Course Kickoff & Big Data Learning Roadmap

• What “Big Data” really means: volume, velocity, variety, veracity, value

• Big Data use cases across industries: retail, banking, telecom, healthcare, IoT

• Batch vs streaming processing: when to use which

• Big Data ecosystem overview: Hadoop, Spark, Kafka, Hive, HBase, Flink, cloud services

• Setting up the learning lab: local vs cloud labs, datasets, tools, and workflow

• How this course is delivered: theory + hands-on labs + mini projects + capstone

Data Engineering Foundations (Must-Know Basics)

• Data formats: CSV, JSON, Parquet, ORC, Avro (pros and cons)

• Compression basics: gzip, snappy, zstd and performance impact

• Schema design basics and schema evolution

• Data quality and validation concepts

• Data governance overview: metadata, catalog, lineage, ownership

• Understanding data pipelines and lifecycle: ingest → store → process → serve

Linux, Git & Networking Essentials for Big Data

• Linux commands for data engineers: file ops, permissions, process basics

• Working with logs and text: grep, awk, sed basics

• SSH, ports, firewall basics and cluster connectivity

• Git fundamentals for pipeline code and collaboration

• Shell scripting basics for automation in data projects

SQL for Big Data (Core Skill Module)

• SQL fundamentals refresh: SELECT, JOIN, GROUP BY, HAVING, window functions

• Query optimization basics: filtering early, partition-aware queries

• Hands-on SQL on large datasets: practical query patterns

• Analytical SQL patterns: funnels, cohorts, time-series aggregations

• Building business-friendly datasets and reporting layers

Hadoop Fundamentals & Distributed Storage Concepts

• Why Hadoop came: limitations of single-machine processing

• HDFS architecture: NameNode, DataNode, blocks, replication

• Reading and writing data in HDFS: practical commands and patterns

• Data locality and why it matters

• Fault tolerance and recovery concepts

• Best practices for directory structure and data organization in HDFS

Hadoop Cluster & Resource Management (YARN)

• YARN overview: ResourceManager, NodeManager, ApplicationMaster

• Scheduling basics: FIFO, Capacity Scheduler, Fair Scheduler

• Resource planning and cluster sizing concepts

• Running jobs and tracking job status

• Understanding containers, memory, CPU, and tuning basics

MapReduce (Concept + Practical Understanding)

• MapReduce model: map, shuffle, reduce explained clearly

• Writing simple MapReduce jobs (conceptual + lab)

• Combiner, partitioner, sorting, and grouping concepts

• Understanding performance bottlenecks in MapReduce

• Why Spark replaced many MapReduce use cases

Apache Hive for Data Warehousing on Big Data

• Hive architecture and where it fits in analytics

• Managed vs external tables, partitions, bucketing

• HiveQL deep dive: joins, aggregations, UDFs basics

• File formats and performance: Parquet/ORC with Hive

• Metastore and schema management

• Hands-on: create tables, load data, partition strategy, query optimization

Apache Spark Core (Most Important Module)

• Spark architecture: driver, executors, cluster manager

• RDD vs DataFrame vs Dataset: which to use and why

• Transformations vs actions and lazy evaluation

• Spark SQL fundamentals for data processing

• Catalyst optimizer overview (what it improves)

• Hands-on: build ETL pipelines using Spark DataFrames

• Performance tips: caching, partitions, shuffle, join strategy basics

Spark Advanced: Performance Tuning & Best Practices

• Partitioning strategies and avoiding data skew

• Broadcast joins and when to use them

• Handling large joins safely

• Memory tuning basics (executor memory, overhead, GC awareness)

• Monitoring Spark jobs: UI and key metrics

• Writing production-quality Spark jobs with robust handling

Spark Streaming / Structured Streaming

• Streaming basics: events, time windows, watermarking

• Structured Streaming concepts: micro-batch vs continuous

• State management and checkpointing

• Handling late data and exactly-once semantics (practical explanation)

