Switching from traditional data warehouse to BigQuery

bigquery

Enterprises have always been relying on traditional on-premises data warehouses to collect and store their most valuable data. These traditional data warehouses can be costly, inflexible and difficult to maintain. Enterprises need an easy, scalable way to store all that data, as well as to take advantage of advanced analytic tools that can help them find valuable insights. As a result, many are turning to cloud data warehousing solutions like BigQuery.

BigQuery is Google Cloud’s serverless, highly scalable, low-cost enterprise data warehouse designed to make all data analysts productive. There’s no infrastructure to manage, so you can focus on finding meaningful insights using familiar Standard SQL.

bigquery

Enterprise Strategy Group (ESG) released a report examining the economic advantages of migrating enterprise data warehouse workloads to BigQuery. They developed a three-year total-cost-of-ownership (TCO) model that compared the expected costs and benefits of upgrading an on-premises data warehouse, migrating to a cloud-based solution provided by the same on-premises vendor, or redesigning and migrating data warehouse workloads to BigQuery. ESG found that an organisation could potentially reduce its overall three-year costs by 52% versus the on-premises equivalent and by 41% when compared to an AWS deployment.

Starting your journey to a modern data warehouse

A typical data warehouse migration requires three distinct steps:

  1. Data migration: the transfer of the actual data contents from the data warehouse from the source to the destination system.
  2. Schema migration: the transfer of metadata definitions and topologies.
  3. Workload migration: the transfer of workloads that include extract, transform, load (ETL) pipes, processing jobs, stored-procedures, reports and dashboards.

Using BigQuery’s data warehouse migration utility, you will be able to automate migrating data and schema to BigQuery and significantly reduce the migration time.

At PointStar, we equip you with architecture and design guidance from Google-Cloud-certified engineers, training and usage credits to help speed up your modernisation process.

Here’s how it works:

Step 1: Planning consultation

You’ll receive expert advice from us and you’ll work with our professional services team on your proof of concept.

Step 2: Training

You’ll get training from us to deepen your understanding of BigQuery and related GCP services.

Step 3: Expert design guidance

Our Google-Cloud-certified engineers will provide you with architecture design guidance, through personalised deep-dive workshops.

Step 4: Migration support

Google Cloud’s professional services organisation have helped enterprises all over the world migrate their traditional data warehouses to BigQuery.

Leading global enterprises like 20th Century Fox, Domino’s Pizza, Heathrow Airport, HSBC, Lloyds Bank UK and The New York Times rely on BigQuery for their data analysis needs, helping them do everything from break down data silos to jump-start their predictive analytics journey. These processes have greatly reduced costs and man-hours.

Learn more about our Professional Services for Google Cloud Platform

https://www.point-star.com/services/consulting-architecture/professional-services-for-google-cloud-platform/

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