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
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 Alongside the commonly known traditional, on-premises DW shortcomings (cost, inflexibility, outdated technology, performance issues), there are also inherent architectural issues.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- Outdated Technology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
Snowflake simplifies data engineering, delivering performance so organisations can focus on getting value from their data instead of managing the pipelines and infrastructure.
Data Lake
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.
Data Warehouse
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.
Data Science
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.
Data Applications
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.
Data Exchange
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
Audit
First discussion on key objectives
Set of interviews with key stakeholders
Identify fundamental problems
Overview current infrastructure
Presentation of findings
Show process to undertake
Proof of Concept
4 Weeks
Prove transition to cloud is possible
Demonstrate immediate value
Deployment
Typically 3-6 Months (depending on size), up to 12 months for migrations
Incremental, phased approached
Demonstrating value along the way