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Logu Lokesh
Logu Lokesh
  • Lab Topology
  • KinAi-Project
  • FinCrime-Project
  • Legacy Projects
  • Back to Logu.au
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Projects Category: Live Project

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Live Project
61

Reviving a Broken Screen Laptop into a Solar-Powered Smart Display with Rainmeter

Reviving a Broken Screen Laptop into a Solar-Powered Smart Display with Rainmeter

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Introduction:

Embark on an innovative journey to transform a broken screen laptop into a solar-powered smart display that seamlessly integrates with Airflow, Kafka, PostgreSQL, and Home Assistant. This DIY project breathes new life into old hardware, adds a touch of eco-friendliness, enhances your home with real-time weather updates and customizable widgets, and introduces smart home automation capabilities.

High-level architecture diagram:

Components for the Solar-Powered Smart Display

  1. Broken Screen Laptop: Salvage your laptop with a non-functional or damaged screen, repurposing it for a new role as a smart display.

  2. External 32-Inch Display: Connect an external 32-inch display to the laptop, serving as the primary visual interface for the smart display.

  3. Solar Battery System: Implement a solar battery system to power the laptop independently, reducing reliance on traditional power sources and embracing eco-friendly practices.

  4. Rainmeter Software: Utilize Rainmeter, a versatile desktop customization tool, to create a visually appealing and customizable display on the external monitor.

  5. OpenWeather API: Integrate the OpenWeather API for real-time weather updates. This API fetches weather data, enhancing the display with up-to-date information.

  6. Airflow, Kafka, and PostgreSQL: Employ Apache Airflow to orchestrate the data flow. Use Kafka as a distributed event streaming platform to transmit weather data, and PostgreSQL as a database to store the information efficiently.

  7. Home Assistant: Integrate Home Assistant for smart home automation. Connect and control various smart devices to enhance the overall functionality of your smart display.

Setting Up the Solar-Powered Smart Display

Hardware Configuration

  1. Connect the external 32-inch display to the laptop, utilizing available ports.
  2. Establish a stable power connection between the laptop and the solar battery system.

Software Integration

  1. Install Rainmeter on the laptop and configure it to display the desired widgets on the external monitor.
  2. Set up Apache Airflow, Kafka, PostgreSQL, and Home Assistant on the laptop.

API Interaction

  1. Obtain an API key from OpenWeather and configure the laptop to fetch weather information using this key.
  2. Create an Airflow DAG (Directed Acyclic Graph) to periodically fetch weather data from OpenWeather and publish it to the Kafka topic.

Data Storage

  1. Configure another Airflow DAG to consume data from the Kafka topic and store it in PostgreSQL for persistent storage.

Smart Home Automation

  1. Integrate Home Assistant with your smart display to control and monitor smart home devices seamlessly.

Display Automation

  1. Develop a Rainmeter skin that dynamically updates based on the weather data stored in PostgreSQL and interacts with Home Assistant for smart home automation.
  2. Configure Rainmeter to display real-time weather information, customizable widgets, and smart home device status on the 32-inch external screen.

Benefits of the Upcycled Solar-Powered Smart Display

  • Cost-Effective Upgrade: Repurposing a broken screen laptop minimizes costs compared to investing in new hardware.

  • Eco-Friendly Solution: Harnessing solar power for the display system aligns with sustainable practices, reducing the environmental impact.

  • Smart Home Features: Enjoy the benefits of a smart display with real-time weather updates, customizable widgets, and smart home automation capabilities.

  • Data Orchestration: Apache Airflow orchestrates the entire data pipeline, ensuring a seamless and efficient flow of weather information and other data.

  • Cloud Integration: By storing weather information in PostgreSQL and utilizing Home Assistant, you ensure data persistence, accessibility, and comprehensive control over your smart home.

  • Learn and Experiment: This project offers an opportunity to learn about DIY electronics, programming, data orchestration, cloud integration, and smart home automation, fostering a sense of accomplishment.

 

Conclusion

Transform your broken screen laptop into a solar-powered smart display, blending creativity, technology, and sustainability. With the integration of Airflow, Kafka, PostgreSQL, and Home Assistant, you not only rejuvenate your old hardware but also create a sophisticated and connected home display system with added smart home automation capabilities. Embrace the DIY spirit and usher in a new era of smart living with this repurposed solar-powered smart display.

 
Live Project
14

Securing Weather Insights: A Vault-Encrypted Journey with Airflow

Securing Weather Insights: A Vault-Encrypted Journey with Airflow

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In the realm of data security, safeguarding sensitive information is paramount. This blog delves into a project that utilizes HashiCorp Vault to encrypt and decrypt transformed weather data. The pipeline, orchestrated by Apache Airflow, exemplifies a robust approach to data security, ensuring that valuable insights remain confidential and protected.

