Introduction
Data migration is a critical part of digital transformation and system upgrades, and it involves transferring data from one system to another. This guide focuses on the migration from Teradata, a leading relational database management system, to Google BigQuery, a serverless, highly scalable, and cost-effective multi-cloud data warehouse. It provides a detailed roadmap for a successful data assets migration, including key considerations, challenges, and best practices.
Conducting a comprehensive assessment of the Teradata environment, choosing the right migration approach, optimizing data for BigQuery, using BigQuery's native services, implementing data security and compliance, minimizing downtime and disruption, training the team, and monitoring and optimizing the BigQuery environment after migration are recommended best practices.
Additionally, data and IT teams will need to understand and prepare for differences in how each platform handles things like data types, partitioning, indexing, and cost management to ensure a smooth migration and efficient use of the new system.
Octopai plays an integral role in enabling organizations to overcome technical challenges and maximize the value of their data migration from Teradata to Google BigQuery. Its capabilities are designed to ensure risk mitigation, cost reduction, and efficiency in man hours, thereby contributing to an overall smoother, more cost-effective, and risk-averse data migration process. Octopai's Data Discovery tool, for example, automates the process of scanning Teradata systems to identify existing data assets, their location, format, and business relevance.
The Value Proposition of Cloud Migration
Transitioning from on-premise solutions to cloud-based platforms brings a host of benefits, including cost savings, scalability, increased collaboration, and access to advanced analytics and machine learning capabilities. The move also presents opportunities to adopt new methodologies like DevOps and Agile practices, promoting innovation and reducing time-to-market. However, this transition also has significant implications for Data and IT teams, as they need to manage the shift in technologies, adopt new skill sets, and adjust to different operational practices.
Teradata vs. Google BigQuery - unique aspects
When discussing the migration from Teradata to Google BigQuery, it's essential to understand the unique aspects of both platforms that may affect the migration process. Here are some key characteristics:
Teradata:
- Mature and Robust: Teradata has been around for a long time, and it is known for its robustness and stability. It has a range of features built over time that can handle complex queries and large volumes of data.
- BTEQ Scripting: BTEQ (Basic Teradata Query) is a utility in Teradata that allows users to submit SQL commands in batch mode. It's highly versatile and can be used for importing and exporting data, as well as controlling database workflows.
- Stored Procedures: Teradata supports complex stored procedures. This feature might require significant effort to migrate, as BigQuery’s scripting and stored procedure capabilities differ.
- TPump, FastLoad, and MultiLoad: Teradata has powerful utilities for data loading and extraction, which might have been used heavily in data and IT teams current setup.
Google BigQuery:
- Serverless and Fully Managed: BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse. It takes away the hassle of managing infrastructure, allowing organizations to focus more on data analysis.
- Automatic Scaling: BigQuery automatically scales up and down based on the workload, which means data and IT teams don't need to worry about capacity planning.
- Real-Time Analysis: BigQuery allows for real-time analytics on massive streaming data, which isn't typically a strength of traditional databases.
- Integrated with Google Cloud: BigQuery is well-integrated with other Google Cloud services, making it easy to connect with data storage, machine learning, and data processing tools.
- Data Transfer Service (DTS): BigQuery offers DTS for automating data movement from multiple online and offline sources.
- Security: BigQuery provides robust security measures, including encryption at rest and in transit, identity and access management, and support for VPC Service Controls.
Potential Technical and Business Challenges
In general, migrating from an on-premises solution to a cloud-based one presents a variety of challenges that need to be carefully considered and managed.
Migration from Teradata to Google Big Query can pose both technical and business challenges, including schema migration, stored procedures conversion, standard and complex views adaptation, handling of BTEQ scripts (Batch Teradata Query), performance considerations and complex risk management.
In some cases, it may be determined that certain tables from Teradata are no longer needed, even before the migration, or for example, that specific ETL processes loading data into these tables may be retired.
After the migration, it is crucial to review the entire project, looking for opportunities to reduce maintenance, CPU, storage, people resources, and time.
With the right approach and the use of Octopai platform that supports Cross Systems, Inner Systems, and End-to-End Column Lineage for both Teradata and Google BigQuery, data and IT teams can ensure a smooth and efficient migration, leading to better data management and utilization in the long run.
