Extract recent data from a social media platform (e.g., recent tweets or posts), load into a data warehouse with Airbyte, and perform quick sentiment analysis.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Recreation and Booking Insights with Airbyte

Extract data on camping, tours, or ticket availability from the Recreation.gov API with Airbyte. Load it into a warehouse to analyze popular recreation spots, booking trends, or seasonal demands.

You can use a tool like dbt for data transformation, and an orchestrator like Airflow or Dagster if needed.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Use Airbyte to efficiently extract data from a specific API (e.g., Twitter, GitHub) and load it directly into a data warehouse for quick analytics.

Integrating Postgres Data into Snowflake with Airbyte.

Leverage Airbyte to integrate data from Postgres databases into Snowflake efficiently, enabling scalable analytics and insights on the unified data warehouse platform.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Demographic Analysis with Airbyte

Use Airbyte to extract demographic, economic, or housing data from the US Census API. Load it into a warehouse for insightful analysis on population trends, economic indicators, or housing developments.

You can use a tool like dbt for data transformation, and an orchestrator like Airflow or Dagster if needed.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Optimizing Ad Performance with Facebook Ads and Google Ads Data.

Integrate and analyze Facebook Ads and Google Ads data using Airbyte and dbt to optimize ad spend and improve campaign ROI.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Analyzing User Retention with Cohort Data.

Extract and transform cohort data from user analytics platforms using Airbyte and dbt to gain insights into user retention and churn.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Extract log data (e.g., web server logs) using Airbyte, load them into a data warehouse, and quickly analyze patterns or anomalies.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Extract recent weather data from open APIs (Weatherstack), load it into a data warehouse using Airbyte, and analyze weather patterns or predictions.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Aggregating Data from MySQL and Postgres into BigQuery with Airbyte.

Use Airbyte to aggregate and streamline data from MySQL and Postgres databases into Google BigQuery, facilitating advanced analytics and reporting on consolidated data.

Data Replication from Postgres to Postgres with Airbyte.

Set up a seamless data replication pipeline using Airbyte to synchronize data between two Postgres databases, ensuring data consistency and availability across systems.

Extract data from a popular source with Airbyte, load it into a data warehouse, and perform some essential transformations using dbt.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Deriving Insights from GitHub Repositories.

Extract repository, commit, and pull request data from GitHub using Airbyte, transform it using dbt, and analyze to gain insights into code quality, collaboration patterns, and project health.

Migrating Data from MongoDB to Postgres with Airbyte.

Utilize Airbyte for migrating data from a MongoDB NoSQL database to a Postgres SQL database, transforming document-based structures to relational formats.

Integrating Google Analytics and Google Search Console.

Extract web traffic and search query data from Google Analytics and Google Search Console, transform it using dbt for insights into user behavior and search performance, and store in a data warehouse like BigQuery.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Advanced Data Operations with Airbyte, dbt, Prefect and Databricks.

Create advanced data operations by extracting data with Airbyte, transforming it with dbt, and orchestrating the end-to-end workflow using Prefect, optimizing for scalability and maintainability.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

End-to-end Data Stack with Airbyte, dbt, Snowflake and Looker.

Leverage the powerful integration of Airbyte, dbt, Snowflake, and Looker to build a comprehensive analytics stack.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Efficient Data Migration from Postgres to MySQL with Airbyte.

Utilize Airbyte to facilitate a smooth and efficient migration of data from a Postgres database to a MySQL database.

Advanced Data Operations with Airbyte, dbt, and Prefect.

Create advanced data operations by extracting data with Airbyte, transforming it with dbt, and orchestrating the end-to-end workflow using Prefect, optimizing for scalability and maintainability.

In the dbt prefect bigquery workflow, the first command in the list (pwd) fails because it's not a dbt command. Every command in the DbtCoreOperation is presumed to be a valid dbt command and --profiles-dir and --project-dir are appended during command run. when you append the commands to the bash command pwd (e.g pwd --profiles-dir /some/dir --project-dir /some/dir) it fails because it treats the appends as flags.

Weirdly, this does not trigger the task to fail. It only shows on the logs as an info. See screenshot below

Optimizing System Performance with Datadog Insights.

Extract performance metrics and logs from Datadog using Airbyte, transform and analyze the data using dbt to optimize system performance and resource utilization.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Evaluating Sales Performance with CRM Data.

Integrate CRM platforms using Airbyte and transform the extracted data with dbt to derive insights into sales performance and strategies.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Continuous Data Synchronization with Airbyte, dbt, and Apache Airflow.

Utilize Airbyte and dbt for data extraction and transformation, and implement Apache Airflow to orchestrate continuous data synchronization workflows for always-up-to-date data.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Optimizing Error Resolution Processes with Sentry Insights.

Extract and analyze error data from Sentry using Airbyte and dbt to optimize error resolution processes, reduce downtime, and improve user experience.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Building Customer Segments with E-commerce Data.

Extract customer interaction and transaction data from e-commerce platforms using Airbyte and dbt to create detailed customer segments for personalized marketing.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Advanced Data Operations with Airbyte, dbt, and Prefect.

