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      Data Warehouse

      SnowFlake: A Clean and Fresh Approach to Cloud Data Warehousing

      Jul 22, 2020

      4 minute read

      Massive data volumes are driving the rising demand for effective business intelligence solutions.

      With a data warehouse, you get enhanced business intelligence that enables you to boost operational efficiency, make informed decisions, and improve customer retention.

      In fact, the global data warehousing market is projected to expand by more than 12% by 2025.

      In this blog post, we’ll take a look at the key benefits of cloud data warehousing as well as what makes SnowFlake a highly-effective cloud solution, to help you power business growth with cloud analytics.

      So, let’s get started!

      Key benefits of cloud data warehousing

      • Spend less: It is expensive to store data on-premises. With compute and storage being separate in a cloud data warehouse, purchase only what you require.
      • Deploy quickly: You no longer need to calculate how much storage and compute power you require. With a cloud solution, you can quickly build your data warehouse, data mart, or sandbox.
      • Strengthen capabilities: Get quicker and smarter business insights with improved availability, scalability, and performance. A cloud data warehouse helps you drive business growth with analytics and data integration.
      • Get seamless self-service: Access fully automated management, patching, as well as upgrades. Focus on giving your business more strategic value.

      SnowFlake’s unique architecture

      SnowFlake’s architecture is made up of three key layers:

      SnowFlake: A Clean and Fresh Approach to Cloud Data Warehousing

      Database storage

      When loading data, SnowFlake reorganizes it into a columnar format for storage over the cloud.

      This is carried out using compression and optimization techniques.

      In addition to this, SnowFlake uses objects to handle aspects of data storage, such as metadata, compression, and file structure.

      Access these objects with SQL query operations provided by SnowFlake.

      Query processing

      With regard to the cloud service layer, SnowFlake accomplishes most of its part by optimizing the plan or scope of the data that is to be processed.

      Once the plan is ready, the only job remaining in the query processing layer is the distribution of this plan amongst the worker nodes available on virtual warehouses.

      Furthermore, SnowFlake tracks and notes the efficiency of this task for auditing purposes.

      Cloud services

      This layer may also be referred to as the ‘brain’ of a cloud storage service.

      With its multi-tenant nature, the layer helps provide hassle-free cloud services.

      Elements such as authentication, infrastructure & metadata management, query parsing & optimization, and access control are tied together for effective performance and scalable solutions.

      Make smarter business decisions with smarter insights from a range of data analytics solutions!

      Learn more.

      What Makes Snowflake an Effective Cloud Data Warehousing Solution?

      Metadata management

      SnowFlake stores metadata in a very crisp manner.

      This is done by maintaining a single key for usage statistics, as well as metadata information.

      In addition to this, SnowFlake’s metadata processing is automatic.

      This means that as your compute resources scale-out, your data warehouse can scale near infinitely.

      Micro-partitions and data clustering

      SnowFlake uses micro-partitioning to break a table down into a compressed set of columns.

      This pruning technique helps you fetch statistical data from the micro-partitions being computed.

      Furthermore, micro-partitions are derived automatically.

      This means that you don’t need to manage them or define them up-front.

      SnowFlake collects and records clustering metadata to help you speed up querying by avoiding the unnecessary scanning of micro-partitions.

      So, here’s how this works:

      SnowFlake: A Clean and Fresh Approach to Cloud Data Warehousing

      Separation of compute and storage

      Building a distributed storage elastic enough to handle massively parallel processing (MPP) workload requires significant effort in terms of development, deployment, and support.

      With SnowFlake, you get compute separated from storage, where stateless worker pools (virtual warehouses) talk to the cloud storage to read and write data.

      This allows you to scale your data warehouse in the manner you want, with the ability to increase compute power when required.

      Stateless compute nodes

      With the virtual warehouse being a set of stateless worker nodes, you can scale these as required.

      In addition to this, you can run several virtual warehouses over the data in your cloud storage.


      With Fail-safe, SnowFlake ensures data protection in case of hardware failure.

      This is a cost-effective option as compared to backup, during which the remaining risk is eliminated in addition to scaling with your data.

      360° integration support

      Working with a wide range of industry-leading tools and technologies, SnowFlake enables access through multiple connectors, drivers, programming languages, and utilities.

      SnowFlake: A Clean and Fresh Approach to Cloud Data Warehousing

      Wrapping up

      SnowFlake has been delivering a clean and fresh approach to cloud data warehousing for both data administrators and users to get the most out of the solution.

      In this blog post, we looked at the key benefits of cloud data warehousing as well as what makes SnowFlake a highly-effective cloud solution, to help you power business growth with cloud analytics.

      Drive Business Success by Accelerating Cloud Analytics With Snowflake! Get in Touch.

      At Grazitti Interactive, the data analytics team is dedicated to helping you power business growth with enterprise data warehousing.

      Get started today!

      You can also write to us [email protected] and we will take it from there.

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