High-level architecture diagram:

Phase 1: Transform and Encrypt with Vault

1.1 Data Transformation with DBT

Begin by leveraging DBT to transform the raw weather data into meaningful insights. The transformed data is then directed to a designated folder, weather_transformed.

1.2 Vault Encryption Layer

Introduce HashiCorp Vault to add an encryption layer to the transformed data. The data is encrypted and stored in a new folder, weather_transformed_vault. Vault ensures that sensitive information remains secure, adhering to best practices in data security.

Phase 2: Airflow Orchestration for Encryption and Decryption

2.1 Encrypting with Airflow DAG: “encrypt_and_store_data_to_postgres”

Create an Airflow DAG named “encrypt_and_store_data_to_postgres” to orchestrate the encryption process. This DAG triggers the execution of the DBT transformation and then directs the transformed data to be encrypted using Vault before storing it securely.

2.2 Decrypting with Airflow DAG: “decrypt_and_store_data_to_postgres”

Implement a corresponding Airflow DAG named “decrypt_and_store_data_to_postgres” for the decryption process. This DAG is responsible for retrieving the encrypted data from weather_transformed_vault, decrypting it through Vault, and subsequently storing it in Postgres.

Vault Encryption Workflow Overview

  1. Data Transformation with DBT: Transform raw weather data into meaningful insights stored in weather_transformed.

  2. Encryption with Vault: Utilize HashiCorp Vault to encrypt the transformed data and store it in weather_transformed_vault.

  3. Airflow DAGs Orchestration:

    • “encrypt_and_store_data_to_postgres”: Initiates the DBT transformation and encrypts the data using Vault before storing it securely.
    • “decrypt_and_store_data_to_postgres”: Retrieves the encrypted data from weather_transformed_vault, decrypts it through Vault, and stores it in Postgres.

Benefits of Vault Encryption in the Data Pipeline

  • Data Security: Vault ensures that sensitive information is encrypted, providing an additional layer of security.

  • Key Management: Centralized key management through Vault simplifies encryption key handling and rotation.

  • Compliance: Adherence to data security best practices ensures compliance with privacy regulations.

Conclusion:  A Secure Data Journey

In conclusion, this project showcases the significance of incorporating encryption into your data pipeline using HashiCorp Vault. By integrating Vault with Airflow DAGs, the weather data undergoes a secure transformation and storage process. This not only safeguards sensitive information but also adheres to the highest standards of data security. As we continue to advance in the era of data-driven insights, ensuring the confidentiality and integrity of our data remains paramount. The orchestration capabilities of Airflow, combined with the security features of Vault, exemplify a robust and dependable approach to handling and protecting sensitive information.

Live Project
20

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

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

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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.

Live Project
41

From Weather Data to Cassandra: A Data Pipeline Journey with Airflow, Kafka, Spark, and Trino

From Weather Data to Cassandra: A Data Pipeline Journey with Airflow, Kafka, Spark, and Trino

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In the era of data-driven insights, automating the extraction, transformation, and loading (ETL) of weather data is essential. This blog takes you through a project that utilizes Apache Airflow, Kafka, Apache Spark Structural Streaming, Cassandra DB, and Trino to seamlessly extract, process, and query weather data for Pondicherry and Melbourne in intervals of every 10 minutes.

High-level architecture diagram:

Phase 1: Data Extraction with Airflow

1.1 OpenWeather API Integration

Start by integrating the OpenWeather API with Airflow to fetch weather data for Pondicherry and Melbourne. The JSON structure of the data is as follows:

json
{
"id": "556c12f62222411b8fb1a9363c39087b",
"city": "Pondicherry",
"current_date": "2023-12-31T11:39:56.709790",
"timezone": "b'Asia/Kolkata' b'IST'",
"timezone_difference_to_gmt0": "19800 s",
"current_time": 1704022200,
"coordinates": "12.0°E 79.875°N",
"elevation": "3.0 m asl",
"current_temperature_2m": 27,
"current_relative_humidity_2m": 68,
"current_apparent_temperature": 28.756290435791016,
"current_is_day": 1,
"current_precipitation": 0,
"current_rain": 0,
"current_showers": 0,
"current_snowfall": 0,
"current_weather_code": 1,
"current_cloud_cover": 42,
"current_pressure_msl": 1011.4000244140625,
"current_surface_pressure": 1011.0546875,
"current_wind_speed_10m": 16.485485076904297,
"current_wind_direction_10m": 58.39254379272461,
"current_wind_gusts_10m": 34.91999816894531
}

1.2 Airflow Scheduling

Set up an Airflow DAG to schedule the data extraction task every 10 minutes. This ensures a regular and timely flow of weather data into the pipeline.

