Big Data with Hadoop & Beyond

Big Data with Hadoop & Beyond

Cluster to Cloud Analytics - 3 to 5 days

Tagline: Build and scale real-world data pipelines with Hadoop, Spark, and Databricks—guided by 30+ years of hands-on industry expertise.

The numbers tell a story of unprecedented scale: petabytes of transaction records, sensor readings that arrive millisecond by millisecond, social interactions mapped across billions of users. This is the raw material of modern enterprise, and traditional computing infrastructure buckles under its weight.

Hadoop emerged not as elegant software architecture, but as pragmatic necessity. Engineers at Yahoo faced servers choking on web crawl data and made a radical choice: instead of buying more powerful machines, they would teach thousands of modest computers to think as one. The result was a framework that could digest data sets previously considered impossible to process.

Yet even revolutionary solutions evolve or risk obsolescence. Where Hadoop excelled at handling massive batch operations, today's organizations demand something different—the ability to query streaming data, run machine learning algorithms in real time, and iterate on analysis without waiting hours for results. Apache Spark answered this call with in-memory processing that can execute certain operations up to 100 times faster than traditional disk-based approaches.

Databricks pushed the boundaries further still, recognizing that the real bottleneck wasn't computational power but human collaboration. Their platform transformed data analysis from a solitary pursuit into a shared workspace where code, visualizations, and insights flow seamlessly between team members.

This course strips away marketing promises to focus on practical mastery. You'll construct Hadoop clusters from Linux fundamentals, navigate the ecosystem of open-source tools that form the backbone of modern data infrastructure, and progress to the high-performance analytics that distinguish competent practitioners from true experts. The knowledge here isn't theoretical—it's the hard-won experience of engineers who've scaled systems under real-world pressure.

Learning Outcomes

By the end of this course, you will be able to:

• Articulate the key characteristics and challenges of big data.
• Explain Hadoop’s architecture, components, and operational principles.
• Deploy and manage a secure, multi-node Hadoop cluster on Linux.
• Ingest, process, and analyze data using MapReduce, Hive, Pig, and HBase.
• Build scalable, high-performance data pipelines using Apache Spark 4.0, including Structured Streaming and MLlib.
• Leverage the Databricks Lakehouse Platform for production-grade ETL, analytics, and machine learning workflows.
• Apply the latest features of Spark (including Spark Connect, ANSI SQL, advanced streaming, and observability).
• Optimize performance, monitor jobs, and prepare for Databricks Spark certification.

Prerequisites

• Basic familiarity with the Linux command-line and system administration.
• Programming knowledge in Python, Java, or Scala.
• Working understanding of SQL and data structures.
• Prior experience with databases or scripting is helpful but not mandatory.

5-Day Course Outline

Days 1–3: Hadoop Foundations

Day 1: Understanding Big Data & the Hadoop Ecosystem

• The landscape of big data in 2025: business cases and industry adoption trends.
• Defining the 5 Vs: volume, velocity, variety, veracity, value.
• Limitations of traditional RDBMS for big data use-cases.
• Core Hadoop architecture: HDFS, YARN, and MapReduce explained in detail.
• HDFS internals: block storage, replication, failover, NameNode and DataNode roles.
• YARN as a resource manager: scheduling, containers, scalability, and high availability.
• The MapReduce paradigm: execution pipeline, mapping, shuffling, reducing, combiners.
• Key components of the Hadoop ecosystem: introduction to Hive, Pig, HBase, Sqoop, and Flume.

Day 2: Deploying and Managing a Hadoop Cluster (Linux-Based)

• Preparing Linux environments for Hadoop: user accounts, SSH key setup, permissions.
• Installing Java (OpenJDK 8/11) and Hadoop binaries: download, extraction, verification.
• Configuration essentials: editing core-site.xml, hdfs-site.xml, yarn-site.xml, and environment variables.
• Formatting and initializing the HDFS filesystem.
• Starting and stopping Hadoop daemons: step-by-step for NameNode, DataNode, ResourceManager, NodeManager.
• Accessing and interpreting Hadoop and YARN web interfaces for monitoring.
• Running basic HDFS commands: file upload/download, directory creation, permissions, quotas.
• Troubleshooting common installation and configuration issues.
• Health checks: using hdfs fsck, logs, and cluster status commands.
• Securing the cluster: basic authentication and firewall considerations.

Day 3: Data Ingestion and Processing with Hadoop Tools

• Hive: architecture, data warehouse concepts, the Metastore, writing and optimizing HiveQL queries.
• Pig: ETL pipeline design, Pig Latin scripting, custom UDFs, data flow examples.
• HBase: NoSQL concepts, column family design, read/write patterns, integrating with HDFS.
• Workflow automation: scheduling and chaining jobs with Oozie.
• Importing data with Sqoop (from RDBMS to HDFS/Hive) and Flume/Kafka (for streaming ingestion).
• Comparative strengths: When to use MapReduce, Hive, Pig, or HBase.
• Real-world scenarios: Data cleansing, transformation, and aggregation at scale.
• Performance monitoring and resource tuning at the platform level.

Days 4–5: Advanced Spark and Databricks

Day 4: Advanced Data Processing with Apache Spark 4.0

• Spark architecture: driver, executors, cluster manager, task scheduling, DAG execution.
• RDDs, DataFrames, and Datasets: similarities, differences, best practices.
• Spark SQL: schema inference, SQL queries, window functions, and UDF integration.
• Hands-on transformations: map, filter, reduceByKey, join, groupBy, aggregation.
• Structured Streaming: continuous data pipelines, windowed aggregations, watermarks, late data handling.
• Performance tuning: partitioning, caching/persistence, shuffle optimization, broadcast variables.
• Introduction to Spark MLlib: building and evaluating pipelines for regression, classification, clustering.
• Spark 4.0 new features:
  • Spark Connect architecture for remote client/server.
  • Default ANSI SQL compliance, PIPE and session variable support.
  • VARIANT type for handling semi-structured data.
  • Enhanced stateful structured streaming and arbitrary stateful processing.
  • Structured logging and improved observability.
  • Expanded Python APIs: native plotting, new DataSource API, polymorphic UDTFs.
• Integrating Spark with Hadoop YARN, Standalone, and Kubernetes.

Day 5: Databricks Lakehouse and Production-Grade Data Engineering

• Introduction to the Databricks platform: workspace, notebooks, and cluster management.
• Delta Lake fundamentals: ACID transactions, schema enforcement, time travel, upserts, deletes.
• Building robust batch and streaming ETL pipelines using Databricks and Delta Lake.
• Optimizing storage and queries: Z-ordering, file compaction, caching.
• Monitoring, debugging, and profiling Spark jobs in Databricks.
• Orchestrating production workflows: Databricks Jobs, integration with CI/CD, job parameterization.
• Security and governance in Databricks: access controls, secrets, audit logs.
• Collaboration features: notebooks, dashboards, versioning, and team workflows.
• Certification preparation: Databricks Certified Associate Developer for Apache Spark exam—key topics and exam strategies.
• Discussion: Comparing Spark, Databricks, and other modern big data platforms for real-world needs.