations tasks of data warehousing management and security to the cloud vendor and plus potential to pay for only the storage and computing resources actually used, when you use them.

Having worked numerous times over the years with everything from Hadoop to Teradata/Oracle as well as having been deeply involved on migration projects moving workloads from on-premise environments to the cloud and the existing data ingestion process was stable and mature. Daily, an ETL script loads the raw CSV/TXT/JSON file from the file system and inserts the file’s contents into a SQL table which is stored in ORC/CSV format with snappy compression.

In order to avoid re-implementation of the ETL process, the first constraint was the cloud data warehouse needed to support the ORC file format. There are two cloud data warehouses that support ORC file format i.e. Snowflake and Amazon Redshift Spectrum. As external files located in Amazon S3, both Snowflake and Redshift Spectrum permit queries on ORC files. However, Snowflake edged out Redshift Spectrum for its expertise to also load and transform ORC data files directly into Snowflake.

Read more on our blogs Cluster Analysis 101

Features of Snowflake Architecture


  • Snowflake stores semi-structured data such as JSON, Avro, ORC, Parquet, and XML alongside your relational data. Query all data with standard i.e. ACID-compliant SQL, and dot notation.
  • Support concurrent use cases with independent virtual warehouses (compute clusters) that reference your common data
  • Maintain your investment and assets in the skills and tools a user already rely on for your data analytics.
  • Easily forge one-to-one, one-to-many, and many-to-many data sharing relationships, so your business units, subsidiaries, and partners can securely query read-only, centralized data.

Optimizing Business Efficiency with Snowflake


Snowflake accredit the data-driven enterprise with instant elasticity, secure data sharing and per-second pricing. Its built-for-the-cloud architecture combines the power of data warehousing, the flexibility of big data platforms, and the elasticity of the cloud. Snowflake is an APN Advanced Technology Partner and has achieved Data & Analytics Competency.

The Snowflake frame is a modern data warehouse which is effective, affordable, and accessible to all data users within the organization. In the next part, we’ll introduce the Architecture, Pros/Cons and Table Design Considerations.

Most Popular

Let's Connect

Please enable JavaScript in your browser to complete this form.

Join Factspan Community

Subscribe to our newsletter

Related Articles

Add Your Heading Text Here

Case Studies

GenAI-Powered Transformation: Optimizing Hospital Reporting for Improved Patient Care

Executive Summary In a strategic initiative, a prominent hospital chain in …

Read More ...
Case Studies

Transforming Merchandising Efficiency – Data Migration with MuleSoft and Snowflake

Executive Summary Factspan Analytics addressed a critical business challenge for a …

Read More ...

Meta’s LLAMA 2 Vs Open AI’s ChatGPT

Explore the world of cutting-edge AI with a detailed analysis of Meta’s LLaMA and OpenAI’s ChatGPT. Uncover their workings, advantages, and considerations to help you make the right choice for your specific needs. Dive into the future of AI and its profound impact on content creation and data analysis.

Read More ...

Data Contract Implementation in a Kafka Project: Ensuring Data Consistency and Adaptability

Data contracts are essential for ensuring data consistency and adaptability in data engineering projects. This blog explains how to implement data contract in a Kafka project and how it can be utilized to solve data quality and inconsistency issues.

Read More ...
Webinar & Events

Chaos to Control in Generative AI era: Laying the foundation through Data Governance & Engineering

In today’s data-driven world, businesses face numerous challenges, from ensuring consistent …

Read More ...

CDP: A band-aid solution?

Step into the world of Customer Data Platforms (CDPs) with our captivating blog, designed to guide you through every angle. Discover the origin story of CDPs – why they stepped into the spotlight. Uncover their true essence and explore the four common categories they belong to. Delve into real-life scenarios with eight compelling use cases that are revolutionizing businesses today. Tackle the question: are CDPs a quick fix or a sustainable solution? And don’t shy away from addressing the challenges that come with CDP territory. Wrapping it all up, you’ll find key takeaways that provide fresh insights into this dynamic technology.

Read More ...