How to load data from Datadog to TiDB

Learn how to use Airbyte to synchronize your Datadog data into TiDB within minutes.

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 Datadog connector in Airbyte

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

Set up TiDB for your extracted Datadog data

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

Configure the Datadog to TiDB 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

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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 to Manually

Step 1: Understand DataDog's Data Export Capabilities

Begin by reviewing DataDog's documentation to understand how you can export the data you need. DataDog allows data export through their API, so you'll need to familiarize yourself with the API endpoints and the specific data formats available for export. Identify the metrics, logs, or traces you wish to transfer to TiDB.

Step 2: Set Up Access to DataDog API

To access DataDog's API, you need an API key and an application key. Log into your DataDog account and navigate to the API section under the Integrations tab. Generate the necessary keys and ensure they have the right permissions to access the data you intend to export.

Step 3: Develop a Script to Extract Data

Write a script in a programming language such as Python, JavaScript, or Ruby, to extract data from DataDog. Use HTTP requests to interact with DataDog's API, paginate through the results if necessary, and handle authentication using the API and application keys. Ensure your script can output data in a structured format like JSON or CSV.

Step 4: Set Up TiDB Environment

Ensure you have a TiDB cluster set up and accessible. Install any necessary client tools that will allow you to interact with TiDB from your local environment. Configure the TiDB server to accept connections from your machine or server where the script will run.

Step 5: Transform Data to Fit TiDB Schema

Design the schema within TiDB that will hold the data coming from DataDog. This may involve creating tables that reflect the structure of your data. Adjust the script to transform the exported data into SQL insert statements, or modify your data format to match TiDB's requirements.

Step 6: Insert Data into TiDB

Extend your script to connect to TiDB and insert the transformed data. Use TiDB's native client libraries or execute SQL commands directly via a command-line tool like `mysql` compatible with TiDB. Ensure that you handle potential errors in data insertion and implement logging for monitoring the process.

Step 7: Test and Automate the Process

Run your script to verify that the data is correctly exported from DataDog and imported into TiDB. Check the data integrity and consistency. Once verified, schedule the script using a cron job or any other scheduling tool to automate the process at regular intervals, ensuring data in TiDB remains updated with minimal manual intervention.