Summarize
Trusted by data-driven companies
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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“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.”
Rupak Patel
"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."
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.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Tempo is a global software-as-a-service company (SaaS) focused on providing companies with productivity and time management tools to drive more efficient and successful business. Products include resource planning, budget management, and world-class time tracking solutions for Jira (Tempo has claimed ownership to the #1 Jira time tracking app since 2010). Tempo drives business success by providing software that affords insights into teams’ productivity capabilities.
Tempo's API provides access to a wide range of data related to time tracking, resource management, and project management. The following are the categories of data that can be accessed through Tempo's API:
1. Time tracking data: This includes data related to time entries, such as start and end times, duration, and comments.
2. Resource management data: This includes data related to resources, such as employee information, team information, and workload.
3. Project management data: This includes data related to projects, such as project information, project status, and project timelines.
4. Billing and invoicing data: This includes data related to billing and invoicing, such as billing rates, invoices, and payment information.
5. Reporting data: This includes data related to reporting, such as timesheet reports, project reports, and resource reports.
6. Custom fields data: This includes data related to custom fields, such as custom fields for time entries, resources, and projects.
Overall, Tempo's API provides a comprehensive set of data that can be used to manage time, resources, and projects more effectively.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