Here are some of the potential technical and business challenges that could arise:
Technical Challenges
Technical challenges can range from system compatibility issues, data conversion challenges, or issues with connectivity and access rights. It will arise due to differences in data types and SQL dialects, absence of traditional stored procedure support in BigQuery, differences in view handling, managing large files, and distinct performance characteristics of Teradata and BigQuery. Let’s review some of them:
- Schema Migration: Teradata and Google BigQuery have different data types and SQL dialects. This means that some data transformations may be necessary to convert Teradata data types to BigQuery compatible ones. You might need to rewrite SQL queries due to these syntactical differences.
- Microviews and Central Views: In Teradata, views (including Microviews and Central views) are used extensively for data abstraction, simplifying queries, and controlling access. The concept of views exists in BigQuery as well, but the behavior might be different. For instance, BigQuery's views are logical views, not materialized. That means they compute the data when queried, which might affect the performance if not managed properly.
- BTEQ Scripting and Stored Procedures: Teradata's BTEQ scripts and stored procedures might not be directly compatible with BigQuery, requiring rewriting and transformation. For example, you'll need to consider how to transform Teradata BTEQ scripts and stored procedures into BigQuery compatible SQL scripts or Cloud Functions, or how to replace Teradata's utilities with BigQuery's Data Transfer Service.
- Performance and Data Transfer Considerations: The performance characteristics of Teradata and BigQuery can be quite different due to the underlying architecture differences. BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility, while Teradata is a popular relational database management system. Therefore, optimizing for performance might require different strategies in BigQuery as compared to Teradata. Moving large amounts of data from one platform to another can be time-consuming and risk-prone. There is a risk of data loss, corruption, or security breaches during the migration process.
- Utilizing Teradata Utilities: If you are used to Teradata utilities like TPump, FastLoad, and MultiLoad, you will need to figure out how to accomplish these tasks with BigQuery's tools.
Business Challenges
Business challenges can include risk management, cost considerations, training and change management. For instance, there might be costs associated with potential downtime during the migration, training for staff to use the new system, and potential resistance from users who are comfortable with the legacy system.
- Risk Management: Data migration always carries risk. These can include data loss, corruption, or breaches of security. It's important to have robust backup and recovery strategies in place.
- Cost: The cost of migration can be high, including the cost of potential downtime, the manpower costs of performing the migration, and the costs of any necessary new software.
- Training and Change Management: Staff may need to be retrained to use BigQuery, which can require both time and expense. Change management is also a significant concern, as employees need to adapt to the new system.
Octopai is instrumental in addressing data migration challenges, by effectively mitigating risks, cutting costs, and reducing man hours, including wasted time, in transitioning from Teradata to Google BigQuery. Its platform enhances risk management through thorough data mapping, ensuring optimal security while minimizing data loss or corruption. Octopai's automation facilities contribute to substantial savings in both costs and time, streamlining the migration process and making staff training more efficient. As such, Octopai is fundamental in facilitating a more cost-efficient, safer, and time-saving data migration process.
Migration Process Overview and Best Practices
Make sure you are following the migration workflow by setting up and configuring data transfer, choosing the right extraction method, and identifying and customizing schema are key components of the migration workflow.
Here's the step-by-step suggested migration process:
- Download the Migration Agent: The process starts with downloading the migration agent which facilitates the data transfer.
- Configure a Transfer in the BigQuery Data Transfer Service: Set up the data transfer based on your requirements.
- Run the Transfer Job: Copy the table schema and data from your data warehouse to BigQuery.
- Monitor Transfer Jobs: You can optionally monitor the transfer jobs using the Google Cloud Console.
During the migration, the BigQuery Data Transfer Service provides two methods for transferring data: the Teradata Parallel Transporter (TPT) tbuild utility and extraction using a JDBC driver with a FastExport connection. The TPT method is faster but if storage space is a constraint, the JDBC driver with FastExport connection is recommended.
The service also provides automatic schema detection and data type mapping during the data transfer. You can alternatively specify a custom schema file, which can be created manually or generated by the migration agent.
The migration process can be broken down into seven distinct phases: data discovery, dependencies and scoping for effort assessment, data cleansing and deprecation to migrate the right data, prioritization according to critical paths, selective migration, data conversion, documentation to capture knowledge, and post migration for onboarding and monitoring.
Assessment - Data Discovery and Scoping
Understanding the existing data landscape is the first step - identifying existing data, its location, format, and business relevance. This phase forms the foundation for all subsequent steps, establishing a comprehensive inventory of data assets and their dependencies, which is critical for determining what data to migrate and how.
