We are moving Analytics to the Cloud

In today’s fast-moving and competitive environment, businesses need to be agile, flexible, and responsive in an increasingly competitive landscape. Getting faster, more accessible, and more scalable analytics is now a necessity for many organisations.
We have the technical knowledge, industry experience and proven deployment record required to take your analytics to the next level. If you are looking for assistance in accelerating your transition to cloud-based Data Warehouses and Data Lakes, get in touch to see how we can help.
Benefits of Moving to Cloud-based Analytics
Actionable Insights
Speed to insight. Break down data silos and make information available across all business units. Generate information for reports in real-time.
Optimise Internal Processes
Reducing operating costs and scale your analytics easily. Disaster recovery made much simpler and faster, with zero maintenance.
Reduce Costs
Pay only for the resources you use. The pay-per-use model of cloud analytics allows for businesses to optimise their operational costs and avoid the costs of maintaining expensive legacy systems.
The Limitations of Legacy Data Warehousing
Traditionally, businesses have built/used expensive and inflexible on-premise Data Warehousing and Data Lake solutions. Alongside the commonly known traditional, on-premises DW shortcomings (cost, inflexibility, outdated technology, performance issues), there are also inherent architectural issues.
Legacy data warehouse architectures contain physical limitations and complexities inherent to their design that prevent high levels of scalability and agility – adding new physical capacity is costly and disruptive.
On-premises environments often store files in their native format, which means a significant amount of effort is required to query and deliver insights from semi-structured data.
Simply moving a data platform to the cloud does not solve these challenges, as using the same DW platform on the cloud can just replicate the same physical scaling limitations in a cloud environment.
What is needed is completely new data platform and relational database management system that can deliver a dynamic infrastructure with instant, disruption-free, scalability and performance-as-a-service at any cloud scale, all at a fraction of the cost of traditional systems.
Expensive
Outdated Technology
Inflexible
Physical Scaling Limitations
Poor Security
Poor Performance
Slow Speed to insight
No Single Source of Truth
Snowflake Cloud Data Platform

Snowflake’s cloud data platform enables a wide variety of workloads and applications on any cloud, including data warehouses, data lakes, data pipelines, and data exchanges as well as business intelligence, data science, and data analytics applications.
Its multi-cluster shared data architecture consolidates data warehouses, data marts, and data lakes into a single source of truth that powers multiple types of analytics.
Snowflake’s architecture is built to be cloud- agnostic and it can distribute data across regions or even across cloud providers, so organisations can mix and match clouds as they see fit.
Snowflake’s Core Workloads

- Data Engineering
- Data Lake
- Data Warehouse
- Data Science
- Data Applications
- Data Exchange
Snowflake simplifies data engineering, delivering performance so organisations can focus on getting value from their data instead of managing the pipelines and infrastructure.
Using Snowflake as either a standalone data lake or as a means to augment an existing one, delivers the best value in the market for storage, transformations, and data warehousing within one platform to serve all business needs.
Snowflake’s support for data warehousing and analytics provides a low-maintenance, cost-effective way for organisations to consolidate all their data silos into a single source of truth they can query to get results fast. By providing consistently fast queries, more users analyse more data and collaborate with their peers.
Snowflake helps data scientists operate quickly and efficiently by providing a centralised source of high- performance data to a robust ecosystem of data science partners that handle modelling and training algorithms. Partner-provided output is fed back into Snowflake where it’s easily accessible to technical and nontechnical users.
Snowflake provides a unique architecture that enables the development of modern applications without managing complex data infrastructure. Because Snowflake is a fully managed data platform with features such as high concurrency, scalability, automatic scaling, and support for ANSI SQL, developers can quickly deliver data applications that are fast and scalable.
Snowflake Data Marketplace enables instant, frictionless, secure sharing of live data within and between organisations. Unlike traditional data sharing methods such as email, FTP, cloud storage (Amazon S3, Box), and APIs, Snowflake eliminates data movement, does not require the data consumer to reconstruct data (ETL), and provides direct access to live data in a secure environment. Snowflake Data Marketplace allows companies to grant instant access to ready-to-use data to any number of data consumers without any data movement, copying, or complex pipelines.
Understanding Snowflake Architecture
Snowflake was created to support powerful analytics. The service, which is available on AWS and Azure (and GCP in some regions), runs completely on public cloud architecture. There is no hardware or software (virtual or physical) to install, configure or manage. Ongoing maintenance, management, and tuning is handled by Snowflake.
Snowflake is composed of a three-layer design with separate storage, compute, and cloud services layers. The architecture excels because, while compute and storage resources are physically separate, they are logically part of a single, integrated data platform system that provides nondisruptive scaling. The unique multi-cluster shared data architecture delivers performance, scale, elasticity, and concurrency.


