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

Teradata to BigQuery FAQs

Why should we consider migrating from Teradata to BigQuery?

Migrating from Teradata to BigQuery offers several advantages. BigQuery provides a highly scalable, serverless, and cost-effective cloud-based data warehouse solution. It eliminates the need for manual hardware management, offers seamless integration with Google Cloud services, and allows organizations to pay only for the resources they use.

Yes, BigQuery supports standard SQL, making it compatible with many Teradata SQL queries. While most queries can be migrated with minimal modifications, some may require adjustments due to differences in syntax or functions between the two platforms. Google Cloud offers tools and documentation to assist with the migration process.

BigQuery is designed to handle massive amounts of data and automatically scales its resources as needed. It can effortlessly accommodate growing workloads without requiring manual intervention. Unlike Teradata, which may require additional hardware provisioning and capacity planning as data volumes increase, BigQuery’s serverless architecture ensures seamless scalability.

BigQuery follows a pay-as-you-go pricing model, which means you only pay for the storage used and the data processing resources consumed during queries. This cost-effective approach can be more budget-friendly compared to the upfront investment and ongoing maintenance expenses associated with Teradata.

Yes, BigQuery is fully integrated with the Google Cloud platform. This means you can easily combine BigQuery’s data warehousing capabilities with other cloud services like Google Data Studio for data visualization, Cloud Machine Learning Engine for advanced analytics, and Google Cloud Storage for data storage and backup.

Short answer is “Yes”. BigQuery offers built-in analytical functions and supports integration with popular machine learning frameworks such as TensorFlow. With these capabilities, you can perform complex analytics and leverage machine learning models directly on your data within the BigQuery environment.

Let’s Modernize with Teradata to BigQuery Migration

Gain a competitive advantage by responding to business events in real time with event-driven analysis. Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query.