The data discovery and scoping phase is fundamental. For instance, in a large company, you may have hundreds of Teradata databases, each with hundreds or thousands of tables. By using Octopai Discovery and integrated Data Catalog, Data teams can search, scope and document data entities (tables, views), their owners, data volumes, and how often they're accessed. This information helps to build a solid understanding of what resides in your current databases, guiding your migration plan.
Octopai's Data Discovery will automatically scan your Teradata system, identify data entities, their relationships, and how they're used. By gathering insights about your data assets, data and IT teams gain a comprehensive view of what's in your system, guiding data teams to build a solid migration plan.
Data Cleansing and Deprecation
Once the data assets have been scoped, the next step is to cleanse the data in the Teradata system. This involves removing redundant, irrelevant, or erroneous data to avoid migrating 'garbage'. This step is also an opportune moment to deprecate unused or unnecessary reports fed by Teradata.This prevents the migration of 'garbage', and presents an opportunity to deprecate duplicates, redundant or unused assets.
Octopai's Cross-system Lineage and Inner-system Lineage tools play a crucial role in this phase. By visualizing the data lineage, Octopai pinpoints data assets that are no longer being used and can be deprecated, streamlining the migration process and avoiding the migration of unnecessary data.
Data teams will operate with Octopai Teradata lineage and see which tables and ETL processes are no longer in use. For example, you might have ETL processes or tables created for specific projects that have now ended, and these data assets are no longer used or updated. Identifying and removing these from your migration scope will save considerable time and resources.
Prioritization
Selective migration is the best approach for prioritization of the most critical data assets to be migrated first. This can help minimize downtime and disruptions to key business operations. However, it can also introduce the challenge of double maintenance - managing both the legacy and new systems concurrently, which can put additional stress on IT and data teams. With selective migration, you could start by migrating a small subset of data, perhaps one business area or a specific application. This would allow data and IT teams to identify any issues on a smaller scale before moving on to the rest of the data. It's important to note that this will likely require your team to manage both the old and new systems concurrently, which could increase workload in the short term.
For prioritizing and selecting data, the best approach is to migrate the most accessed tables or those critical to business operations first. Suppose you have a database heavily used by the finance department for monthly reporting. In that case, this might be prioritized to ensure continued business operation. The prioritization can be based on the data's business impact, data quality, or compliance needs.
After cleansing, data assets are prioritized for migration based on factors like business value, user needs, or other defined criteria. The final selection of data to be migrated is then made by the migration team.
Octopai's data lineage and catalog capabilities empower the data team to analyze table dependencies, allowing them to collaborate with the business stakeholders and tag critical accounting data assets within Octopai's data catalog for prioritized migration. You can ensure a seamless transition, focusing on migrating the essential data assets first and minimizing any potential disruptions to their financial operations during the migration to BigQuery.
Data Conversion
In this phase, the selected data is converted and transformed to suit the schema, stored procedures, and views of the BigQuery system.
The migration process involves downloading the migration agent, configuring a transfer in the BigQuery Data Transfer Service, running the transfer job, and optionally monitoring transfer jobs using the Google Cloud console.
Data conversion involves converting Teradata-specific SQL code into BigQuery standard SQL.
For example, Teradata's TIMESTAMP might need to be converted to BigQuery's TIMESTAMP, and similar conversions for other data types. You will need to consider stored procedures as well since Teradata procedures might not work directly in BigQuery due to syntactical differences.
During the data conversion phase, Octopai's Live lineage functionality proves invaluable in simulating the effects of transformations, allowing for a comprehensive understanding of potential issues and their impact on data lineage. With Octopai, the data team can simulate the conversion of Teradata-specific SQL code into BigQuery's standard SQL, identifying any syntactical differences or required data type conversions. By testing and verifying the simulated data lineage, the team can confidently address errors, make necessary fixes, and ensure the accuracy and integrity of the data throughout the migration process.
Octopai’s Data lineage tracking ensures that data is accurately translated during migration. It serves as a validation method, comparing the lineage of data before and after migration.
Special Considerations for Stored Procedures Migration
Despite differences in stored procedures between Teradata and Google BigQuery, migration can be achieved by identifying and documenting Teradata stored procedures, learning about BigQuery stored procedures, manually rewriting procedures for BigQuery, testing the new procedures, and iterating on the converted procedures.
When it comes to BTEQ scripts, an important Teradata utility, there's no direct equivalent in BigQuery. This presents a challenge since these scripts often contain business logic. One way to tackle this issue is to convert BTEQ scripts into SQL scripts, then refactor them to comply with BigQuery SQL syntax.