Create advanced data operations by extracting data with Airbyte, transforming it with dbt, and orchestrating the end-to-end workflow using Prefect, optimizing for scalability and maintainability.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Optimizing Engineering Performance with GitHub, Datadog, and Sentry.

Integrate and analyze data from GitHub, Datadog, and Sentry using Airbyte and dbt to assess and optimize overall engineering performance, from code quality and collaboration to system health and error resolution.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Incremental Data Synchronization from MySQL to Postgres with Airbyte.

Configure Airbyte to perform incremental data synchronization from MySQL to Postgres, optimizing for data freshness and reducing the load on source and destination systems.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Predicting Revenue Trends with Historical Data.

Extract historical sales data using Airbyte, transform it using dbt, and employ predictive modeling to forecast revenue trends.

Analyzing Developer Productivity with GitHub Data.

Leverage Airbyte to integrate GitHub data, apply transformations using dbt, and assess developer productivity, code review efficiency, and collaboration trends.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Analyzing Support Ticket Volume with Zendesk Data.

Leverage Airbyte to extract ticket data from Zendesk, transform the data using dbt, and analyze ticket volumes, patterns, and trends to optimize support operations and resource allocation.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Reducing Employee Turnover with HR Data Insights.

Extract HR data using Airbyte, transform it using dbt, and analyze it to develop strategies to enhance employee retention.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Analyzing Cryptocurrency with Airbyte

Extract live price, volume, or historical data from cryptocurrency exchanges like CoinGecko. Use Airbyte to load the data and perform in-depth market analysis, arbitrage opportunities, or trend predictions.

You can use a tool like dbt for data transformation, and an orchestrator like Airflow or Dagster if needed.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Monitoring Infrastructure Health with Datadog Data.

Integrate infrastructure monitoring data from Datadog using Airbyte, transform the data with dbt, and create dashboards to monitor system health and performance.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Extract a snapshot of a specific table or dataset from a database using Airbyte and load it into a data warehouse for immediate analysis.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Achieving Low-Latency Data Availability with Logical Replication from Postgres using Airbyte.

Configure Airbyte with logical replication to facilitate low-latency data availability from a Postgres database, ensuring near real-time data access and enhancing responsiveness for analytical queries and applications.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Social Media Monitoring with Airbyte

Extract recent tweets, hashtags, or user data from the Twitter API using Airbyte. Load the data into a warehouse and analyze trends, sentiments, or influencer activities.

You can use a tool like dbt for data transformation, and an orchestrator like Airflow or Dagster if needed.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Optimizing Customer Satisfaction with Zendesk Data.

Extract support ticket data from Zendesk using Airbyte, transform it using dbt, and analyze customer satisfaction scores and feedback to identify areas for improvement and enhance customer experience.

Stock Market Analysis with Airbyte

Extract real-time or historical stock data, news, or financials from the Polygon Stock API using Airbyte. Load the data into a warehouse to perform trend analysis, predictions, or portfolio evaluations.

You can use a tool like dbt for data transformation, and an orchestrator like Airflow or Dagster if needed.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Pokémon Analysis and Insights with Airbyte

Extract Pokémon, ability, or move data from the PokeAPI using Airbyte. Load the data into a warehouse for in-depth analysis on Pokémon attributes, popularity, or battle strategies.

You can use a tool like dbt for data transformation, and an orchestrator like Airflow or Dagster if needed.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Analyzing Shopping Cart Abandonment with E-commerce Data.

Extract shopping cart data from e-commerce platforms using Airbyte and dbt, analyze user behavior, and devise strategies to reduce cart abandonment rates.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Enhancing Customer Experience with Personalized Product Recommendations.

Extract user interaction and purchase data using Airbyte and dbt to generate personalized product recommendations and improve customer experience.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Optimizing Recruitment Processes with Applicant Data.

Extract data from Applicant Tracking Systems using Airbyte, transform it with dbt, and analyze to optimize recruitment processes.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Advanced Data Operations with Airbyte, dbt, and Dagster.

Create advanced data operations by extracting data with Airbyte, transforming it with dbt, and orchestrating the end-to-end workflow using Prefect, optimizing for scalability and maintainability.

Tracking Product Feature Usage with Event Data.

Employ Airbyte to extract event data from product analytics tools, transform the data using dbt, and analyze feature usage to enhance product development.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Continuous Data Synchronization with Airbyte, dbt, and Apache Airflow.

Utilize Airbyte and dbt for data extraction and transformation, and implement Apache Airflow to orchestrate continuous data synchronization workflows for always-up-to-date data.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Analyzing Application Errors with Sentry Data.

Integrate error tracking data from Sentry with Airbyte, transform it using dbt, and analyze to identify common issues, affected users, and prioritize fixes.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

Ensuring Data Quality with Airbyte, dbt, and Great Expectations.

Extract data using Airbyte, transform it using dbt, and utilize Great Expectations to validate data quality, ensuring the reliability and accuracy of your analytical data.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery