Real-Time Data Pipeline Design Digital Course
🌊 Batch Processing Tells You What Happened. Real-Time Tells You What’s Happening. The Difference Is Often What Matters.
The architectural shift from batch data processing to real-time streaming data pipelines is one of the most significant and most technically demanding transitions in modern data engineering. It is also one of the most frequently mismanaged, because the patterns, failure modes, and operational characteristics of streaming systems are genuinely different from those of batch systems in ways that catch engineers who approach them with a batch-processing mental model. A batch pipeline that fails can be rerun. A streaming pipeline that falls behind its consumer lag produces growing delays that compound until the system is processing stale data and the consumer depends on it. A batch transformation that produces incorrect output can be corrected before consumers see it. A streaming transformation error propagates to downstream consumers in real time, potentially corrupting derived datasets before the error is detected.
The Real-Time Data Pipeline Design Digital Course is the most comprehensive digital course available for data engineers, backend engineers, and software architects who need to design, implement, operate, and troubleshoot real-time streaming data pipelines. It covers the full technical landscape of real-time data engineering: streaming architecture theory, Apache Kafka in depth, stream processing with Apache Flink and Kafka Streams, schema management, exactly-once processing guarantees, stateful stream processing, windowing and watermarking, real-time ML feature serving, operational observability, and the systematic failure mode analysis that makes the difference between a streaming system that handles operational reality and one that only handles the happy path.
📦 Complete Course Package Contents
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Core Course Curriculum (.pdf, 12 modules, 280+ pages)
Module 1: Streaming Architecture Foundations (22 pages) The conceptual framework for real-time data systems. Covers: the event-driven architecture paradigm vs. the request-response paradigm, how streaming systems model reality (every change as an event, the log as the ground truth), the Lambda architecture, the Kappa architecture, and the trade-offs that inform the choice between them in different organizational contexts. Covers the CAP theorem as it applies to streaming systems (availability vs. consistency trade-offs in distributed streaming platforms), delivery guarantee semantics (at-most-once, at-least-once, exactly-once, and what exactly-once actually means across the producer-broker-consumer chain), and the distinction between streams and tables (and the stream-table duality that underlies ksqlDB and Kafka Streams).
Module 2: Apache Kafka Architecture Deep Dive (30 pages) Kafka from the perspective of someone who needs to design and operate production systems. Covers: topic and partition architecture (how partitioning affects ordering guarantees, throughput, and consumer parallelism), the replication protocol (ISR, leader election, unclean leader election and its durability trade-off), producer configurations that determine throughput vs. durability (acks, batch.size, linger.ms, compression.type, and their interaction), consumer group mechanics (partition assignment, rebalance protocols, static group membership for reducing rebalance frequency), offset management (auto-commit vs. manual commit, at-least-once delivery and idempotency requirements), consumer lag (what it means, how to measure it, how to act on it), Kafka Connect architecture (connectors, tasks, workers, exactly-once delivery through Kafka Connect), and Kafka broker configuration for production environments (log retention, log compaction, quotas, monitoring configuration).
Module 3: Schema Design and Schema Evolution (20 pages) Managing data contracts in streaming systems. Covers: why schema management is more critical in streaming than in batch systems (downstream consumers cannot be easily re-run if a schema change breaks them), Apache Avro (schema definition, schema evolution rules: backward, forward, full compatibility and what each allows), Protocol Buffers for Kafka (when to choose Protobuf over Avro), JSON Schema for Kafka (the trade-offs vs. binary formats), the Confluent Schema Registry (subject naming strategies, compatibility configuration, schema registration in producer code, schema retrieval in consumer code), and schema evolution workflow in production (how to roll out a schema change safely without coordinating a synchronized deployment across all producers and consumers).
Module 4: Stream Processing with Apache Flink (28 pages) Flink as the primary engine for complex real-time processing. Covers: Flink architecture (JobManager, TaskManager, execution graph), the DataStream API (sources, transformations, sinks, type system), stateful processing (value state, list state, map state, broadcast state, state backends: RocksDB vs. memory, state size considerations), time semantics (event time vs. processing time vs. ingestion time, watermarks, how watermarks propagate through a Flink job), windowing (tumbling windows, sliding windows, session windows, global windows, window functions), side outputs for routing late data, Flink SQL and Table API for declarative stream processing, checkpointing (exactly-once semantics through aligned and unaligned checkpoints, checkpoint configuration for throughput vs. recovery time trade-off), and Flink job monitoring and troubleshooting (task manager metrics, checkpoint duration, backpressure visualization).
Module 5: Kafka Streams for Service-Integrated Processing (20 pages) Kafka Streams as the library choice for stream processing embedded in microservices. Covers: when to choose Kafka Streams vs. Flink (operational simplicity and deployment model vs. sophisticated stateful processing requirements), the Streams DSL (KStream, KTable, GlobalKTable, stateless transformations, stateful aggregations), the Processor API for custom processing logic, state stores (in-memory and persistent, queryable state), join semantics (stream-stream join, stream-table join, stream-global-table join with their respective behavior differences), interactive queries for serving state externally, and deployment considerations (topology parallelism, standby replicas for state recovery, topology versioning).
