How to Migrate from Teradata to Synapse (Step-by-Step Guide)

Organizations are shifting from traditional data warehouses to cloud platforms - are you ready to keep up?. Although Teradata-legacy data warehouses offer robust analytical capabilities, they struggle to keep pace with increasing demands. That’s why companies are migrating towards cloud-native platforms like Azure Synapse Analytics, which provides flexibility, performance, and cost savings. Azure Synapse is a unified platform that combines big data and data warehousing capabilities.

 Let’s walk through what Synapse offers and the steps involved in the Teradata to Synapse migration.

Why Migrate from Teradata to Synapse?

Teradata is a legacy and robust Relational Database Management System (RDBMS) used to manage, store, and analyze massive volumes of data. It uses Massive Parallel Processing (MPP) architecture to handle large volumes of data. Over the years, it has become expensive, lacks flexibility, and requires a specialized skill set to maintain the large volumes of data on-premises. This disadvantage is overcome by the cloud-native platform - Azure Synapse. It gives the following advantages over Teradata.

• Performance and scalability: Synapse works with Custer Columnstore Indexes (CCI), which provide faster query results for large datasets. Azure Synapse allows an auto-scaling feature and adjusts resources based on workload. 
  The cloud model offers flexible, pay-as-you-go pricing, allowing you to pay only for the resources you use.‍
• Cost savings: Azure Synapse eliminates Capital expenditures (CAPEX) and works on Operational Expenditures (OPEX).  It offers built-in disaster recovery capabilities, thereby eliminating the need for separate disaster recovery infrastructure.‍
• Faster deployment: Azure Synapse offers rapid server deployment and application development cycles compared to Teradata.‍
• Innovation and future-proof: Synapse fosters innovation and agility compared to traditional on-premises data warehouses. It keeps updating the data platform regularly with new services and features.
  By migrating to Synapse, we are modernizing data platforms to adopt lakehouse architecture and utilizing AI-driven analytics. Synapse enables real-time data analysis by integrating with Power BI and its built-in connectors.‍
• Unified Platform:  It brings many data workloads such as Data Integration (ETL/ELT), SQL, and Spark for big data processing, Machine Learning, and Artificial Intelligence, all together in a single platform.‍
• Enhanced security and compliance: It provides more security features such as data encryption, access controls, and threat detection mechanisms.

Things to Consider Before Migrating from Teradata to Synapse

With compelling advantages as listed above, a Teradata to Synapse migration can be done in a successful way. In order to achieve full cloud-native solutions, the following points should be considered.

• Schema mapping: Examine Teradata’s schema and determine the equivalent in Synapse. Teradata uses primary and secondary indexes, whereas Synapse uses distributed tables and columnstore indexes. Consider factors like data distribution, partitioning, and indexing, and plan for schema transformations.
• SQL Compatibility and Stored Procedures: Teradata SQL differs from T-SQL, which is used in Synapse. Teradata SQL has unique constraints (eg, QUALIFY, specific data/time functions) that need to be rewritten in Synapse. Stored procedures in Teradata (SPL) must be rewritten in Synapse but can be implemented in pipelines.
• Performance optimization:  Optimize specific workloads in Synapse by using distribution keys, indexes, and caching. Test the performance of queries and workloads in Azure Synapse to ensure that they meet business requirements.
• Security and Access: Role-based access in Azure AD is different from Terdata’s internal user and access controls. Migrate user accounts, roles, and permissions to Azure Synapse by ensuring appropriate access controls.
• Cost optimization: Use Azure cost management tools for compute and storage. Pause the Synapse SQL pool when it is not used. 
• Extraction tools: Choosing the right tools for data extraction, loading, and their transformation is important. Tools like Azure Data Factory and Azure Synapse pipelines can make the Synapse migration process smooth and effective. Use Azure Data Factory (ADF) for data movement and orchestration, Azure Data Box for large data transfers, and AzCopy for smaller data.

How to Migrate from Teradata to Synapse 

Having explored the considerations for Teradata to Synapse data migration, we will now see the steps to migrate from Teradata to Synapse Analytics.

1. Assessment, Planning, and Strategy: Begin the Teradata to Synapse conversion by analyzing the amount of data, tables, views, schemas, dependencies, and stored procedures. This will help in determining the scope and complexity of Teradata migration. Define the migration strategy, whether it is a single approach or done in phases. 
2. Set up Azure Synapse environment: Create Synapse workspace with SQL pools and resource groups. Azure Data Lake Storage (Gen2) is used for external data storage.
   Choose distribution methods according to the table's size. Hash-distributed is used in case of large tables, round-robin is used for small or staging tables, and replicated is used for small tables. ‍
3. Schema conversion: Convert Teradata schemas to a synapse-compatible format either manually or through automation. This needs to be done to adjust the datatype difference and unsupported features. 
   Indexes, primary/foreign keys, and constraints are to be built in Synapse. Stored Procedures, functions, Triggers, and macros should be corrected for T-SQL syntax in Synapse. Both Teradata and Synapse differ in their data types. Review and rewrite the exact conversion of data type based on the precision. ‍
4. Data extraction from Teradata: Now we can start extracting data from Teradata through Teradata Parallel Transporter (TPT) or third-party tools. Using TPT, extract the data from Teradata into a file format that is suitable for Azure Synapse (eg, CSV). 
   Depending on the data set size, transfer the extracted files for staging into Azure Data Lake Storage Gen2. For small or medium datasets, consider using AzCopy or Azure Data Factory, and for large datasets, use Azure Data Box.‍
5. Data loading into Synapse: Import the data into Synapse's dedicated SQL pool using Azure Data Factory or Azure Synapse Studio. Map the data structures to Azure Data format and create a data structure in Azure Synapse. 
   Use Polybase or COPY statements in Synapse SQL Pool for highly efficient and parallel data loading. After the initial process, establish processes for continuous data loading to keep Synapse synchronized with Teradata. Confirm the data by checking the row count and comparing the results of both Teradata and Synapse.‍
6. ETL Process conversion: Refactor or migrate the ETL process using Azure Data Factory or Synapse pipelines. Teradata and Synapse use different SQL dialects and features. 
   Teradata-specific constructs like QUALIFY, date/time, TOP N WITH TIES, and GROUP BY need manual intervention to change them to T-SQL. SQL conversion tools like Azure Database Migration Assistant (DMA) and SQL Translation toolkits can be used for complex logic translation. 
   Update all application tools, reporting tools, and connection strings to point to the new Azure Synapse SQL pool. Thoroughly test the converted ETL process to ensure that it reproduces the same results.‍
7. Performance Optimization: Query tuning is important to achieve the required performance. Choose the right distribution and partitioning strategies for large tables. Use Synapse features like result set caching, workload management, and resource allocation. Prioritize critical queries by using Classifiers and Synapse workload groups, and divide resources accordingly.‍
8. Security configuration: Security in the cloud differs from premises. Move Teradata user roles/groups to Azure Synapse Role-based access controls. Ensure that data is encrypted on both sides, masked, and transferred to Azure Synapse. Use Synapse Role-based Access Control for workspace, databases, and storage.‍
9. Test and confirm: The final phase of Teradata migration focuses mainly on fine-tuning, validation, and transition to Synapse. Test and validate individual SQL scripts, stored procedures, and ETL components in the Synapse environment. Involve key business users in the process to validate data and overall system functionality against business requirements.

What to Do After You Migrate from Teradata to Synapse

After a successful migration from Teradata to Synapse, some critical steps and strategies need to be looked at for 

• Data validation and Testing: Validate all data that has been transferred to Synapse. This includes row count, data types of Teradata to Synapse. Test all the functionalities, including stored procedures, functions, and triggers. Examine ETL jobs and identify the areas where they can be optimized.
• Rebuild dashboards: Update the connection strings for all BI tools like Power BI, Tableau to take data from the Synapse database and reconnect your APIs and downstream applications by pointing them to new Azure Synapse endpoints. Dependencies, utilities, and connectors are to be changed to point to the Synapse database. Cross-check the test reports and data extracts to ensure data integrity and performance.
• Monitor and review: Monitor activity logs, query logs, and set up alerts for tracking errors regularly.
• Training: Provide adequate training to the team on the Synapse database features, its tools, SQL dialect differences, resource scaling, and processes. Update technical documentation and collaborate between data engineers, analysts, and other teams using Synapse Studio workspaces.
• Cost optimization: Identify and track the unused resources and review daily, weekly, and monthly the resource consumption and its costs.
• Automate operational tasks: Automate maintenance and workflows using Azure-native tools. Archive and take backups using Azure automation or Logic Apps. Monitor ETL pipelines with built-in retry and failure alerts. 
• Decommissioning Teradata: This is the final step. Check and review the stability, performance, and user experience of Synapse. Plan and carefully decommission the Teradata environment.

Why choose Entrans for your Teradata to Synapse Migration

Moving from Teradata to Synapse is not just migration - it is about faster performance, flexibility, and cost effectiveness. A successful migration lays the foundation for a future-ready platform that supports AI, ML, and real-time analytics. 

Choosing a correct migration team of experts can handle the Teradata to Synapse migration at ease, and Entrans brings fast, accurate, and smooth Teradata migration. Our team of experts is dedicated to ensuring a seamless migration with minimal downtime and maximum efficiency. 

Don’t be held back by outdated technology. Reach out to us to start a seamless Teradata migration.

Frequently Asked Questions (FAQs):

1. How long does it take to migrate from Teradata to Synapse?

Teradata to Synapse migration depends on size, complexity, and available resources. A migration project with smaller data might take around 10 weeks to complete.

2. How is security handled in Synapse?

Synapse’s security is ensured by Azure Active Directory-based Access Controls and data encryption from the starting point to the endpoints. It also offers additional features like masking and auditing.

3. How to load data into Synapse?

Data can be loaded into Azure Synapse Analytics using Polybase or the COPY INTO command from Azure Data Lake. Tools like Azure Data Factory and Synapse pipelines facilitate these methods.

4. Is automation supported in Teradata to Synapse migration?

Yes, automation is supported in Teradata for synapse data migration. Tools like SQL translation scripts and schema conversion utilities streamline data movement, object translation during migration. For complex cases still manual intervention is still needed.

5. How does Synapse handle large data?

Azure Synapse uses a distributed Massive Parallel Processing (MPP) and optimized storage to handle large datasets. It utilizes SQL pools for high-performance data warehousing.