Summarize this article with:
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 Toggl. Log into your Toggl account, navigate to the Reports section, and select the data range and specific projects or clients you wish to export. Use the export function to download the data in a CSV or Excel format. Ensure the file is saved locally on your computer.
Set up your local environment by installing the AWS CLI if you haven't already. This tool will allow you to interact with AWS services from your command line. Download and install the AWS CLI from the official AWS website, then configure it by running `aws configure` and providing your AWS Access Key ID, Secret Access Key, region, and default output format.
Log in to your AWS Management Console and navigate to the S3 service. Click on "Create bucket" and follow the prompts to create a new bucket that will temporarily store your Toggl data. Ensure that the bucket name is unique and choose the appropriate AWS region. Set permissions and access settings according to your security requirements.
Use the AWS CLI to upload your exported Toggl data file to the S3 bucket you created. Open your terminal and run the command `aws s3 cp /path/to/your/toggl_data.csv s3://your-bucket-name/`. Replace `/path/to/your/toggl_data.csv` with the actual path to your Toggl data file and `your-bucket-name` with your S3 bucket's name.
Navigate to the AWS Glue service in your AWS Management Console. Create a new database within AWS Glue to serve as your data catalog. Then, create a new crawler and configure it to point to the S3 bucket where your Toggl data file is stored. Run the crawler to populate the data catalog with the schema of your Toggl data.
With your data catalog established, create an ETL (Extract, Transform, Load) job in AWS Glue. This job will allow you to transform your Toggl data as needed. Define the source as your S3 data catalog and specify any transformations required (e.g., data type conversions or renaming columns). Define the target as your data lake storage in Amazon S3.
Execute the AWS Glue ETL job to load your transformed Toggl data into your AWS Data Lake. Monitor the job's progress and verify the data has been loaded correctly by navigating to your designated data lake storage area in S3. Ensure that the data meets your format and schema requirements for use in your data lake analytics.
Following these steps will allow you to move data from Toggl to an AWS Data Lake environment, leveraging AWS services and your local setup without the use of 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.
Toggl is a favorite app which lets you track how much time you spend on activities. Toggl generally builds work tools to uphold your productivity and eliminate stress. Toggl Track is entirely designed for effortless time tracking. It is a simple but mighty time tracker that exhibits you how much your time is valuable. Time tracking that is easy, powerful, and frictionless. The app that helps you make the most of your time. Start and stop tracking your time with a single tap.
Toggl's API provides access to a wide range of data related to time tracking and project management. The following are the categories of data that can be accessed through Toggl's API:
1. Time entries: This includes data related to the time spent on tasks, projects, and clients.
2. Projects: This includes data related to the projects being worked on, such as project name, description, and status.
3. Clients: This includes data related to the clients associated with the projects, such as client name, contact information, and billing details.
4. Users: This includes data related to the users who are using Toggl, such as user name, email address, and role.
5. Tags: This includes data related to the tags associated with time entries, projects, and clients.
6. Workspaces: This includes data related to the workspaces in which the projects and time entries are being managed.
7. Reports: This includes data related to the reports generated by Toggl, such as time summary reports, detailed reports, and project reports.
Overall, Toggl's API provides a comprehensive set of data that can be used to track time, manage projects, and generate reports.
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:
