Big Data & Analytics
Big Data covers distributed data processing, analytics platforms, and visualization tools. Learn how modern organizations handle massive datasets at scale.
Tutorials in This Section
Learning Path
flowchart LR
A[Big Data Overview] --> B[Apache Hadoop]
B --> C[Apache Spark]
A --> D[Data Warehousing]
D --> E[Modern Data Warehousing]
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Pages in this section
Big Data Explained — Complete Beginner's Guide
Learn Big Data fundamentals: the 3 Vs (Volume, Velocity, Variety), how Netflix and Amazon use big data, and the tools that process massive datasets at scale.
✓ LiveApache Hadoop — Complete Beginner's Guide
Learn Apache Hadoop from scratch: HDFS distributed storage, MapReduce processing model, and how to process large datasets across a cluster. Includes simple data processing examples.
✓ LiveApache Spark — Complete Beginner's Guide
Learn Apache Spark: RDDs, DataFrames, in-memory vs disk-based processing. Compare Spark with Hadoop. Includes PySpark code examples for data processing and analysis.
✓ LiveData Warehousing Explained — A Beginner's Guide
Learn data warehousing fundamentals: ETL pipelines, star schema vs snowflake schema, data warehouse vs data lake differences, and how businesses use them for analytics.
✓ LiveApache Kafka Deep Dive — Topics, Partitions, Consumer Groups & Exactly-Once
Master Apache Kafka: topics, partitions, consumer groups, offset management, replication, and exactly-once semantics. Includes Python confluent-kafka producing/consuming examples.
✓ LiveTableau Guide — Dimensions, Measures, Dashboards & Calculated Fields
Learn Tableau: dimensions vs measures, worksheets, dashboards, calculated fields, LOD expressions, and connecting to data sources. Includes a sales dashboard building example.
✓ LivePower BI Guide — Data Modeling, DAX Formulas, Power Query & Dashboards
Master Microsoft Power BI: data modeling (star schema), DAX formulas, measures vs calculated columns, Power Query (M language), reports and dashboards with practical examples.
✓ LiveApache Flink — Stream Processing, Event Time, Watermarks & Windowing
Master Apache Flink: stream processing, event time vs processing time, watermarks, windowing (tumbling, sliding, session), state management, exactly-once semantics. Compare to Spark Streaming with Flink word count example.
✓ LiveData Governance Explained — Catalogs, Lineage, Quality & GDPR Compliance
Master data governance: data catalogs, data lineage, data quality frameworks, metadata management, GDPR/CCPA compliance, and data contracts. Tools: Apache Atlas, DataHub, Great Expectations.
✓ LiveReal-Time Analytics Explained — Streaming Architectures, Lambda vs Kappa & Dashboards
Master real-time analytics: streaming architectures, Lambda vs Kappa architecture, dashboards, alerting, anomaly detection. Example: real-time dashboard with Kafka + ClickHouse.
✓ LiveData Lake Architecture — Medallion, Delta Lake, Iceberg & Hudi
Master modern data lake architecture: medallion bronze/silver/gold layers, Delta Lake, Apache Iceberg, Apache Hudi, catalog integration, and real-world patterns.
✓ LiveModern Data Warehousing — Snowflake, BigQuery, Redshift Guide
Learn modern data warehousing: Snowflake storage/compute separation, BigQuery slots and partitioning, Redshift distribution styles and sort keys. Compare Snowflake vs Redshift vs BigQuery.
✓ LiveStream Processing Deep Dive — Event Time, Watermarks & Exactly-Once
Deep dive into stream processing: event time vs processing time, watermarks for late data, exactly-once semantics, stateful vs stateless operators, and Kappa architecture patterns.
✓ LiveApache Storm — Real-Time Stream Processing with Topologies
Apache Storm tutorial: Storm topologies with spouts and bolts, Trident for exactly-once processing, reliability mechanisms, and comparison with Flink and Spark Streaming.
✓ LiveData Catalog & Lineage — Atlas, DataHub, Amundsen & Column-Level Lineage
Master data catalog tools: Apache Atlas, DataHub, Amundsen. Understand column-level lineage, impact analysis, data discovery, and governance integration for enterprise data platforms.
✓ Live