Build a faster, more scalable & cost-optimised data warehouse in the Cloud.
Limitless data storage, secure and accessible data to globally distributed teams, and dynamic processing of large data sets in real-time.
They're only a few of the benefits linked to moving to a cloud-based data warehouse.
As the cornerstone of Business Intelligence, BigQuery permits uninterrupted data transformation, integration and analysis. Get your projects up and running in virtually no time. Modernise your data infrastructure with Google’s proven migration methodology & our services.
Streamline your workloads and run analytics with the ML-powered BigQuery.
See as much as up to a 52% decrease in your TCO.
Trust Big Query’s next gen cybersecurity infrastructure to keep your data safe.
Smooth migration with no downtime.
Build & operationalise ML models directly within your data warehouse with BigQuery ML. Train powerful models on structured data with Cloud Machine Learning Engine and TensorFlow integration.
Reduce the need for code rewrite & provide advanced SQL features with a standard ANSI:2011 compliant SQL dialect - supported by BigQuery.
On-demand or flat-rate pricing models available for you to choose the pricing that fits you best.
Get the resources you need, when you need them. Skip the overhead of infrastructure or system engineering.
Stay up to date with your business & predict business outcomes, in real-time. Meet BigQuery’s high-speed streaming insertion API, which ensures a powerful foundation for real-time analytics.
Benefit from a 26%–34% lower three-year TCO than Cloud data warehouse alternatives.
Ensure security, governance and reliability with BigQuery's controls and default data encryption.
Securely access and share analytical insights in your organisation with a few clicks.
Add beautiful data visualisation on top of it with BI tools like Looker.
Speed up your data & analytics projects up to 3x by automating repeating processes like config management, versioning, orchestration, etc.
Standardise structures & processes and replicate data & analytics projects across your organisation.
Easily ensure data governance.
Get an out-of-the-box top notch DevOps ecosystem for your data & analytics projects.
This includes deployment stability, readable declarative code, fast iteration, fully managed CI/CD, Infrastructure-as-code & more.
Günther Ghijsels
CIO & CDO, Randstad Group Belgium
BigQuery ML enables data scientists and data analysts to build and operationalize ML models on planet-scale structured or semi-structured data, directly inside BigQuery, using simple SQL—in a fraction of the time. Export BigQuery ML models for online prediction into Cloud AI Platform or your own serving layer. Learn more about the models we currently support.
BigQuery BI Engine is a blazing-fast in-memory analysis service for BigQuery that allows users to analyze large and complex datasets interactively with sub-second query response time and high concurrency. BigQuery BI Engine seamlessly integrates with familiar tools like Data Studio and will help accelerate data exploration and analysis for Looker, Sheets, and our BI partners in the coming months.
BigQuery GIS uniquely combines the serverless architecture of BigQuery with native support for geospatial analysis, so you can augment your analytics workflows with location intelligence. Simplify your analyses, see spatial data in fresh ways, and unlock entirely new lines of business with support for arbitrary points, lines, polygons, and multi-polygons in common geospatial data formats.
An implementation blueprint created in the pre-study phase with customer stakeholders.
Key points covered in this document are
The ETL pipelines are tested and delivered along with the Data Warehouse. Additionally, the ETL pipeline should be scalable.
Included in the delivery is the CI /CD for ETL and Schema changes of the Data Warehouse.
The Data Warehouse will be reconciled against the previous data warehouse and signed off by the customer.
Any functional visualisations on the previous data warehouse will be migrated over to the new data warehouse and also reconciled to ensure that the reports show the same results.
The implementation of Looker may also be included in this deliverable, along with the relevant best practices.
Cost optimisations on BigQuery will be conducted after the usage of the new data warehouse.
Suggestions such as converting to a flat-rate plan may be warranted if usage matches the need.
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