How to load data from My Hours to BigQuery

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

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a My Hours 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 My Hours 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 My Hours 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.

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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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.

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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.

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What our users say

Raman Singh

Tech Lead at Symend

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

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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.”

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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: Export Data from My Hours

Begin by exporting the required data from My Hours. Log in to your My Hours account, navigate to the relevant project or timesheet data, and use the export feature to download the data in CSV format. Ensure that the CSV file is saved to a location on your computer that is easily accessible.

Step 2: Prepare CSV Data for BigQuery

Once you have your CSV file, open it using a spreadsheet application like Microsoft Excel or Google Sheets. Clean and format the data as needed, ensuring there are no empty headers, and that the data types are consistent with what you plan to use in BigQuery. Save the clean CSV file.

Step 3: Set Up Google Cloud Project

If you haven't already, set up a Google Cloud Project. Go to the Google Cloud Console, sign in with your Google account, and create a new project. Make sure to enable billing and set up necessary permissions for accessing BigQuery.

Step 4: Create a BigQuery Dataset

In the Google Cloud Console, navigate to BigQuery. Create a new dataset by clicking on the "Create Dataset" option. Specify the dataset ID, data location, and other optional settings as needed. This dataset will serve as the container for your tables.

Step 5: Create Schema for BigQuery Table

Before importing your CSV, define the schema for your BigQuery table. This includes specifying the field names and data types that correspond to the columns in your CSV file. You can do this via the Google Cloud Console by creating a new table and entering schema details manually.

Step 6: Upload CSV to BigQuery

With the dataset and schema in place, you can now upload your CSV file. In BigQuery, navigate to your dataset and select "Create Table." Choose "Upload" as the source, select your CSV file, and configure the table name and schema. Make sure to match the CSV file columns with the schema fields you defined.

Step 7: Verify Data in BigQuery

After the upload completes, verify that the data has been correctly imported into BigQuery. Run some basic queries to check the data integrity and ensure that all fields have been imported as expected. If there are any discrepancies, you may need to adjust your CSV file or schema and repeat the upload process.

By following these steps, you can successfully move data from My Hours to BigQuery without relying on external connectors or integrations.