Real-World Traffic Analysis Project-Medallion Arch,Autoloader,Structured Streaming,Workflows,Environments,Github,CICD
β±οΈ Length: 5.6 total hours
β 4.96/5 rating
π₯ 154 students
π September 2025 update
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- Course Overview
- Embark on a comprehensive, hands-on journey to master the Databricks platform within the robust Google Cloud (GCP) ecosystem.
- This project-centric course demystifies the end-to-end development and deployment of sophisticated data analytics solutions.
- Leverage cutting-edge cloud-native tools and methodologies to build a production-ready data pipeline from ingestion to insight generation.
- Gain practical experience in managing complex data flows, ensuring data quality, and operationalizing analytics for real-world impact.
- The curriculum is meticulously designed to mirror industry best practices, providing a tangible, portfolio-worthy project.
- By the end, you will possess the confidence and skills to tackle similar data engineering challenges in a cloud environment.
- Explore the synergy between Databricks’ powerful processing capabilities and GCP’s scalable infrastructure.
- Understand the lifecycle of data within a modern data platform, from raw ingestion to actionable business intelligence.
- This course is not just about learning tools; it’s about building a complete, deployable solution.
- Target Audience
- Data Engineers seeking to enhance their cloud-based data pipeline development skills.
- Data Scientists looking to bridge the gap between raw data and analytical readiness on GCP.
- Cloud Architects interested in implementing robust data solutions on Databricks and GCP.
- Software Developers aiming to specialize in data engineering and cloud analytics.
- Anyone passionate about building practical, real-world data projects using leading cloud technologies.
- Requirements / Prerequisites
- Foundational Knowledge of Python: Proficiency in Python programming is essential for scripting and data manipulation.
- Basic Understanding of SQL: Familiarity with SQL concepts for data querying and manipulation.
- Familiarity with Cloud Concepts: A general understanding of cloud computing principles.
- Exposure to Big Data: Some prior exposure to big data concepts and challenges is beneficial.
- Google Cloud Platform (GCP) Account (Recommended): While not strictly mandatory for learning, having access to a GCP account will allow for direct implementation and experimentation.
- Comfort with Command Line Interface (CLI): Basic command-line operations will be helpful for certain setup and integration steps.
- Skills Covered / Tools Used
- Databricks Platform: In-depth utilization of Databricks Runtime, notebooks, and job orchestration features.
- Google Cloud Storage (GCS): Mastering cloud object storage for efficient data handling.
- PySpark: Advanced techniques for distributed data processing.
- Databricks SQL: Querying and managing data within Databricks.
- Structured Streaming: Real-time data processing and continuous analytics.
- Auto Loader: Efficiently processing incremental data streams.
- Unity Catalog: Centralized governance and metadata management.
- GitHub: Version control, collaboration, and code management.
- CI/CD Principles: Implementing continuous integration and continuous deployment workflows.
- Data Modeling: Designing and implementing effective data structures.
- ETL/ELT Design: Building robust data extraction, transformation, and loading pipelines.
- GCP Integration: Seamlessly connecting Databricks with other GCP services.
- Benefits / Outcomes
- Develop a **portfolio-ready, end-to-end data analytics project** ready for demonstration.
- Gain practical, **hands-on experience** with a top-tier cloud data platform.
- Understand the intricacies of building **scalable and robust data pipelines** on GCP.
- Master techniques for **real-time data ingestion and processing** using Databricks features.
- Acquire expertise in implementing **data governance and cataloging** with Unity Catalog.
- Become proficient in **automating and orchestrating complex data workflows**.
- Learn to integrate data projects with **industry-standard version control and CI/CD practices**.
- Be able to derive **meaningful business insights** from real-world datasets.
- Enhance your **employability** in the high-demand fields of data engineering and cloud analytics.
- Build the confidence to tackle **advanced data challenges** in a professional setting.
- Course Project Focus
- Analyze complex, real-world **traffic data** to uncover critical patterns and trends.
- Explore geographical traffic densities and identify high-congestion zones.
- Investigate the evolving landscape of **Electric Vehicle (EV) adoption** through traffic data proxies.
- Quantify and understand **year-over-year traffic volume fluctuations**.
- Transform raw, unstructured traffic sensor data into a structured, analysis-ready format.
- The project serves as a living example of applying learned concepts in a practical context.
- PROS
- Project-Based Learning: Highly practical, focusing on building a complete, real-world application.
- Comprehensive Tool Integration: Covers a wide array of essential Databricks and GCP services.
- Up-to-Date Content: September 2025 update ensures relevance with current industry standards.
- High Rating and Student Numbers: Indicates proven value and student satisfaction.
- End-to-End Solution: Addresses the full data lifecycle from ingestion to analysis.
- CONS
- Intensive Scope: The comprehensive nature might require a significant time commitment for learners new to all the tools involved.
Learning Tracks: English,IT & Software,Other IT & Software
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