Google Cloud has some fantastic offerings in the Big Data, Machine Learning and Serverless spaces. Many of these can be utilised in addition to AWS or Azure, whether you're copying the data across or analysing it in-situ. GCP is happy to be “one of the clouds” rather than “the cloud” for its customers. 

For experienced AWS/Azure practitioners, learning GCP is mostly about learning different names for similar offerings, such as Google Compute Engine for AWS EC2 or Azure Virtual Machines. The simplest way is to build something small and familiar that already exists in an AWS or Azure environment.

Google Cloud also offers lots of free online training. For cloud newbies, start with the Cloud Digital Leader Certification.

GCP offers several services that can interact with data in AWS or Azure. You can also look to enhance your existing systems using GCP’s Machine Learning APIs, for example, to classify documents or images, provide translations or determine sentiment in customer reviews.

Google Cloud has a security and compliance section with information on the assurance checks and compliance details in response to regions, industries and a specific country’s regulations. These include GDPR in Europe and the 14 Cloud Security Principles from the UK’s National Security Centre (NCSC).

As with all computers, security is a big concern, and misconfiguration can happen. An on-premises data centre can be compromised via physical security or a cyber-attack too. As well as physical security, Google Cloud provides a suite of products to control identity and access management, protect secrets and keys, identify and redact sensitive data, and detect and prevent threats.

We know companies who have moved large amounts of data or CPU-heavy workloads to GCP and experience significant cost reductions, or a large performance increase, following the migration.

Many customers opt for a multi-cloud approach. This allows you to take advantage of different specialised services in each cloud, use pricing variations to your advantage, and ensure your workload is robust and scalable even if there is a major outage in a cloud vendor. Some GCP services specifically cater for hybrid on-premises/GCP or multi-cloud scenarios.

There are services that you can use to simplify the multi-cloud approach. For example, you may have containers running in AWS, Azure, on-premise, and in GCP. Rather than managing each cluster separately, you can use Google Anthos to manage all your container workloads centrally.

You may also have lots of legacy data spread over AWS and Azure. You can use this data by allowing your users to search through all data, no matter where it is stored. This can be achieved by deploying Google BigQuery Omni. Data in both (or all three) clouds can be searched using one simple SQL query thanks to the power of BigQuery.

Many different approaches can be used, such as HTTPS and firewalls, VPN, Network Interconnects, and VPCs. Our architects will work with you to ensure your workload and its data is understood and will design an appropriate solution that meets all your requirements.

If you have Google Workspace, then this is already 95% done! We can work with you to ensure best-practices are considered and setup appropriate IAM permissions. If you do not have Google Workspace, we can help integrate with your Azure or on-premise Active Directory.