How to load data from Tempo to BigQuery

Learn how to use Airbyte to synchronize your Tempo data into BigQuery within minutes.

Try Airbyte CloudManual method (the hard way)

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 Tempo connector in Airbyte

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

Set up BigQuery for your extracted Tempo 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 Tempo to BigQuery 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.
Try Airbyte CloudManual method (the hard way)

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

Talk to our team

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.

Try Airbyte CloudManual method (the hard way)

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: Export Data from Tempo

Begin by exporting your data from the Tempo database. This typically involves using Tempo's built-in export functionality, which may allow you to export data in formats such as CSV or JSON. Ensure that the export process captures all the necessary data fields and rows you need to move.

Download and install the Google Cloud SDK on your local machine. This toolkit will provide you with the `gcloud` and `bq` command-line tools needed to interact with Google Cloud services, including BigQuery. Follow the installation instructions specific to your operating system, and authenticate the SDK with your Google Cloud account.

Before uploading, ensure your data is formatted correctly for BigQuery. BigQuery supports various file formats like CSV, JSON, Avro, and Parquet. If your Tempo export isn't in one of these formats, you may need to convert it. Ensure that the data structure matches the schema you plan to use in BigQuery.

Use Google Cloud Storage as an intermediary to upload your data files. Create a new bucket in Google Cloud Storage, and use the `gsutil cp` command to upload your files. For example, `gsutil cp /local/path/to/file.csv gs://your-bucket-name/`. This step allows you to leverage Google Cloud's infrastructure for secure and efficient data handling.

Log in to the Google Cloud Console and navigate to BigQuery. Create a new dataset where you plan to store your imported data. This is a necessary organizational step that helps manage your tables and control access.

With your data in Google Cloud Storage, use the `bq` command-line tool to load it into BigQuery. You can do this using a command like:
```
bq load --source_format=CSV your_dataset.your_table gs://your-bucket-name/file.csv
```
Replace `your_dataset`, `your_table`, and `file.csv` with your specific dataset, table names, and file name. Specify the correct source format and schema if necessary.

Once the data is loaded, verify that the import was successful. Use the BigQuery console to run simple queries and check for data integrity and completeness. This step ensures that your data is ready for analysis and that the transfer process did not introduce any errors or omissions.

By following these steps, you can successfully move data from Tempo to BigQuery using Google Cloud's native tools without relying on third-party connectors or integrations.