Phase 2: Streaming to Kafka and Spark

2.1 Kafka Integration

Integrate Kafka into the workflow to act as the intermediary for streaming data between Airflow and Spark. Configure topics for Pondicherry and Melbourne, allowing for organized data flow.

2.2 Spark Structural Streaming

Leverage Apache Spark Structural Streaming to process the JSON data from Kafka in real-time. Implement Spark jobs to handle the incoming weather data and perform any necessary transformations.

Phase 3: Loading into Cassandra DB

3.1 Cassandra Schema Design

Design a Cassandra database schema to accommodate the weather data for both Pondicherry and Melbourne. Consider factors such as partitioning and clustering to optimize queries.

3.2 Cassandra Data Loading

Use Spark to load the processed weather data into Cassandra. Implement a robust mechanism to handle updates and inserts efficiently.

Phase 4: Querying with Trino

4.1 Trino Configuration

Set up Trino (formerly PrestoSQL) to act as the query engine for the data stored in Cassandra and Kafka. Configure connectors for both systems to enable seamless querying.

4.2 Query Examples

Provide examples of Trino queries that showcase the power of querying weather data from both Cassandra and Kafka. Highlight the flexibility and speed of Trino for data exploration.

Conclusion: A Seamless Data Journey

In conclusion, this project demonstrates the power of automation and integration in the data processing realm. By orchestrating data extraction with Airflow, streaming with Kafka and Spark, loading into Cassandra, and querying with Trino, we’ve created a robust and scalable pipeline. This not only ensures a continuous flow of weather data but also enables efficient querying and analysis, unlocking valuable insights for various applications.

 
Live Project
31

Unlocking Insights: Visualizing and Monitoring Oracle Cloud VM Hosted Tools with Grafana

Unlocking Insights: Visualizing and Monitoring Oracle Cloud VM Hosted Tools with Grafana

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In the dynamic landscape of cloud computing, monitoring and visualizing the health and performance of your hosted tools are paramount. This blog post takes you through the journey of setting up Grafana dashboards to monitor various tools hosted on an Oracle Cloud VM. Specifically, we’ll be diving into three Grafana dashboards:

High-level architecture diagram:

Dashboard 1: Docker Host

1.1 Setting the Stage

Begin by visualizing and monitoring the Oracle Cloud VM itself. Grafana Dashboard 1 focuses on essential metrics such as CPU usage, memory utilization, disk I/O, and network traffic. This provides a bird’s-eye view of the overall health and performance of your hosting environment.

Download Screenshot 

Dashboard 2: Docker Containers

2.1 Tools Overview

In Dashboard 2, we delve into the Docker containers running on the VM, each representing a critical tool in your infrastructure. These include Apache Airflow, Airbyte, PostgresDB, Cassandra DB, Vault, DBT, Kafka, Spark Structural Streaming, Grafana, Metabase, Nginx, Prometheus, Telegram, Minio, and Trino.

2.2 Metrics and Health Checks

For each container, visualize key metrics such as CPU and memory usage, network statistics, and any custom health checks specific to the tool. A comprehensive overview of your entire toolset allows for quick identification of bottlenecks or issues.

Download Screenshot 

Dashboard 3: Prometheus Exporter

3.1 Domain Monitoring

Dashboard 3 focuses on monitoring various domains associated with your tools. From logu.au to specific tool instances like airbyte.logu.au and kafka.logu.au, track metrics related to performance, errors, and system health.

3.2 Leveraging Prometheus Exporter

Integrate Prometheus exporter metrics into Grafana to ensure a unified monitoring experience. Explore Prometheus metrics for Spark, Nginx, Minio, Trino, and more. This dashboard acts as a centralized hub for all your Prometheus-exported metrics.

Download Screenshot 

Implementation and Configuration Tips

4.1 Data Sources

  • Integrate Grafana with Prometheus for metric collection.
  • Ensure proper configuration of data sources for Docker and Oracle Cloud VM metrics.

4.2 Visualization Best Practices

  • Utilize Grafana’s rich visualization options to create intuitive and informative graphs.
  • Leverage templating for dynamic dashboard elements, facilitating easy navigation.

4.3 Alerts and Notifications

  • Implement Grafana alerts for critical metrics to receive timely notifications.
  • Configure notification channels such as email or messaging platforms like Telegram.

