As a Teradata Enterprise Data Warehouse owner, are you tired of:
- buying and hosting expensive proprietary hardware,
- patching operating systems,
- installing specialized database software,
- managing database servers,
- tuning database parameters,
- planning upgrades and downtime,
- worrying about increased data load times and ever-increasing data consumption needs,
- dreading that multi-million $ yearly license renewal.
4 Facts about Google BigQuery
- Did you know that a Google BigQuery project comes with a default 2,000 query execution slots that can be extended to more slots upon request? Does your on-premise Teradata Data Warehouse infrastructure have 2,000 slot elasticity to run analytical queries?
- Did you know that Google Cloud Platform bills separately for storage and query data processing? Are you overpaying Teradata for either compute or storage capacity due to static hardware configuration and pricing models?
- Did you know that Google Cloud Platform utilizes Petabit network to distribute your data across multiple regions for redundancy and high availability? Are you worried about your Teradata cross-data-center Data Warehouse failover and disaster recovery?
- Do you know how much time, effort and resources you are spending on managing Teradata Data Warehousing on-premise infrastructure complexity instead of focusing on data, insights and your customer?
Advantages of Google BigQuery
Migration of very large Data Warehouses from a Teradata platform to a Google BigQuery offers significant potential advantages:
- The elastic scalability of the cloud infrastructure eases cost/performance tradeoffs
- Data ingestion patterns can be simplified
- Integration with sophisticated cloud-based analytical toolsets is readily supported
- A serverless NoOps environment frees infrastructure maintenance burden allowing to refocus resources on data and business insights
Read our blog here where a series of articles, collaboratively written by data and solution architects at Myers-Holum, Inc, and Google, describe an architectural framework for conversions of data warehouses from Teradata to the Google Cloud Platform. The series will explore common architectural patterns for Teradata data warehouses and outline best-practice guidelines for porting these patterns to the Google Cloud Platform toolset.
Myers-Holum is here to help
Myers-Holum can assist with navigating considerations of performance, cost, reliability, and security for including cloud platforms in your mix for data warehouse deployments.
MHI uses a model-based approach and metadata-wise tools to efficiently migrate data warehouse components from traditional to cloud platforms, translating schemas and ingestion and consumption processes for optimal performance in the new architecture. We maintain high standards for metadata integrity and governance, data lineage, and code discipline.
We evaluate where it makes sense to include Cloud platforms in your Data Warehouse environment, and assess the complexity of making the migration. The Assessment is focused on core business requirements and three different Teradata data warehouse implementation architectural styles, canonical data models used if any, layer architectures and semantic layers implementations. We then review existing batch and streaming source data capture to preserve your existing investment. We analyze data consumption patterns including frequency, resources, and volume. And finally, we review your Data Governance programs in place.
We propose Google Cloud Platform products to be used and best practices to be applied based on the assessment. We define data modeling patterns and detail examples for converting Teradata semantic layer and star schema into BigQuery repeated nested structures. We suggest source data capture approach ETL vs. ELT vs. UPM Dataflow, and tooling within Google Cloud Platform utilizing either native Cloud Dataflow capabilities and/or 3rd party integration tools, with lift and carry as much as possible. For data ingestion into the Google BigQuery, we define connectivity to on-premise and cloud data sources. For data consumption, we recommend an approach that utilizes best of breed solution either using existing analytics and reporting tools or newly available analytics tooling to democratize of data analytics. It’s important to define data security and access models, and auditing approach for enterprise data in the cloud. We suggest adjustments to Data Governance programs for the cloud. Finally, we recommend aspirational machine learning data insights opportunities utilizing CloudML, Tensoflow, Google Cloud AI APIs.
We work with your expert staff to create a business, financial, architectural and technical roadmap to migrating DW to the cloud. Special attention is paid on ROI and iterative delivery to show progress early and often.
We carry out the migration following a carefully planned, staged implementation strategy, delivering real business benefit at each stage.
Contact us at email@example.com or 646.844.4493 to learn more about Teradata to BigQuery migrations!