Description

This curriculum spans the technical and operational rigor of a multi-workshop program, addressing the same data architecture, pipeline engineering, and production governance challenges encountered in enterprise-scale data platform deployments.

Module 1: Defining Data Requirements and System Scope

Selecting data sources based on business SLAs, including real-time feeds versus batch archives from enterprise data lakes.
Negotiating data access rights with data stewards across departments to ensure compliance with internal data governance policies.
Determining data freshness requirements for downstream analytics and deciding between micro-batch and streaming ingestion.
Documenting data lineage needs early to support auditability in regulated industries such as finance or healthcare.
Choosing between schema-on-write and schema-on-read based on anticipated query patterns and data volatility.
Estimating storage growth over 12–24 months to inform infrastructure procurement and cloud cost modeling.
Aligning data retention policies with legal discovery obligations and GDPR/CCPA compliance requirements.
Defining key performance indicators for data pipeline reliability, including uptime and latency thresholds.

Module 2: Architecting Scalable Data Ingestion Pipelines

Configuring Kafka topics with appropriate partition counts to balance throughput and parallelism for downstream consumers.
Implementing idempotent consumers to handle message duplication in high-availability streaming topologies.
Choosing between change data capture (CDC) tools like Debezium and batch extract-load processes for database synchronization.
Securing data in transit using TLS and managing certificate rotation for ingestion endpoints across hybrid environments.
Designing dead-letter queues for failed records and establishing alerting thresholds for backlog accumulation.
Throttling ingestion rates during peak loads to prevent downstream system overloads in resource-constrained clusters.
Validating data structure at ingestion using schema registries to enforce Avro or Protobuf contracts.
Monitoring end-to-end latency from source to sink using distributed tracing tools like OpenTelemetry.

Module 3: Storage Layer Design for Performance and Cost

Selecting file formats (Parquet, ORC, Avro) based on query patterns, compression efficiency, and schema evolution needs.
Partitioning data by time or entity keys to optimize query pruning in distributed SQL engines like Spark SQL or Presto.
Implementing tiered storage policies to move cold data from hot SSDs to object storage like S3 or Azure Blob.
Configuring replication factors in HDFS or object storage to balance fault tolerance and storage overhead.
Enabling columnar compression and dictionary encoding to reduce I/O in analytical workloads.
Managing metadata consistency in distributed file systems using tools like Apache Hudi or Delta Lake.
Designing data layout strategies to minimize small file problems in distributed processing frameworks.
Encrypting data at rest using KMS-integrated solutions and managing key rotation schedules.

Module 4: Distributed Processing Frameworks and Workload Optimization

Tuning Spark executor memory and core allocation to avoid out-of-memory errors and underutilization.
Choosing between Spark Structured Streaming and Flink based on exactly-once processing guarantees and state management needs.
Optimizing shuffle operations by adjusting partition counts and enabling shuffle service reuse.
Implementing broadcast joins for small lookup tables to reduce network transfer in large-scale joins.
Scheduling resource-intensive jobs during off-peak hours to avoid contention in shared clusters.
Using dynamic allocation to scale executors up and down based on workload demand.
Profiling job performance using Spark UI metrics to identify bottlenecks in serialization or garbage collection.
Containerizing processing jobs with Kubernetes for consistent deployment across environments.

Module 5: Data Quality, Validation, and Monitoring

Embedding data validation rules in ingestion pipelines using Great Expectations or custom assertions.
Setting up automated alerts for data drift, such as unexpected null rates or value distribution shifts.
Implementing reconciliation jobs to compare source and target record counts after ETL execution.
Versioning data quality rules to track changes and support rollback during pipeline updates.
Logging data quality metrics to a centralized observability platform for trend analysis.
Handling missing or malformed records by routing to quarantine zones with human review workflows.
Defining SLAs for data validation execution time to avoid pipeline delays.
Integrating data quality checks into CI/CD pipelines for automated testing of new data transformations.

Module 6: Security, Access Control, and Compliance

Implementing row- and column-level security in SQL engines using Apache Ranger or custom UDFs.
Managing service account permissions for ETL jobs to follow the principle of least privilege.
Auditing data access patterns using logging agents and feeding logs into SIEM systems.
Masking sensitive fields like PII in non-production environments using deterministic tokenization.
Enforcing data classification tags and blocking untagged data from entering governed zones.
Integrating with enterprise identity providers via SAML or OIDC for centralized user authentication.
Conducting periodic access reviews to deactivate orphaned or overprivileged accounts.
Encrypting configuration files containing credentials using tools like Hashicorp Vault.

Module 7: Orchestration and Pipeline Lifecycle Management

Defining dependency graphs in Airflow DAGs with appropriate retry policies and timeout thresholds.
Parameterizing pipelines to support multiple environments (dev, staging, prod) without code duplication.
Version-controlling pipeline definitions using Git and enforcing pull request reviews for production changes.
Managing backfills for historical data processing while avoiding resource contention with live pipelines.
Implementing health checks for external dependencies before triggering dependent workflows.
Scheduling pipeline runs based on data availability rather than fixed intervals using sensors.
Archiving completed workflow logs to meet compliance retention policies.
Using pipeline testing frameworks to validate data output before promoting to production.

Module 8: Real-Time Analytics and Serving Layer Integration

Choosing between OLAP databases (Druid, ClickHouse) and materialized views in data warehouses for low-latency queries.
Designing pre-aggregated rollups to support fast dashboard rendering in BI tools.
Integrating streaming results into feature stores for real-time machine learning inference.
Implementing cache invalidation strategies when underlying data is updated in near real time.
Load-testing serving layers to determine query throughput and concurrency limits.
Exposing data via REST or GraphQL APIs with rate limiting and authentication.
Synchronizing state between transactional databases and analytical stores using CDC pipelines.
Monitoring query performance and optimizing indexes or partitioning in serving databases.

Module 9: Production Operations and Cost Governance

Setting up centralized logging and monitoring for all data services using Prometheus and Grafana.
Creating runbooks for common failure scenarios, such as pipeline backpressure or cluster outages.
Allocating cloud cost attribution tags by team, project, and workload for chargeback reporting.
Automating cluster scaling policies based on historical utilization patterns.
Conducting quarterly cost reviews to decommission unused clusters or idle resources.
Implementing canary deployments for pipeline updates to detect regressions early.
Managing software dependencies and patching schedules for open-source components.
Establishing incident response procedures for data corruption or compliance breaches.