Conclusion

By implementing these Grafana dashboards, you’re not only visualizing the current state of your Oracle Cloud VM and hosted tools but also proactively monitoring for potential issues. This approach ensures that you have actionable insights into the performance, health, and status of your critical infrastructure, empowering you to make informed decisions and maintain a robust and efficient system.

Live Project
21

Automating Weather Forecast Extraction and Proverb Delivery with Airflow, PostgreSQL, Kafka, and Telegram

Automating Weather Forecast Extraction and Proverb Delivery with Airflow, PostgreSQL, Kafka, and Telegram

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In today’s fast-paced world, having timely and accurate information is crucial. Imagine a scenario where you can receive the next 5-hour weather forecast along with a random proverb from Thirukkural, all delivered to your Telegram every few hours. This blog post walks you through a project that achieves just that, leveraging the power of Airflow, PostgreSQL, Kafka, and Telegram.

High-level architecture diagram:

 

Phase 1: Data Extraction

1.1 OpenWeather API Integration

Begin the automation journey by obtaining an API key from OpenWeather. Craft a script to pull the next 5-hour weather forecast and store the data as a JSON file. This serves as the foundation for the entire project.

1.2 Thirukkural Proverbs in PostgreSQL

Populate a PostgreSQL database with random proverbs from Thirukkural. Each proverb should be associated with a unique identifier for efficient retrieval during the automation process.

Phase 2: Airflow Orchestration

2.1 Airflow DAG Configuration

Set up an Airflow DAG to serve as the conductor for the entire automation symphony. Schedule the DAG to execute at 9 AM, 12 PM, 3 PM, and 6 PM, ensuring timely and periodic data extraction.

2.2 Task Execution – OpenWeather and Thirukkural

Within the DAG, configure tasks to execute the OpenWeather API script and fetch a random proverb from the PostgreSQL database. Leverage Airflow’s task dependencies to ensure a sequential and error-resilient execution.

2.3 Kafka Integration

Upon successful extraction, dispatch the obtained JSON data to a Kafka topic named “Forecast.” This establishes a communication bridge between the data extraction phase and subsequent processing.

Phase 3: Telegram Delivery

3.1 Airflow-Kafka-Telegram Integration

Configure Airflow to consume the JSON messages from the “Forecast” Kafka topic. Implement a task that sends the extracted data as a message to a designated Telegram channel. This step ensures a user-friendly and accessible delivery mechanism.

3.2 Benefits and Conclusion

Reflect on the benefits of the automated system, emphasizing the timely and accurate delivery of weather forecasts and Thirukkural proverbs. Discuss how this project seamlessly integrates various technologies, providing a practical example of automation and orchestration in a real-world scenario.

Implementation Insights

4.1 Scalability and Maintenance

  • Discuss the scalability of the system for potential future enhancements.
  • Highlight maintenance considerations, such as versioning for API changes and database updates.

4.2 Error Handling and Logging

  • Detail the error handling mechanisms in place within Airflow.
  • Emphasize the importance of comprehensive logging for debugging and monitoring.

4.3 User Interaction

  • If applicable, discuss potential ways to allow user interaction with the system, such as customizing the time of forecast delivery.

In conclusion, this project exemplifies the power of automation in delivering valuable information seamlessly. By orchestrating the extraction, processing, and delivery phases, the system ensures a reliable and timely stream of weather forecasts and cultural wisdom to end-users through the synergy of Airflow, PostgreSQL, Kafka, and Telegram.

Live Project
24

Simplifying Secure Connections: Setting Up Nginx Reverse Proxy with Certbot for Let’s Encrypt SSL Certificates

Simplifying Secure Connections: Setting Up Nginx Reverse Proxy with Certbot for Let’s Encrypt SSL Certificates

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High-level architecture diagram:

Prerequisites:

  1. Nginx Installed: If Nginx isn’t already installed, you can install it using your package manager. For example, on Ubuntu:
    sudo apt update
    sudo apt install nginx
                        
  2. Domain Pointing to Your Server: Ensure your domain is correctly pointed to your server’s IP address.
  3. Certbot Installed: Install Certbot on your server:
    sudo apt-get update
    sudo apt-get install certbot python3-certbot-nginx
                        

Configuring Nginx Reverse Proxy for Each Service:

For each service, create a separate Nginx configuration file in /etc/nginx/sites-available/:

sudo nano /etc/nginx/sites-available/SERVICE_NAME
            

Replace SERVICE_NAME with the actual service name and BACKEND_ADDRESS with the address of your backend server.