• Hands-on: streaming pipeline reading from Kafka and writing to storage

Apache Kafka for Real-Time Data Pipelines

• Kafka architecture: brokers, topics, partitions, replication

• Producers and consumers: consumer groups and offsets

• Delivery semantics: at-most-once, at-least-once, exactly-once (meaning in practice)

• Schema management concepts (Schema Registry overview)

• Hands-on: publish/consume data and build streaming pipeline integration

NoSQL & Big Data Databases (HBase + Alternatives)

• Why NoSQL: key-value, column-family, document, graph overview

• HBase architecture: region servers, tables, column families

• Data modeling for HBase (how to design row keys properly)

• Read/write patterns and performance basics

• Use cases: time-series, fast lookups, large-scale storage

• Short overview: Cassandra, MongoDB, DynamoDB use-case mapping

Data Ingestion Tools & Pipeline Orchestration

• Ingestion patterns: batch import vs streaming ingest

• Apache Sqoop (concept) for RDBMS to HDFS (legacy but important)

• Apache Flume (concept) for log ingestion (legacy but important)

• Modern alternatives: Kafka Connect overview

• Orchestration with Apache Airflow: DAGs, scheduling, retries, dependencies

• Hands-on: build a simple Airflow pipeline for ETL orchestration

Data Lake, Lakehouse & Modern Big Data Design

• Data lake concept: raw, curated, serving layers

• Bronze/Silver/Gold model for clean data pipelines

• Lakehouse basics: why it exists and how it helps

• Delta Lake / Apache Iceberg / Apache Hudi overview (table formats)

• ACID on data lakes, schema evolution, time travel concept

• Choosing storage layout, partitioning, compaction basics

Cloud Big Data (AWS / Azure / GCP Overview)

• Cloud big data design patterns

• AWS mapping: S3, EMR, Glue, Athena, Kinesis (overview)

• Azure mapping: ADLS, HDInsight/Synapse, Data Factory, Event Hubs (overview)

• GCP mapping: GCS, Dataproc, BigQuery, Dataflow, Pub/Sub (overview)

• Cost control and best practices (partition pruning, compression, lifecycle policies)

Data Quality, Testing & Observability

• Data quality dimensions: accuracy, completeness, timeliness, uniqueness

• Validations at ingestion and transformation stages

• Building data tests (basic framework approach)

• Pipeline observability: logs, metrics, lineage, and alerts

• Monitoring SLAs, freshness, and anomaly detection basics

• Practical: create checks for null spikes, duplicates, schema drift

Security, Governance & Compliance for Big Data

• Access control models: RBAC, policies, and audit logs

• Encryption at rest and in transit (concepts)

• Data masking and tokenization basics

• Governance: metadata catalog, data lineage, ownership workflows

• Compliance overview: how to think about regulations without complexity

• Secure pipeline design best practices

Real-World Big Data Project Patterns

• Building a batch ETL pipeline end-to-end

• Building a streaming pipeline end-to-end

• Designing a data model for analytics and reporting

• Handling backfills and reprocessing safely

• Incremental vs full load strategies

• Handling failures: retries, dead-letter patterns, idempotency concepts

Capstone Project (Big Data End-to-End Implementation)

• Capstone Goal: design and implement a modern data platform workflow

• Typical implementation path:

  • Ingest data (batch + streaming)

  • Store data in a data lake structure

  • Process data with Spark

  • Query with Hive/Spark SQL

  • Serve curated datasets for analytics

  • Add orchestration with Airflow

  • Add monitoring, data quality checks, and documentation

• Final deliverables: architecture diagram, pipeline code, testing, job run evidence

Interview Preparation & Job Readiness

• Big Data interview questions: Hadoop, Spark, Kafka, SQL, pipeline design

• Common troubleshooting scenarios: skew, shuffle, memory errors, slow queries

• Portfolio guidance: how to present your capstone project

• Resume keywords and practical framing for data engineering roles

• Next roadmap: advanced Spark, Flink, cloud certifications, and lakehouse mastery