How to load data from Timely to BigQuery

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

Try Airbyte Cloud Manual 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 Timely 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 Timely 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 Timely 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 Cloud Manual 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.

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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.

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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 Cloud Manual 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.

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Tech Lead at Symend

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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."

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How to Sync to Manually

Step 1: Extract Data from Timely

Begin by accessing your Timely account. Navigate to the data or reports section where your desired data is stored. Use Timely’s export feature to download the data in a CSV format. Ensure the data is organized and complete before proceeding.

Step 2: Prepare Your Local System for Data Handling

Ensure your local system has the necessary tools for handling CSV files. You’ll need a text editor or spreadsheet software like Excel or Google Sheets to review and clean your data. Additionally, install the Google Cloud SDK if it’s not already on your system, as it will be necessary for uploading data to BigQuery.

Step 3: Clean and Format the CSV Data

Open your downloaded CSV file and review the data for consistency and accuracy. Clean up any discrepancies, such as missing values or incorrect formats. Ensure that the CSV file adheres to a schema that will be compatible with BigQuery. Save any changes you make.

Step 4: Create a Google Cloud Project

Log in to the Google Cloud Console and create a new project if you don’t have one already. This project will be where your BigQuery dataset resides. Name your project and take note of the project ID, as you’ll need it for later steps.

Step 5: Set Up a BigQuery Dataset and Table

In the Google Cloud Console, navigate to BigQuery. Here, create a new dataset within your project to house the data. Once the dataset is created, set up a new table. Define the table schema to match the columns in your CSV file, specifying data types for each field appropriately.

Step 6: Upload the CSV Data to Google Cloud Storage

Before importing to BigQuery, upload your CSV file to Google Cloud Storage (GCS). In the Google Cloud Console, navigate to Storage and create a new bucket. Upload your CSV file to this bucket. Ensure the file is publicly accessible or that you have permissions set for BigQuery to access it.

Step 7: Load Data from Google Cloud Storage to BigQuery

Use the BigQuery console or the command line to load your data from GCS into BigQuery. In the BigQuery console, select your dataset and then the table where you want the data to reside. Use the “Create Table” option and choose “Google Cloud Storage” as the source. Input the path to your CSV file in GCS, configure the import options, and execute the load operation. Monitor the process to ensure successful data transfer.

This guide provides a straightforward method to move your data from Timely to BigQuery without relying on third-party tools, offering full control over the data transfer process.