Example for Airbyte (airbyte.logu.au):

server {
    listen 80;
    server_name airbyte.logu.au;

    location / {
        proxy_pass http://BACKEND_ADDRESS;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
    }

    location ~ /.well-known/acme-challenge {
        allow all;
        root /var/www/html;
    }
}
            

Repeat this step for each service, customizing the server_name and proxy_pass accordingly.

Enabling Nginx Configuration:

Create symbolic links to enable the Nginx configurations:

sudo ln -s /etc/nginx/sites-available/SERVICE_NAME /etc/nginx/sites-enabled/
            

Testing and Restarting Nginx:

Ensure there are no syntax errors:

sudo nginx -t

If no errors are reported, restart Nginx:

sudo service nginx restart

Obtaining Let’s Encrypt SSL Certificates:

Run Certbot for each service:

sudo certbot --nginx -d SERVICE_NAME

Follow the prompts to configure SSL and automatically update Nginx configurations.

Testing Renewal and Updating DNS Records:

Test the renewal process for each service:

sudo certbot renew --dry-run

Ensure DNS records for each service point to your server’s IP address.

Verifying HTTPS Access:

Access each service via HTTPS (e.g., https://airbyte.logu.au). Ensure the SSL padlock icon appears in the browser.

Repeat these steps for each service, replacing SERVICE_NAME with the respective service’s domain.

By following these steps, you’ll have a secure setup with Nginx acting as a reverse proxy and Let’s Encrypt providing SSL certificates for each service. Adjust configurations as needed based on specific service requirements. Stay secure and enjoy your enhanced web services!

Live Project
53

Orchestrating Real-Time Insights: Weather Data Extraction with The Modern Data Stack

Orchestrating Real-Time Insights: Weather Data Extraction with The Modern Data Stack

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Introduction: 

In today’s data-driven world, the ability to harness real-time data is crucial for making informed decisions. In this home lab workshop, we aim to extract real-time weather data from Pondicherry and Melbourne every 10 minutes. To accomplish this, we’ll leverage a modern data stack comprising various cutting-edge tools and technologies. This hands-on project will provide practical insights into modern data architecture and its components.

High-level architecture diagram:

Tools in Our Modern Data Stack:

  1. Apache Airflow:

    • Purpose: Workflow automation and scheduling.
    • Role: Orchestrating the entire data pipeline, ensuring seamless execution of tasks.
  2. Airbyte:

    • Purpose: Data integration and replication.
    • Role: Facilitating the extraction of weather data from diverse sources and ensuring its uniformity.
  3. PostgresDB:

    • Purpose: Relational database management.
    • Role: Storing structured weather data for easy retrieval and analysis.
  4. Cassandra DB:

    • Purpose: NoSQL database management.
    • Role: Handling large volumes of data with high write and read throughput.
  5. Vault:

    • Purpose: Secret management and data protection.
    • Role: Safeguarding sensitive information such as API keys and credentials.
  6. DBT (Data Build Tool):

    • Purpose: Transforming and modeling data.
    • Role: Enabling analysts to work with structured, clean data for insights.
  7. Kafka:

    • Purpose: Distributed event streaming platform.
    • Role: Facilitating real-time data streaming between different components of the stack.
  8. Spark Structural Streaming:

    • Purpose: Real-time data processing.
    • Role: Performing complex computations on streaming data.
  9. Grafana:

    • Purpose: Data visualization and monitoring.
    • Role: Creating dashboards to visualize weather trends and system performance.
  10. Metabase:

    • Purpose: Business intelligence and analytics.
    • Role: Empowering users to explore and analyze data through a user-friendly interface.
  11. Nginx:

    • Purpose: Web server and reverse proxy server.
    • Role: Securing and optimizing data transmission between components.
  12. Prometheus:

    • Purpose: Monitoring and alerting toolkit.
    • Role: Keeping track of system metrics and ensuring reliability.
  13. Telegram:

    • Purpose: Communication and alerting.
    • Role: Sending notifications and alerts based on predefined conditions.
  14. Minio:

    • Purpose: Object storage.
    • Role: Storing unstructured data such as raw weather data.
  15. Trino:

    • Purpose: Distributed SQL query engine.
    • Role: Enabling users to query and analyze data stored in different databases seamlessly.

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Tags

#Alerting #Analytics #BusinessIntelligence #CloudStorage #Communication #Database #DataIntegration #DataOrchestration #DataTransformation #DataVisualization #DistributedDatabase #ETL #Messaging #Monitoring #NoSQL #ObjectStorage #QueryEngine #RealTimeProcessing #ReverseProxy #SecretManagement #Security #Spark #SQL #Visualization #WebServer #WorkflowAutomation

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