Description

This curriculum spans the design and operational rigor of a multi-workshop program on industrial-scale data platforms, covering the breadth of decisions and trade-offs encountered in building and maintaining secure, observable, and cost-effective data pipelines across batch and streaming contexts.

Module 1: Architecting Scalable Data Ingestion Pipelines

Choose between batch and streaming ingestion based on SLA requirements and downstream latency tolerance.
Implement idempotent consumers in Kafka to handle duplicate messages during retries without corrupting data.
Design schema evolution strategies using Avro with compatibility rules to support backward and forward reading.
Configure backpressure handling in Spark Streaming to prevent executor OOM errors during traffic spikes.
Select appropriate serialization formats (e.g., Parquet vs. JSON) based on query patterns and storage cost.
Integrate change data capture (CDC) tools like Debezium with transactional databases for real-time replication.
Deploy ingestion pipelines with infrastructure-as-code (Terraform) to ensure reproducible environments.
Monitor ingestion lag using Kafka consumer group metrics and trigger auto-scaling of consumers.

Module 2: Distributed Storage Design for Big Data

Partition large datasets by time and entity key to optimize query performance and lifecycle management.
Choose between HDFS and cloud object stores (S3, ADLS) based on cost, durability, and compute integration.
Implement tiered storage policies to move cold data from hot to archive tiers automatically.
Configure replication factors in HDFS balancing fault tolerance against storage overhead.
Design metadata management for Parquet files using Hive Metastore or AWS Glue Catalog.
Enforce file size targets (128MB–1GB) during writes to prevent small file problems in HDFS.
Use erasure coding in HDFS for cold data to reduce storage footprint by 50% compared to replication.
Implement object locking and versioning in S3 to meet compliance requirements for data immutability.

Module 3: Data Transformation at Scale

Optimize Spark DAGs by minimizing wide transformations like groupBy and join to reduce shuffling.
Use broadcast joins for small lookup tables to avoid expensive shuffle operations.
Configure dynamic allocation and speculative execution in Spark to handle straggler tasks.
Implement data skew mitigation using salting techniques on high-cardinality keys.
Select appropriate file formats (Parquet, ORC) with compression (Snappy, Zstandard) for storage efficiency.
Write deterministic transformations to ensure reproducibility across job runs.
Use delta lake upserts with merge operations to handle slowly changing dimensions.
Validate data quality during transformation using embedded assertions and fail-fast logic.

Module 4: Data Quality and Observability

Define and monitor freshness SLAs for critical datasets using timestamp checks in orchestration tools.
Instrument data pipelines with structured logging to enable root cause analysis during failures.
Implement anomaly detection on row counts and null rates using statistical baselines.
Deploy data profiling jobs to track schema drift and unexpected data type changes.
Set up alerting on data quality rules (e.g., PII leakage, invalid enums) using Great Expectations or custom checks.
Integrate lineage tracking with tools like OpenLineage to map field-level data flow.
Use synthetic test data to validate pipeline behavior during deployment rollouts.
Log data quality metrics to a central warehouse for audit and trend analysis.

Module 5: Security and Access Governance

Implement column- and row-level security in query engines (e.g., Presto, Snowflake) using Ranger or native policies.
Manage secrets for data sources using HashiCorp Vault instead of hardcoding in pipeline configs.
Enforce encryption at rest and in transit for all data movement and storage components.
Assign least-privilege IAM roles to data processing jobs to limit blast radius of compromise.
Conduct periodic access reviews for data lake roles and revoke stale permissions.
Mask sensitive fields (e.g., SSN, email) in non-production environments using deterministic tokenization.
Log all data access attempts for audit purposes, especially for PII-containing tables.
Integrate data classification tools to auto-tag datasets based on content (e.g., PCI, PHI).

Module 6: Orchestration and Workflow Management

Model dependencies in Airflow DAGs using explicit task relationships and avoid implicit assumptions.
Implement sensor patterns to trigger pipelines on external events (e.g., file arrival, API callback).
Use backfill strategies with caution to avoid overwhelming downstream systems with historical data.
Parameterize workflows to support multi-environment deployment (dev, staging, prod).
Set task retries with exponential backoff and circuit breaker logic to prevent cascading failures.
Monitor DAG run durations and alert on deviations from historical baselines.
Version control DAG definitions and couple them with CI/CD pipelines for deployment safety.
Use sub-DAGs or TaskGroups to modularize complex workflows and improve maintainability.

Module 7: Performance Tuning and Cost Optimization

Right-size cluster resources based on historical utilization metrics from monitoring tools.
Use spot instances for fault-tolerant batch workloads to reduce compute costs by up to 70%.
Precompute aggregations for frequently accessed metrics to reduce query latency.
Implement data compaction jobs to merge small files and improve scan efficiency.
Cache frequently accessed datasets in memory or SSD-backed storage layers.
Analyze query execution plans to identify expensive operations (e.g., full table scans).
Set up auto-termination for idle clusters to prevent unnecessary billing.
Compare cost per query across engines (e.g., Athena vs. Redshift) for workload-specific decisions.

Module 8: Real-Time Processing and Stream Analytics

Choose windowing strategies (tumbling, sliding, session) based on business use case semantics.
Handle late-arriving data in Flink or Spark Structured Streaming using watermarking and allowed lateness.
Implement exactly-once processing semantics using two-phase commit or transactional sinks.
Scale Kafka consumer groups dynamically based on lag and message throughput.
Use stateful processing for sessionization or running aggregates with TTL management.
Deploy stream processing jobs in high-availability mode with checkpointing enabled.
Monitor end-to-end latency from source to sink to ensure SLA compliance.
Test stream applications with replayable topics to validate logic under production data.

Module 9: Data Lifecycle and Retention Management

Define retention policies per dataset based on regulatory requirements and business value.
Automate data archival using lifecycle rules in cloud storage (e.g., S3 Glacier transitions).
Implement soft-delete patterns with tombstone markers before physical deletion.
Coordinate deletion across replicated systems to maintain consistency during purge operations.
Log all data deletion activities for audit and compliance verification.
Validate referential integrity before dropping partitioned datasets with dependencies.
Use metadata tagging to classify data by sensitivity and retention class.
Conduct quarterly reviews of retention policies to align with evolving legal standards.