Post Migration Challenges and Optimization
Post-Migration Challenges:
Adopting New Tools and Infrastructure: BigQuery is a part of Google Cloud Platform, and it works well with other Google services. Your team might need to familiarize themselves with a whole new set of tools.
- New Data Management Practices: BigQuery uses a different model of computing resources, and it automatically scales based on the workload. This could change how data and IT teams manage data and resources, including considerations around cost control.
- SQL Syntax Differences: BigQuery uses a slightly different SQL syntax than Teradata. If your team is used to writing SQL for Teradata, they might need to learn some new syntax and conventions.
- Change Management: Adapting to new systems can be difficult. Your team will need to change their workflow and habits, which can lead to resistance.
- Performance Tuning: After migrating, data and IT teams may need to spend time optimizing query performance and cost in BigQuery. This can be a complex task and may require a deep understanding of how BigQuery processes queries.
- Training: Staff may need to be retrained to use BigQuery, which can require both time and expense.
- Continuing to Monitor and Optimize: Once you've migrated to BigQuery, you will need to continually monitor your system to make sure it's working effectively and adjust as needed.
Once the data is successfully migrated, a review and optimization phase is crucial. This phase involves running tests to verify data integrity, analyzing performance, and fine-tuning the system as required. Additionally, feedback from users is collected to further improve and customize the system to better fit their needs. Post-migration, regular monitoring is crucial. For example, data teams might set up a daily check of failed SQL jobs in BigQuery. If data and IT teams see a recurring failure, data and IT teams can then dig deeper to understand the issue - perhaps a data type mismatch, or syntax error, and address the root cause.
There are additional considerations : adopt new practices and terminologies related to cloud technologies. Here are some key points to keep in mind:
- Cloud Terminology: Familiarize yourself with the terminology used in cloud computing and specifically in Google Cloud Platform (GCP) and BigQuery. This includes terms like projects, datasets, tables, buckets, regions, and zones. Understanding these terms will help you navigate and manage your cloud environment effectively.
- Cloud Data Management: Cloud-based data management introduces new practices and tools. It's essential to understand how data is stored, processed, and managed in BigQuery. This includes concepts like data partitioning, clustering, and streaming inserts. Learn about best practices for optimizing data storage, query performance, and cost management in BigQuery.
- Security and Compliance: Cloud environments have their unique security and compliance considerations. Familiarize yourself with the security features and options provided by GCP and BigQuery. Implement proper access controls, encryption, and data governance practices. Ensure compliance with relevant regulations and standards that apply to your data.
- Automation and Orchestration: Cloud platforms offer automation and orchestration capabilities that can streamline data pipelines and processes. Explore tools to automate data transformations, workflows, and data integration tasks. Leverage these tools to optimize your data pipelines in the cloud.
- Scalability and Elasticity: Cloud-based systems provide scalability and elasticity, allowing you to scale your resources up or down based on demand. Understand how to leverage BigQuery's auto-scaling capabilities to handle varying workloads efficiently. Design your data pipelines and infrastructure to take advantage of the cloud's scalability benefits.
- Data Cataloging and Documentation: Ensure that you create a comprehensive data catalog in BigQuery that accurately documents your data assets, including metadata, data lineage, and business descriptions. This will help users find and understand the data in the new system.
- Continuous Learning and Training: Cloud technologies and best practices evolve rapidly. Encourage continuous learning and training for your data and IT teams to stay updated with the latest advancements in BigQuery and cloud computing. Leverage training resources, online documentation, and community forums provided by Google Cloud to enhance your knowledge and skills.
Efficient Teradata to BigQuery Migration with Octopai
Octopai is a valuable solution that empowers businesses during the migration from Teradata to Google BigQuery. It automates data discovery, streamlining the migration process and ensuring a smooth transition. With Octopai's lineage and impact analysis capabilities, businesses can enhance data management, validate data integrity, and improve overall reliability. Octopai also reduces the time and effort required for identifying and migrating relevant data assets. It optimizes resource allocation by identifying and deprecating unused data, while ensuring data security and compliance through robust lineage tracking. By minimizing downtime and disruptions, Octopai facilitates uninterrupted business operations during the migration. Overall, Octopai delivers significant benefits, making the Teradata to BigQuery migration efficient and successful.
Comments
0 comments
Please sign in to leave a comment.