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Big Data & Distributed Systems: 8-Week Intensive Program
Month 1 – Learning and Hands-on Practice
Week 1-2: Big Data & Hadoop Fundamentals
Concepts: Learn the foundations of distributed computing, including HDFS, MapReduce, and YARN.
Tools: Gain hands-on experience with Hadoop (Cloudera or Hortonworks Sandbox), Hive, and Pig.
Practice: Set up a mini Hadoop cluster and run sample MapReduce jobs to process data.
Deliverable: Build an ETL pipeline using Hive on a sample e-commerce dataset.
Week 3-4: Apache Spark & Distributed Processing
Concepts: Understand core Spark components such as RDDs, DataFrames, Spark SQL, and Spark Streaming.
Tools: Work with PySpark and explore Spark both locally and on cloud platforms like AWS EMR or Databricks.
Practice: Develop Spark jobs focused on log analysis and real-time streaming analytics.
Deliverable: Perform data analysis on over one million rows of Twitter data or log files.
Month 2 – Projects and Interview Preparation
Top 3 Capstone Projects (Best for Group Work)
Project 1: Real-Time Data Pipeline with Kafka, Spark, and Cassandra
Goal: Process and analyze real-time streaming data from IoT or sensor devices.
Stack: Kafka for data ingestion, Spark Streaming for processing, and Cassandra for NoSQL storage.
Key Features: Includes live data generation such as temperature and humidity, Kafka topic ingestion and Spark-based transformations, data storage in Cassandra for rapid retrieval, and real-time monitoring with Grafana.
Skills Gained: Proficiency in building streaming architectures, NoSQL schema design, and ensuring system fault tolerance.
Project 2: Scalable Data Lake with Hadoop, Hive, and AWS S3
Goal: Build a scalable and cost-efficient data lake solution for an e-commerce platform.
Stack: Hadoop and HDFS for distributed storage, Hive for querying, AWS S3 for cloud storage, and AWS Glue for data cataloging and transformation.
Key Features: Implements ingestion of raw data such as customer orders and transactions, supports partitioning and schema evolution, enables SQL-based querying through Hive, and is deployable on AWS with automation tools.
Skills Gained: Hands-on experience in designing cloud-based data lakes, building ETL pipelines, and working with scalable storage and query systems.
Project 3: Interactive Analytics Dashboard using Spark and D3.js or Power BI
Goal: Create a real-time analytics dashboard that visualizes insights from live data streams.
Stack: Apache Spark for data processing, Kafka for ingestion, PostgreSQL or Redis for intermediate storage, and D3.js or Power BI for visualization.
Key Features: Includes sentiment analysis on live Twitter feeds or news articles, real-time key performance indicators such as volume trends and anomaly detection, and visually engaging dashboards for stakeholders.
Skills Gained: Mastery of real-time data analytics, API integration, front-end visualization, and scaling distributed systems.
Week 7-8: Interview Preparation and Job Referrals
Focus on building a well-structured resume tailored for Big Data Engineer roles, highlighting key projects and tools used.
Strengthen your problem-solving skills with Data Structures and Algorithms specifically relevant to data engineering roles.
Participate in mock interviews covering system design, SQL queries, and Apache Spark-based scenarios to boost confidence and technical clarity.
Start networking and applying through referral channels such as LinkedIn, AngelList, InstaHyre, and your university or bootcamp alumni groups.
Bonus Tips
Host your code on GitHub with well-organized repositories and detailed documentation to showcase your professionalism and coding practices.
Record short demos of your projects to build a compelling project portfolio that hiring managers can view quickly.
Highlight experience or familiarity with tools like Apache Airflow, Docker, and Kubernetes in your resume and interviews to stand out among applicants.