Description

This curriculum spans the design and operationalization of a production-grade maintenance dashboard for big data systems, comparable in scope to a multi-sprint internal capability build undertaken by a centralized data platform team to standardize observability across distributed data products.

Module 1: Defining Operational Metrics for Big Data Systems

Select which SLIs (Service Level Indicators) to track for batch and streaming pipelines, such as end-to-end latency, data freshness, and job completion rates.
Determine thresholds for data staleness based on business SLAs, balancing alert sensitivity with operational noise.
Decide on the granularity of metric collection—per job, per data source, or per processing stage—considering storage and query performance trade-offs.
Integrate business-critical key performance indicators (KPIs) into the monitoring schema to align technical health with business outcomes.
Standardize metric naming conventions across teams to ensure consistency in dashboards and alerting.
Implement metric versioning to track changes in definitions over time, especially during pipeline refactors.
Choose between push-based (e.g., Prometheus) and pull-based (e.g., StatsD) metric collection models based on infrastructure topology.
Design metric retention policies that balance historical analysis needs with cost and performance constraints.

Module 2: Instrumenting Data Pipelines for Observability

Embed logging hooks at critical stages of ETL/ELT workflows to capture record counts, schema validation results, and transformation errors.
Configure structured logging formats (e.g., JSON) with consistent fields to enable automated parsing and alerting.
Implement distributed tracing for cross-service data flows using OpenTelemetry, especially in microservices-based architectures.
Decide whether to log full error payloads or only metadata based on PII compliance and storage cost.
Instrument idempotency checks in pipelines and expose metrics on retry behavior and duplicate handling.
Integrate custom counters for data quality rules, such as null rate per critical field or distribution skew.
Use sampling strategies for high-volume logs to reduce overhead while preserving diagnostic utility.
Ensure instrumentation libraries are compatible with existing runtime environments (e.g., Spark, Flink, Airflow).

Module 3: Building Real-Time Data Quality Monitoring

Define data quality dimensions (completeness, accuracy, consistency, timeliness) relevant to each data product.
Implement automated schema drift detection using schema registry diffs and trigger alerts on breaking changes.
Deploy statistical profiling jobs to monitor value distributions and flag anomalies using z-scores or IQR methods.
Set up referential integrity checks between fact and dimension tables in data warehouses.
Configure thresholds for acceptable null rates per column, with escalation paths for violations.
Integrate data validation frameworks (e.g., Great Expectations, Deequ) into CI/CD pipelines for data.
Balance validation overhead against pipeline performance, choosing between inline and post-hoc validation.
Design feedback loops to route data quality issues to source system owners via ticketing integrations.

Module 4: Designing the Dashboard Data Model

Model a star schema for dashboard metrics with fact tables for pipeline runs and dimension tables for services, teams, and environments.
Choose between real-time ingestion (Kafka → Druid) and batch aggregation (Spark → BigQuery) for dashboard backend storage.
Define primary keys and time partitions for metric tables to optimize query performance and cost.
Implement slowly changing dimensions (SCD Type 2) for tracking ownership and service metadata changes.
Select appropriate data types and compression settings for high-cardinality fields like job IDs and hostnames.
Design a metadata layer that maps technical components (e.g., DAGs, topics) to business domains and data stewards.
Implement data masking rules for sensitive fields in the dashboard schema based on RBAC policies.
Establish data lineage tracking at the column level to support root cause analysis in the dashboard.

Module 5: Implementing Alerting and Incident Response

Define alert severity levels (critical, warning, info) based on business impact and required response time.
Configure alert routing rules to direct notifications to on-call engineers, data stewards, or platform teams.
Implement alert deduplication and aggregation to prevent notification storms during cascading failures.
Set up alert muting schedules for known maintenance windows without disabling monitoring.
Integrate with incident management tools (e.g., PagerDuty, Opsgenie) and ensure alert context includes runbook links.
Use probabilistic alerting for metrics with high variance, applying exponential smoothing or Bayesian methods.
Enforce alert ownership by requiring runbook documentation and escalation paths during alert creation.
Conduct blameless postmortems and update alert thresholds based on incident findings.

Module 6: Access Control and Data Governance Integration

Implement row-level security in the dashboard to restrict data visibility by team, environment, or data classification.
Integrate with corporate identity providers (e.g., Okta, Azure AD) using SAML or OIDC for centralized access management.
Map dashboard permissions to existing data governance roles (e.g., data owner, steward, consumer).
Log all access to sensitive metrics and generate audit trails for compliance reporting.
Enforce attribute-based access control (ABAC) for metrics derived from regulated data (e.g., PII, financial).
Coordinate with data governance teams to align dashboard policies with data catalog classifications.
Implement just-in-time (JIT) access for elevated privileges with time-bound approvals.
Design data retention and deletion workflows in the dashboard to support GDPR and CCPA requests.

Module 7: Scaling and Performance Optimization

Partition time-series metric data by ingestion date and implement tiered storage (hot/warm/cold).
Pre-aggregate high-frequency metrics (e.g., per-minute) into hourly summaries to reduce query latency.
Implement caching strategies at multiple layers (e.g., Redis for API responses, materialized views in DB).
Optimize dashboard queries using covering indexes and avoid SELECT * patterns in visualization layers.
Conduct load testing on dashboard endpoints to identify bottlenecks under peak concurrent usage.
Use query pushdown techniques in data warehouse connectors to minimize data transfer.
Monitor backend resource utilization (CPU, memory, I/O) of the dashboard database and scale accordingly.
Implement rate limiting and query timeouts to protect backend systems from expensive dashboard queries.

Module 8: Integrating with DevOps and MLOps Workflows

Expose pipeline health metrics via API for inclusion in CI/CD quality gates (e.g., block deployment on data drift).
Trigger automated pipeline rollback based on dashboard-detected performance regressions.
Integrate model monitoring metrics (e.g., prediction drift, feature skew) into the same dashboard framework.
Link dashboard alerts to Jira tickets automatically using webhooks and predefined templates.
Sync deployment events from CI tools (e.g., Jenkins, GitLab CI) to correlate with metric changes.
Implement canary analysis for data pipelines by comparing metrics across staged rollouts.
Expose dashboard snapshots in pull request comments to show impact of data model changes.
Use infrastructure-as-code (e.g., Terraform) to provision and version dashboard resources.

Module 9: Ensuring Long-Term Maintainability and Evolution

Document data dictionary and metric definitions in a discoverable, version-controlled repository.
Establish a deprecation process for retired metrics, including notification and sunset timelines.
Conduct quarterly reviews of active alerts to remove stale or noisy rules.
Implement automated testing for dashboard queries and visualizations using synthetic datasets.
Track technical debt in the monitoring stack, such as outdated libraries or hardcoded configurations.
Standardize dashboard templates across teams to reduce onboarding time and ensure consistency.
Collect usage telemetry (e.g., most viewed dashboards, failed queries) to prioritize improvements.
Design extensibility points for new data sources, such as plug-in architectures or webhook ingestion.