Swati Tyagi

• Matillion End-to-End Project: Amazon S3 to Snowflake Integration with Power BI Reporting
  
  Project Workflow:
  1. Data Extraction: Matillion is configured to periodically fetch data files from the specified S3 bucket. This could be scheduled at regular intervals to ensure the availability of up-to-date data.
  2. Data Transformation: Matillion performs data transformations as per the defined business rules and requirements. This may include tasks like data cleansing, aggregation, and enrichment to prepare the data for analysis.
  3. Loading into Snowflake: Matillion orchestrates the loading of transformed data into Snowflake tables. It manages the data loading process efficiently, ensuring data integrity and consistency.
  4.Power BI Integration: Power BI connects to Snowflake as a data source using appropriate credentials. Users can then design and create reports and visualizations based on the data stored in Snowflake.
  5.Report Generation and Analysis: With Power BI, users can generate interactive reports and dashboards to analyze various aspects of the data. This includes sales trends, customer behavior, and inventory insights, among others.
  Benefits:
  Centralized Data Repository: Snowflake serves as a centralized repository for all the organization's data, enabling easy access and analysis.
  Scalability: Both Matillion and Snowflake are highly scalable, allowing organizations to handle growing volumes of data seamlessly.
  Actionable Insights: Power BI empowers users to derive actionable insights from the data through intuitive visualizations and interactive reports.
  
  GITHUB LINK:- https://lnkd.in/gkMkTnSv

Continuous Data Ingestion fromAmazon S3 to Snowflake using Snowpipes and Stages
1. Setting up Snowflake Stage
Create a stage in Snowflake that points to the specific S3 bucket or folder where your data resides.
This stage acts as a virtual location where Snowflake can read data from or write data to.
2. Configuring Snowpipe
Create a Snowpipe, which is a continuous data ingestion service provided by Snowflake.
Configure the Snowpipe to monitor the specified stage for new data arrivals.
Define the file format and any required options for ingesting data (e.g., CSV, JSON, Parquet).
Specify the target table in Snowflake where the data will be loaded.
3. Defining Notification Integration
Integrate Snowflake with an external notification service such as AWS SNS (Simple Notification Service) or AWS EventBridge.
Configure the notification service to trigger Snowpipe execution whenever new files are added to the S3 bucket.
4. Data Ingestion Process
As new data files are added to the designated S3 bucket or folder, the notification service triggers the Snowpipe.
Snowpipe automatically detects the new files in the Snowflake stage and initiates the data ingestion process.
Data from the files is seamlessly loaded into the specified target table in Snowflake, following the defined file format and options.
5. Monitoring and Error Handling
Monitor the Snowpipe execution logs and status to ensure smooth data ingestion.
Implement error handling mechanisms to address any issues that may arise during the data loading process.
Utilize Snowflake's built-in features for data validation and integrity checks to maintain data quality.
Benefits of Using Snowpipes and Stages
Real-time Data Ingestion: Snowpipes enable real-time ingestion of data from S3 to Snowflake, ensuring timely availability of fresh data for analysis.
Automated Process: By automating the data loading process, Snowpipes eliminate the need for manual intervention, reducing the risk of errors and improving efficiency.
Scalability: Snowpipes can handle large volumes of data with ease, making them suitable for enterprise-scale data pipelines.
Integration Flexibility: Snowpipes seamlessly integrate with various data sources and formats, providing flexibility in data ingestion workflows.
By leveraging Snowpipes and stages in Snowflake, organizations can establish robust and automated data pipelines for efficiently loading data from S3 into Snowflake, facilitating agile and data-driven decision-making processes.

GITHUB LINK:- https://lnkd.in/gPX9avsh

Dashboard Creation for Employee Attrition Dataset

This project involved creating an insightful dashboard using data from an employee attrition dataset. Key components included:

• Data exploration and cleaning to ensure accuracy.
• Visualizations depicting attrition trends by demographics, departments, and job roles.
• Metrics such as attrition rates, reasons for leaving, and tenure analysis.
• User-friendly interface for HR and management to derive actionable insights for retention strategies.
• The dashboard facilitated data-driven decision-making to enhance employee retention and organizational stability.
• GITHUB LINK :- https://lnkd.in/dhCU4A2Chttps://lnkd.in/dhCU4A2C

Power BI Dashboard for Amazon.com Sales, Profit, and Loss Analysis

This project involved creating a dynamic dashboard using Power BI to analyze the sales, profit, and loss metrics of Amazon.com. Key components included:

• Data Integration: Extracting and integrating sales, profit, and loss data from Amazon.com's financial records.
• Visualization: Designing interactive charts and graphs to visualize trends in sales revenue, profit margins, and operating expenses.
• Key Metrics: Highlighting key performance indicators such as gross profit margin, net income, and operating expenses ratio.
• Geographical Analysis: Mapping sales and profit data geographically to identify regional performance.
• Time-Series Analysis: Analyzing trends over time to understand seasonal variations and year-over-year growth.
• Dashboard Deployment: Deploying the dashboard on Power BI for stakeholders to access and explore insights easily.

This project aimed to provide actionable insights to stakeholders, enabling informed decision-making and strategic planning to optimize profitability and operational efficiency at Amazon.com

Link:- https://lnkd.in/gyJRTb6x

Chatbot Project Using Rasa, Deployed with Ngrok and Twilio

In this project, I developed and deployed a chatbot using the Rasa framework, integrated with Ngrok for tunneling and Twilio for messaging capabilities. The chatbot was designed to provide a conversational interface for users to interact with various services and obtain information efficiently via SMS or other messaging platforms.

Key responsibilities included:

• Development with Rasa: Designing and training the chatbot's dialogue management system using Rasa's NLU (Natural Language Understanding) and Core components.
• Integration with Ngrok: Setting up a secure tunnel to expose the locally hosted Rasa server to the internet, enabling seamless communication between the chatbot and external messaging platforms.
• Twilio Integration: Configuring Twilio's API to send and receive SMS messages, enabling users to interact with the chatbot via their mobile phones.
• Feature Implementation: Implementing features such as intent recognition, entity extraction, and context handling to ensure accurate and meaningful interactions with users.
• Testing and Optimization: Conducting extensive testing to refine the chatbot's responses, improve its accuracy, and enhance the overall user experience.
• Deployment and Maintenance: Deploying the chatbot to production, monitoring its performance, and providing ongoing maintenance and updates as needed.

Through this project, I demonstrated proficiency in leveraging Rasa's capabilities for building AI-driven conversational agents, integrating external services like Ngrok and Twilio to extend functionality, and delivering a scalable solution that enhances communication and accessibility for users interacting with the chatbot via SMS.

Website Developed Using Django, SQLite, and Deployed on PythonAnywhere

In this project, I created a dynamic and interactive website dedicated to music using the Django framework, SQLite database, and deployed it on PythonAnywhere for seamless access and performance.

Link is- https://swatipython.pythonanywhere.com/

Key components and features of the project include:

• Django Framework: Utilized Django's powerful MVC architecture to structure the website, facilitating efficient development and organization of functionality.
• SQLite Database: Implemented SQLite to manage and store music-related data such as albums, artists, tracks, and user preferences securely.
• User Authentication and Authorization: Integrated Django's built-in authentication system to allow users to register, login, and manage their profiles, enhancing personalized experiences.
• Music Catalog Management: Developed functionalities for users to browse, search, and filter music content based on genres, artists, albums, and tracks.
• Interactive User Interface: Designed a responsive and intuitive UI using Django templates, HTML, CSS, and JavaScript to provide a seamless browsing experience across devices.
• Deployment on PythonAnywhere: Successfully deployed the website on PythonAnywhere, ensuring continuous availability and scalability without the need for extensive server management.
• Performance Optimization: Optimized database queries, implemented caching mechanisms, and utilized Django's middleware to enhance website performance and responsiveness.

Through this project, I showcased proficiency in Django web development, database management with SQLite, and deployment on cloud platforms like PythonAnywhere. The website serves as a comprehensive platform for music enthusiasts to explore, discover, and interact with diverse music content in a user-friendly environment.

• Bootstrap is also used
• It is a website which has the following functionalities: Signup,Login and creating and adding data to playlist is feasible.