Teradata to BigQuery Migration

Teradata to BigQuery Migration Services

Reduce Total Cost of Ownership (TCO) for your data assets while having a petabyte–scale, fast, serverless enterprise data warehouse designed for smart analytics in Google Data Cloud.

Teradata to BigQuery Migration

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Reduce Total Cost of Ownership (TCO) for your data assets using BigQuery Migration Services

Teradata on-premises environments are now not keeping up with the modern analytics requirements and drive a lot of operational effort. Google BigQuery is the perfect match to consider as a target zone for your cloud migration.

Get the advantage of serverless, NoOps, scalable Google Cloud BigQuery environment for your enterprise data warehouse where all hardware and software are abstracted so you can focus on your data and get the advantage of bleeding edge data advancements of Google Data Cloud.

Why Perform Teradata to BigQuery Migration?

Lower TCO

Embrace a fully managed, NoOps enterprise data warehouse that provides unmatched speed, scalability, and security.

Accelerate Time to Insights

Leverage a unique data warehouse architecture and data processing capabilities that speed up analytics and shorten data visualization cycles.

Get a Tailored Pricing

Benefit from BigQuery’s pay-as-you-go service. You only pay for the data that you use to experience cost savings compared to traditional on-premises data warehouse solutions.

Reduce Deployment Cycles

Experience faster time to market for new business applications by enabling high data compression and minimizing data scanning in common data warehouse deployments.

Integrate with the GCP Ecosystem

Innovate faster by integrating Google BigQuery with other Google Cloud services that allow you to process data from IoT devices, build ML models, visualize real–time data, and more.

Embrace Advanced Analytics

Strengthen analytics capabilities by leveraging a range of built-in analytical functions and machine learning frameworks in a single cloud-based data warehouse platform.

What do we do?

Cloud Migration Assessment

We evaluate your Data Warehouse environment and assess the complexity of making the migration. The Assessment is focused on core business requirements. Then we 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.

Roadmap and Future State

We work with your expert staff to create a business, financial, architectural, and technical roadmap to migrating DWH to the cloud. 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 artifacts into BigQuery structures.

Database Migration Implementation

We carry out the migration following a well described migration plan, staged implementation strategy, delivering business benefit at each stage. Along with intensive testing to ensure data validity.

Support (GCP Managed Services)

We maintain the option of clients accessing support desk services for any duration following in-scope migration activities, to ensure extended operational excellence post migration.

Our Approach to Teradata to BigQuery Migration

Discover

In the initial phase, we focus on discovery and preparation. It is about providing stakeholders with an early opportunity to discover the existing use cases and raise initial concerns. We could also conduct an initial analysis of the expected outcomes. The discovery phase process consists of the following tasks: 

  1. Examine the value proposition of BigQuery and compare it to that of your current data warehouse.
  2. Perform an initial TCO analysis. 
  3. Establish which use cases are affected by the migration. 
  4. Model the characteristics of the underlying datasets and data pipelines you want to migrate in order to identify dependencies.

The planning phase is about taking the input from the discovery phase, assessing that input, and then using it to plan the actual cloud migration. This phase can be broken down into the following tasks:  

  1. Catalog and prioritize use cases 
  2. Define measures of success 
  3. Create a definition of “done” 
  4. Design and propose a proof-of-concept (POC), short-term state, and ideal end state 
  5. Create time and cost estimates 

After gathered information about your IT landscape, and created a prioritized backlog of use cases, you can group the use cases into workloads and proceed with the migration. A migration typically contains the following steps: 

  1. Setup and data governance 
  2. Migrate schema and data 
  3. Translate queries 
  4. Migrate business applications 
  5. Migrate upstream pipelines 
  6. Optimize performance 
  7. Verify and validate 
SUCCESS STORY

BigQuery Success Story

A large Canadian organization wanted to migrate their Hadoop-based on-premise data lake solution to the Cloud. The re-platforming needed to be completed within a strict timeline, as the client’s existing technical solution was scheduled to run out of support soon. The client had already decided to move to the Cloud and had selected Google Cloud Platform as their partner of choice.

The client wanted the migration to be as much lift-and-shift as possible, as some of their systems had been developed recently, and Adastra was brought in to plan and perform the migration.

High

availability and uptime

Fully

managed Google Cloud environment

0

capacity limitations

Plan Your Teradata to BigQuery Migration

Let’s assess your Teradata estate and define a BigQuery migration approach with clear cost, timeline and lower TCO.

Teradata to BigQuery Migration FAQ

Why should we consider migrating from Teradata to BigQuery?

Teradata on-premises environments often cannot keep up with modern analytics requirements and require a lot of operational effort. BigQuery is a scalable, serverless and cost-effective cloud data warehouse that removes hardware management, integrates with Google Cloud services and charges only for the resources you use, which lowers your total cost of ownership.

Yes. BigQuery supports standard SQL, so many Teradata queries migrate with minimal changes. Some queries need adjustments because of differences in syntax or functions. Adastra translates queries as part of the migration, defines patterns for converting Teradata artifacts into BigQuery structures and tests results intensively to confirm data validity at every stage.

BigQuery is built for massive data volumes and scales its resources automatically as workloads grow, without manual intervention. Teradata often requires additional hardware provisioning and capacity planning as data increases. With BigQuery’s serverless, NoOps architecture, hardware and software are abstracted, so your team focuses on data and analytics instead of infrastructure.

BigQuery uses pay-as-you-go pricing, so you pay only for the storage you use and the processing your queries consume. This is often more budget-friendly than the upfront investment and ongoing maintenance of Teradata. During discovery, Adastra performs an initial TCO analysis, so you see the expected savings before migration starts.

Yes. BigQuery is fully integrated with Google Cloud, so you can combine its data warehousing with services for data visualization, machine learning, IoT data processing and storage and backup. This lets you build ML models, visualize real-time data and innovate faster from one connected platform instead of stitching separate tools together.

Yes. BigQuery offers built-in analytical functions and supports integration with machine learning frameworks such as TensorFlow. You can perform complex analytics and run machine learning models directly on your data inside BigQuery, without moving it to a separate system. This shortens analytics cycles and makes advanced analytics available to more teams.

Adastra follows three phases. Discover: compare BigQuery with your current warehouse, run an initial TCO analysis and identify affected use cases. Plan: prioritize use cases, define success measures and estimate time and cost. Execute: set up governance, migrate schema and data, translate queries, migrate applications and pipelines, optimize and validate.

After the in-scope migration, you can access Adastra’s support desk through GCP managed services for as long as you need. This keeps operations running smoothly while your team adjusts. Adastra also stages the implementation to deliver business benefits at each stage, so value does not wait until the entire warehouse has moved.