Module 6: Exactly-Once Semantics in Practice (18 pages) The most technically nuanced topic in real-time data engineering. Covers: the idempotent producer (how it prevents duplicate messages at the broker level), transactions in Kafka (how transactions enable atomic write across multiple partitions and the read-committed isolation level for consumers), exactly-once in Kafka Streams (the transactional exactly-once mode and its performance implications), exactly-once across external systems (why Kafka transactions alone don’t guarantee exactly-once delivery to external sinks and what additional mechanisms are required), and the practical guidance for when exactly-once is worth its operational cost vs. when at-least-once with idempotent consumers is the right trade-off.
Module 7: Real-Time Aggregation and OLAP Integration (22 pages) Serving analytics from streaming data. Covers: Lambda architecture for real-time + historical analytics, the emergence of real-time OLAP databases (Apache Druid, ClickHouse, Apache Pinot) and their streaming ingestion capabilities, Kafka-to-Druid pipeline design, materialized view patterns for pre-aggregating common queries, real-time dashboard update patterns (WebSocket-based push, polling optimization, change data capture for derived view maintenance), and the windowed aggregation patterns that produce correct real-time metrics (how to handle late-arriving events, how to handle window boundary correctness).
Module 8: Change Data Capture and Debezium (20 pages) Real-time database replication as a streaming use case. Covers: CDC approaches (log-based, trigger-based, query-based) and their trade-offs for different databases, Debezium architecture (connectors, the Debezium engine, snapshot vs. streaming mode), Debezium connector configuration for PostgreSQL (logical replication, publication and replication slot configuration), MySQL (binlog configuration), MongoDB (oplog configuration), schema evolution handling in CDC streams (what happens when source schema changes), CDC event structure and the envelope format, handling CDC events in downstream consumers (upsert patterns, delete handling, the initial snapshot problem and its solutions), and operational considerations for production Debezium deployments.
Module 9: Real-Time Feature Engineering for ML (20 pages) Streaming pipelines for machine learning feature serving. Covers: the online-offline feature skew problem (why features computed differently at training time vs. serving time are one of the most common causes of ML model performance degradation in production), feature stores (Feast, Tecton, Hopsworks) and their architecture for real-time feature serving, streaming feature computation patterns (feature computation in Kafka Streams or Flink writing to a feature store), point-in-time correct feature retrieval for training data generation, and the operational challenges of real-time feature pipelines (latency requirements, throughput requirements, state management at ML scale).
Module 10: Pipeline Observability and Operational Excellence (20 pages) Making streaming systems observable and operable. Covers: the metrics that matter for streaming systems (consumer lag as the primary health signal, end-to-end latency measurement, processing throughput, error rate by error type, checkpointing health for Flink jobs), Kafka metrics landscape (broker metrics, producer metrics, consumer metrics, the JMX to Prometheus bridge), alerting strategy for streaming systems (lag-based alerting, lag rate-of-change alerting, error budget alerting), distributed tracing in streaming pipelines (propagating trace context through Kafka message headers, visualizing cross-service event flows), and operational runbooks for the most common streaming system failure modes.
Module 11: Performance Tuning and Scalability (20 pages) Getting maximum performance from streaming infrastructure. Covers: Kafka throughput optimization (producer batching, compression selection, partition count guidelines), consumer scaling (consumer group parallelism limits, rebalance frequency reduction), Flink performance tuning (parallelism configuration, network buffer tuning, state backend optimization, checkpointing overhead reduction), and capacity planning for streaming systems (how to size a Kafka cluster, how to estimate resource requirements for a Flink job based on throughput and state requirements).
Module 12: Architecture Patterns and Case Studies (20 pages) Applying the course concepts to real architectural scenarios. Covers: event sourcing implementation with Kafka as the event log, CQRS with Kafka for command and query separation, real-time fraud detection pipeline design, user activity stream processing pipeline design, IoT data processing pipeline design, and a multi-source aggregation pipeline case study tracing every design decision through to its operational implementation.
Hands-On Lab Guide (.pdf + configuration files, 15 labs) Fifteen structured lab exercises with complete Docker Compose environments (no cloud account required):
Lab environments include: single-broker Kafka cluster for basic exercises, multi-broker Kafka cluster for replication and partition experiments, Kafka + Flink for stream processing labs, Kafka + Schema Registry for schema management labs, Kafka + Debezium + PostgreSQL for CDC labs, and a full demonstration environment combining all components.
Each lab includes: learning objectives, prerequisite knowledge reference, complete setup instructions, step-by-step tasks with expected outputs, verification commands confirming successful completion, and extension challenges for advanced learners.
Architecture Decision Record Templates for Streaming Systems (.md, 12 templates) Pre-structured ADR templates for the most consequential streaming architecture decisions: message broker selection, delivery guarantee selection, schema format selection, stream processing engine selection, state backend selection, partition count determination, retention policy design, consumer group architecture, exactly-once adoption decision, and feature store integration approach. Each template contains the specific questions and trade-off dimensions relevant to that decision.
📂 What Downloads to Your Device
📚 Core Curriculum (.pdf, 12 modules, 280+ pages) — Complete real-time pipeline training from architecture theory through advanced Kafka, Flink, CDC, ML features, and operational excellence 🔬 Hands-On Lab Guide (.pdf + Docker Compose configs, 15 labs) — Complete lab environments requiring no cloud account, from basic Kafka through full CDC and stream processing labs 📐 Architecture Decision Record Templates (.md, 12 templates) — Pre-structured ADRs for every major streaming architecture decision
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