Weather Insights Unleashed: A Daily Data Odyssey with ELT and Airflow, Minio, Airbyte, DBT, Metabase, and Trino

In the era of data-driven decision-making, extracting, transforming, and analyzing weather data can unlock valuable insights. This blog chronicles a comprehensive project that employs Apache Airflow, Minio (S3), Airbyte, DBT, Metabase, and Trino to seamlessly orchestrate the daily journey of weather data for Pondicherry and Melbourne, from extraction to analysis.

High-level architecture diagram:

Phase 1: CSV Extraction and Minio Storage

1.1 Data Transformation to CSV

Building upon the existing project, extend the data transformation process to export the weather information for Pondicherry and Melbourne into CSV format.

1.2 Minio (S3) Storage

Integrate Minio, an open-source object storage solution compatible with Amazon S3, into the workflow. Configure Minio to create a bucket and store the extracted CSV files securely.

Phase 2: Loading to Postgres DB with Airbyte

2.1 Airbyte Integration

Leverage Airbyte, an open-source data integration platform, to seamlessly move data from Minio to Postgres DB. Configure Airbyte connections for Minio as the source and Postgres as the destination.

2.2 Airflow Orchestration

Extend the Airflow DAG to orchestrate the entire process. This includes triggering the CSV extraction, storing it in Minio, and orchestrating the data transfer from Minio to Postgres using Airbyte.

Phase 3: ELT with DBT for Daily Analysis

3.1 DBT Modeling

Use DBT, a popular data modeling tool, to define models that transform the raw weather data into meaningful aggregates. Write SQL transformations to calculate average weather metrics for Pondicherry and Melbourne.

3.2 Automated DBT Runs with Airflow

Integrate DBT into the Airflow workflow. Schedule and execute DBT runs every day at 1 AM after each data load, ensuring that the analysis is always up-to-date.

Phase 4: Visualizing Insights with Metabase

4.1 Metabase Integration

Connect Metabase, an open-source business intelligence tool, to the Postgres DB where the transformed weather data resides. Configure Metabase to visualize the data and create dashboards.

4.2 Airflow-Metabase Integration

Extend the Airflow DAG to automate the refreshing of Metabase dashboards every day after the DBT run, ensuring that stakeholders have access to the latest weather insights.

Phase 5: Seamless Querying with Trino

5.1 Trino Configuration

Configure Trino to act as the query engine, allowing users to seamlessly query the transformed weather data stored in Postgres and explore insights.

5.2 Unifying the Ecosystem

Highlight the synergy achieved by integrating Airflow, Minio, Airbyte, DBT, Metabase, and Trino, creating a cohesive ecosystem for daily weather data management and analysis.

Conclusion: Empowering Daily Data-Driven Decisions

In conclusion, this project exemplifies the power of integrating various tools to create a streamlined pipeline for daily weather data extraction, loading, transformation with DBT, analysis, and visualization. By orchestrating this process with Apache Airflow, each component seamlessly contributes to the daily journey, ultimately empowering users to make informed, data-driven decisions based on the average weather insights for Pondicherry and Melbourne. The ELT process using DBT ensures that data transformations are done efficiently and consistently, adding a robust layer to the data pipeline. The system’s automation ensures that stakeholders wake up every day to the freshest weather insights at 1 AM.