How to load data from IBM Db2 to Teradata

Learn how to use Airbyte to synchronize your IBM Db2 data into Teradata within minutes.

Trusted by data-driven companies

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams  to meet all their growing custom business demands in the new AI era.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a IBM Db2 connector in Airbyte

Connect to IBM Db2 or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Teradata for your extracted IBM Db2 data

Select Teradata where you want to import data from your IBM Db2 source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the IBM Db2 to Teradata in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

Guided Tour: Assisting you in building connections

Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Andre Exner
Director of Customer Hub and Common Analytics

"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."

Learn more
Chase Zieman headshot
Chase Zieman
Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Learn more
Rupak Patel
Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

Learn more

How to Sync IBM Db2 to Teradata Manually

Before starting the data migration process, ensure that your IBM Db2 environment is properly configured and accessible. Verify that you have the necessary permissions to extract data and that the database is in a consistent state. Identify the tables and data that need to be migrated by generating a list of schemas, tables, and relevant columns.

Utilize the Db2 EXPORT utility to extract data from the selected tables into a flat file format such as CSV. This can be done using the following Db2 command:
```sql
EXPORT TO 'data.csv' OF DEL MODIFIED BY NOCHARDEL SELECT FROM your_table;
```
Make sure to handle data types and ensure that special characters and delimiters are correctly managed to avoid issues during import.

Once the data is exported into flat files, transfer these files to the Teradata environment. This can be done using secure file transfer protocols such as SCP, SFTP, or using physical media if necessary. Ensure that the target location has sufficient storage and appropriate permissions for file access.

Set up the Teradata environment to receive the data. Create the necessary database schema, tables, and define the appropriate data types that correspond to the source schema in Db2. This can be done using Teradata SQL commands to create tables that match the structure of those in Db2.

Use the Teradata BTEQ (Basic Teradata Query) utility to load data from the flat files into the Teradata tables. The BTEQ script should include commands to read data from the files and insert them into the corresponding Teradata tables, like so:
```sql
.LOGON your_teradata_server/username,password;
.IMPORT DATA FILE = 'data.csv';
REPEAT;
USING (column1 VARCHAR(100), column2 INT, ...)
INSERT INTO your_teradata_table (column1, column2, ...)
VALUES (:column1, :column2, ...);
.LOGOFF;
```

After loading the data, perform data validation to ensure that the migration was successful. Run SQL queries to count records, check for duplicates, and compare sample data between Db2 and Teradata. Consider using checksums or hash values for data comparison to verify accuracy.

Once data integrity is confirmed, optimize the newly imported data in Teradata by collecting statistics and updating indexes. This can improve query performance and ensure efficient data retrieval. Execute commands such as:
```sql
COLLECT STATISTICS ON your_teradata_table;
```
Review the database setup and perform any necessary maintenance tasks to finalize the migration process.

How to Sync IBM Db2 to Teradata Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

Specializing in the development and maintenance of Android, iOS, and Web applications, DB2’s AI technology offers fast insights, flexible data management, and secure data movement to businesses globally through its IBM Cloud Pak for Data platform. Companies rely on DB2’s AI-powered insights and secure platform and save money with its multimodal capability, which eliminates the need for unnecessary replication and migration of data. Additionally, DB2 is convenient and will run on any cloud vendor.

IBM Db2 provides access to a wide range of data types, including:  

1. Relational data: This includes tables, views, and indexes that are organized in a relational database management system (RDBMS).  

2. Non-relational data: This includes data that is not organized in a traditional RDBMS, such as NoSQL databases, JSON documents, and XML files.  

3. Time-series data: This includes data that is collected over time and is typically used for analysis and forecasting, such as sensor data, financial data, and weather data.  

4. Geospatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.  

5. Graph data: This includes data that is organized in a graph structure, such as social networks, recommendation engines, and knowledge graphs.  

6. Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets, feature vectors, and model parameters.  

Overall, IBM Db2's API provides access to a diverse range of data types, making it a powerful tool for data management and analysis.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up IBM Db2 to Teradata as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from IBM Db2 to Teradata and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter