TL;DR

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 Teradata source as a source connector (using Auth, or usually an API key)
2. set up AWS Datalake as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Teradata source

Teradata is a data management and analytics platform that helps businesses to collect, store, and analyze large amounts of data. It provides a range of tools and services that enable organizations to make data-driven decisions and gain insights into their operations. Teradata's platform is designed to handle complex data sets and support advanced analytics, including machine learning and artificial intelligence. It also offers cloud-based solutions that allow businesses to scale their data management and analytics capabilities as needed. Overall, Teradata helps businesses to unlock the value of their data and drive better outcomes across their operations.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

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Prerequisites

1. A Teradata source account to transfer your customer data automatically from.
2. A AWS Datalake account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Teradata source and AWS Datalake, for seamless data migration.

When using Airbyte to move data from Teradata source to AWS Datalake, it extracts data from Teradata source using the source connector, converts it into a format AWS Datalake can ingest using the provided schema, and then loads it into AWS Datalake via the destination connector. This allows businesses to leverage their Teradata source data for advanced analytics and insights within AWS Datalake, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From Teradata to aws datalake

• Method 1: Connecting Teradata to aws datalake using Airbyte.
• Method 2: Connecting Teradata to aws datalake manually.

Method 1: Connecting Teradata to aws datalake using Airbyte

Step 1: Set up Teradata source as a source connector

1. First, navigate to the Airbyte website and create an account.
2. Once you have logged in, click on the ""Sources"" tab on the left-hand side of the screen.
3. Scroll down until you find the Teradata source connector and click on it.
4. You will be prompted to enter your Teradata database credentials, including the host, port, username, and password.
5. After entering your credentials, click on the ""Test"" button to ensure that the connection is successful.
6. If the test is successful, click on the ""Save"" button to save your Teradata source connector settings.
7. You can now use the Teradata source connector to extract data from your Teradata database and load it into your destination of choice.
8. To set up a destination connector, navigate to the ""Destinations"" tab on the left-hand side of the screen and select the destination you want to use.
9. Follow the prompts to enter your destination credentials and configure your destination settings.
10. Once you have set up both your source and destination connectors, you can create a new pipeline to move data from your Teradata database to your destination.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your Teradata source data to AWS Datalake

Once you've successfully connected Teradata source as a data source and AWS Datalake as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Teradata source from the dropdown list of your configured sources.
3. Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Teradata source objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Teradata source to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your Teradata source data.

Method 2: Connecting Teradata to aws datalake manually

Moving data from Teradata to an AWS Data Lake without using third-party connectors or integrations requires several steps, including exporting data from Teradata, uploading it to Amazon S3, and then processing or querying it using AWS Data Lake services like AWS Glue, Amazon Athena, or Amazon Redshift Spectrum. Below is a detailed step-by-step guide:

Step 1: Plan and Prepare Data for Export

1. Identify Data: Determine which tables or datasets you need to transfer from Teradata to your AWS Data Lake.

2. Assess Data Volume: Estimate the size of the data to ensure you have enough storage and to plan for transfer time.

3. Prepare Teradata: Ensure that you have the necessary permissions to export data from Teradata.

Step 2: Export Data from Teradata

1. Choose a Format: Decide on a data format for the export (e.g., CSV, Avro, Parquet).

2. Use Teradata Utilities: Use Teradata's built-in utilities like `BTEQ`, `FastExport`, or `TPT` to export the data.

   - Example using `BTEQ` to export data to CSV:

     ```sql

     .LOGON your_teradata_server/your_username,your_password;

     .EXPORT REPORT FILE = your_export_path/your_data.csv;

     SELECT * FROM your_database.your_table;

     .EXPORT RESET;

     .LOGOFF;

     ```

3. Compress Data: Optionally, compress the exported files to reduce size and transfer time (e.g., gzip).

Step 3: Transfer Data to AWS

1. Set Up AWS CLI: Install and configure the AWS Command Line Interface (AWS CLI) with the necessary permissions.

2. Create S3 Bucket: If not already done, create an S3 bucket in your AWS account where the data will be stored.

   ```bash

   aws s3 mb s3://your-datalake-bucket --region your-region

   ```

3. Upload Data to S3: Use the AWS CLI to upload the exported files to the S3 bucket.

   ```bash

   aws s3 cp your_export_path/your_data.csv s3://your-datalake-bucket/path/to/data/ --recursive

   ```

Step 4: Set Up AWS Data Lake Services

1. AWS Glue: Set up an AWS Glue Data Catalog for your data lake to catalog the data.

   - Define a crawler to scan the S3 bucket and populate the Data Catalog with table definitions.

   - Run the crawler to catalog the data.

2. Amazon Athena or Redshift Spectrum: Set up Athena or Redshift Spectrum to query data directly from S3 using SQL.

   - Define the schema corresponding to your data in S3 if not already defined by AWS Glue.

   - Use Athena or Redshift Spectrum to run queries on your data.

Step 5: Validate and Optimize

1. Validate Data: Run test queries to ensure that the data has been correctly transferred and is accessible.

2. Optimize Storage: Convert data into columnar formats like Parquet or ORC for better performance and cost savings.

3. Partition Data: If you have large datasets, consider partitioning the data in S3 for more efficient queries.

Step 6: Clean Up and Secure Data

1. Remove Local Copies: If you have exported data to a local machine, remove the copies once the transfer is verified.

2. Secure S3 Bucket: Implement proper access control policies on the S3 bucket to secure your data.

3. Monitor Usage: Set up Amazon CloudWatch to monitor access and usage of your Data Lake.

Additional Considerations

- Networking: Ensure that you have a reliable and fast network connection for the data transfer, especially for large datasets.

- Incremental Updates: If you need to synchronize data regularly, plan for incremental updates rather than full transfers.

- Compliance and Data Governance: Make sure that your data transfer complies with data governance and regulatory requirements.

By following these steps, you should be able to move data from Teradata to an AWS Data Lake without third-party connectors or integrations. Keep in mind that while this method avoids third-party tools, it may require more manual effort and maintenance than using dedicated data integration services.

Use Cases to transfer your Teradata source data to AWS Datalake

Integrating data from Teradata source to AWS Datalake provides several benefits. Here are a few use cases:

1. Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Teradata source data, extracting insights that wouldn't be possible within Teradata source alone.
2. Data Consolidation: If you're using multiple other sources along with Teradata source, syncing to AWS Datalake allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Teradata source has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Teradata source data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Teradata source data.
6. Data Science and Machine Learning: By having Teradata source data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Teradata source provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Teradata source table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Teradata source account as an Airbyte data source connector.
2. Configure AWS Datalake as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Teradata source to AWS Datalake after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!