Course Content
Module 1: Introduction to Streamlit for Data Engineering
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1.1 Overview of Streamlit
:: What is Streamlit?
:: Key features and benefits for data engineers
:: Comparison with other frameworks
1.2 Setting Up Your Environment
:: Installing Streamlit and necessary dependencie
:: Configuring development environments (IDE setup)
:: Running and understanding your first Streamlit app
1.3 Streamlit’s Role in Data Engineering
:: Integrating Streamlit into data pipelines
:: Understanding Streamlit’s architecture
1.4 PoC 1: Build a Simple Data Dashboard
:: Create a basic Streamlit app that visualizes static data
:: Incorporate basic widgets and layout features
:: Demonstrate understanding of Streamlit’s capabilities
Module 2: Building Interactive Data Applications
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2.1 Basic Widgets and Layouts
:: Introduction to Streamlit widgets (text inputs, buttons, sliders)
:: Layout options (sidebar, columns, containers)
:: Handling user input and interactions
2.2 Displaying and Analyzing Data
:: Displaying dataframes using st.write(), st.dataframe(), st.table()
:: Basic data analysis and manipulation with Streamlit
:: Creating and customizing visualizations (charts, graphs)
2.3 Advanced Visualization Techniques
:: Integrating with Matplotlib, Seaborn, Plotly, and Altair
:: Building interactive and dynamic visualizations
:: Customizing visualizations for user interaction
2.4 PoC 2: Interactive Data Exploration Tool
:: Build an interactive application to explore a dataset
:: Use multiple Streamlit widgets to filter and analyze data
:: Implement advanced visualizations and dynamic updates
Module 3: Integrating with Kafka
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3.1 Introduction to Apache Kafka
:: Overview of Kafka and its role in data streaming
:: Kafka components: Brokers, Topics, Partitions, Consumers
3.2 Setting Up Kafka
:: Installing and configuring Kafka
:: Creating Kafka topics and managing data streams
3.3 Integrating Kafka with Streamlit
:: Setting up a Kafka producer and consumer
:: Streaming data to and from Kafka within a Streamlit app
:: Visualizing real-time data updates in Streamlit
3.4 PoC 3: Real-Time Data Dashboard with Kafka
:: Build a Streamlit app that consumes data from Kafka in real-time
:: Visualize real-time data streams and updates
:: Handle real-time data processing and interaction
3.5 PoC 4: Kafka Data Pipeline Integration
:: Integrate a Kafka-based data pipeline with a Streamlit app
:: Demonstrate end-to-end data flow from Kafka to visualization
Module 4: Leveraging Snowflake for Data Warehousing
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4.1 Introduction to Snowflake
:: Overview of Snowflake architecture and features
:: Setting up a Snowflake account and environment
4.2 Connecting Snowflake to Streamlit
:: Installing Snowflake Python connectors
:: Establishing connections and executing queries
:: Fetching and displaying data from Snowflake in Streamlit
4.3 Advanced Snowflake Integration
:: Working with Snowflake’s data types and structures
:: Handling large datasets and optimizing queries
:: Using Snowflake for interactive data exploration and analysis
4.4 PoC 5: Snowflake Data Exploration and Reporting
:: Build a Streamlit app that connects to Snowflake and visualizes query results
:: Implement interactive data exploration features
:: Optimize query performance and handle large datasets
4.5 PoC 6: Data Warehousing Dashboard
:: Create a comprehensive dashboard to monitor and analyze data warehoused in Snowflake
:: Use advanced Snowflake features to enrich the data and enhance the dashboard
Module 5: Cloud Integration and Deployment
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5.1 Cloud Platforms Overview
:: Overview of major cloud platforms (AWS, Azure, Google Cloud)
:: Key services for deploying Streamlit applications
5.2 Deploying on Streamlit Sharing
:: Overview and setup of Streamlit Sharing
:: Deployment process and best practices
5.3 Deploying on AWS
:: Setting up an AWS environment (EC2, S3, RDS)
:: Deploying Streamlit apps on AWS Elastic Beanstalk
:: Managing application scaling and monitoring
5.4 Deploying on Azure
:: Overview of Azure services (App Services, Azure Blob Storage)
:: Deploying Streamlit apps on Azure App Services
:: Configuration and scaling options
5.5 Deploying on Google Cloud
:: Google Cloud Platform services (Compute Engine, Cloud Storage)
:: Deploying Streamlit apps on Google App Engine
:: Managing deployments and scaling
5.6 Continuous Deployment and Monitoring
:: Setting up CI/CD pipelines for Streamlit apps
:: Monitoring and logging applications in the cloud
5.7 PoC 7: Cloud Deployment of a Full-Stack Application
:: Deploy a fully integrated Streamlit application with Kafka and Snowflake on a cloud platform
:: Implement CI/CD pipelines for automated deployment
:: Configure monitoring and scaling for the application
Module 6: Advanced Topics and Real-World Projects
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6.1 Advanced Streamlit Features
:: Building custom Streamlit components
:: Integrating machine learning models and advanced analytics
:: Handling asynchronous operations and real-time data
6.2 Real-World Use Cases
:: Case studies of Streamlit applications in data engineering
:: Designing and building a comprehensive data dashboard
:: Implementing end-to-end data pipelines with Kafka, Snowflake, and Streamlit
6.3 Project
:: Develop a complete Streamlit application incorporating Kafka, Snowflake, and cloud deployment
:: Project guidelines: from concept to deployment
:: Peer review and feedback
6.4 Future Trends and Continued Learning
:: Exploring emerging trends in data engineering and Streamlit
:: Advanced topics and additional resources
:: Engaging with the Streamlit and data engineering communities
Streamlit
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