We are official Snowflake Partners
As Snowflake Partners, we can assist in your deployment of Snowflake with professional consulting services. We have the knowledge, expertise and proven track record to help you on your journey to unlocking business value – whether you are initiating a new project, optimising your current deployment or migrating legacy systems.

If you are interested in seeing how we can help you achieve your business objectives with Snowflake, please get in contact. From here, we will put you in touch with one of our technical experts to identify your specific needs.
Implementation Approach
- 1 - Audit
- 2 - Presentation of findings
- 3 - Proof of Concept
- 4 - Deployment
First discussion on key objectives
- Set of interviews with key stakeholders
- Identify fundamental problems
- Overview current infrastructure
Show process to undertake
4 Weeks
- Prove transition to cloud is possible
- Demonstrate immediate value
Typically 3-6 Months (depending on size), up to 12 months for migrations
- Incremental, phased approached
- Demonstrating value along the way
Change how you think about Data Warehousing
Per-second Pricing
Automatic scaling allows you to pay for what you use, eliminating the cost of an idle warehouse.
Per-second Pricing
Pay for the compute and storage that you actually use. Snowflake’s pricing structure allows you to:
- Turn compute resources on and off, meaning you only pay for what you need
- Store unlimited amounts of data at affordable cloud rates
- Grow your analytics infrastructure with linear cost scalability
- Per-second pricing model means you to avoid being charged for idle compute time
- Pay for what you use – no hidden quotas, price bumps or premiums
To find out more about Snowflake’s straightforward pricing options and structures, click here.
High Performance
Built to maintain performance, even at heavy workloads.
High Performance
The Snowflake data warehouse is not built on an existing database or ‘big data’ software platform such as Hadoop. It uses a new SQL data base engine with architecture specifically designed for the cloud. Snowflake’s unique architecture consists of three different layers that allow for key performance benefits:
Database Storage
Data loaded into Snowflake is reorganised into its internal optimised, compressed, columnar format. This data is then stored in the cloud. Snowflake manages all aspect of the data storage, including organisation, file size, structure, compression, metadata and statistics. The data objects that are stored are not directly visible or accessible by customers and can only be accessed through SQL query operations run through Snowflake.
Query Processing
In the processing layer, Snowflake queries using ‘virtual warehouses’. Each virtual warehouse is an MPP compute cluster consisting of multiple compute nodes. The warehouses are independent compute clusters that do not share compute resources with the other warehouses. This means that each warehouse has no impact on the performance of other virtual warehouses.
Cloud Services
The cloud services layer ties together all the different components of Snowflake in order to process user requests. Among the services in this layer include:
- Authentication
- Infrastructure management
- Metadata management
- Query parsing and optimisation
- Access control
Unlimited Concurrency
Compute multiple clusters simultaneously on the same data without affecting performance.
Unlimited Concurrency
Snowflake can support unlimited concurrency with its unique multi-cluster, shared data architecture. This allows multiple compute clusters to operate simultaneously on the same data without degrading performance.
Furthermore, Snowflake can scale automatically with concurrency demands by transparently adding compute resources during peak periods and scaling down when larger loads are not needed.
Share Securely
Real time, high performance data governance allows secure data sharing in real time.
Share Securely
Snowflake has a fully managed service layer that enforces comprehensive security measures, enables data governance, and ensures ACID-compliant transaction integrity.
Snowflake built security into the design of the product from the beginning. Snowflake achieves best practice for managing an enterprise’s data sources, protecting their data through encrypting everything. This includes encryption keys and the use of Amazon CloudHSM to store and use Snowflake’s master keys.
One of the ways in which Snowflake stress tests its systems is through systematically running ‘penetration tests’ on its own systems. These are controlled attempts to exploit vulnerabilities to determine whether unauthorised access or other malicious activity is possible within the target environment. Snowflake engages globally recognised experts to perform these tests, the methodology of which can be found here.
Snowflake’s focus on security is demonstrated through their stellar portfolio of compliance reports and certifications. To view the reports and certifications demonstrating Snowflake’s commitment to enforcing the highest global security standards, please click here.
Multiple Clouds
Store your data where you want to, with multi-cloud functionality.
Multiple Clouds
Implement a multic-cloud strategy to lower costs and gain flexibility. Adopting a cross-cloud platform helps:
- Enable different business units to use a public cloud that best matches their needs and promotes productivity
- Capitalise on regional footprints to leverage the best cloud provider by region based on presence, capacity, and services for local teams
- Protect against a single cloud provider’s multi-region outage, ensuring uptime and SLA adherence