Description

This curriculum spans the technical, operational, and governance dimensions of data collection, comparable in scope to a multi-phase data platform rollout or an enterprise data maturity assessment, addressing everything from pipeline architecture and compliance controls to cross-team coordination and cost management.

Module 1: Defining Strategic Data Requirements

Align data collection objectives with specific business KPIs, such as customer retention rate or supply chain cycle time, to ensure relevance and avoid scope creep.
Select data sources based on decision latency requirements—real-time telemetry vs. batch reporting—impacting infrastructure and tooling choices.
Negotiate access to legacy system logs or third-party APIs where data ownership is fragmented across departments or vendors.
Document data lineage expectations early to support auditability, especially in regulated industries like finance or healthcare.
Balance breadth versus depth in data collection: decide whether to capture comprehensive user behavior logs or focus narrowly on conversion funnel events.
Establish thresholds for data freshness, such as requiring inventory levels to be updated hourly, to maintain decision accuracy.
Define metadata standards for collected data, including source system, collection timestamp, and responsible team, to enable downstream traceability.

Module 2: Selecting and Integrating Data Sources

Assess the reliability of external data providers by reviewing historical uptime, schema stability, and contractual SLAs before integration.
Implement change data capture (CDC) for transactional databases to minimize performance impact on production systems.
Map field-level discrepancies between source systems, such as differing date formats or product categorizations, during ETL pipeline design.
Choose between API polling and webhook-based ingestion based on update frequency and provider capabilities.
Handle schema drift in streaming data sources by implementing schema registry validation with fallback handling.
Isolate high-latency sources (e.g., satellite IoT feeds) into separate processing streams to prevent pipeline blocking.
Design retry and backoff logic for intermittent source outages, particularly in cloud-based SaaS integrations.

Module 3: Designing Scalable Data Ingestion Architectures

Size message queues (e.g., Kafka topics) based on peak data volume and retention requirements to avoid data loss during processing spikes.
Partition data streams by business key (e.g., customer ID) to support parallel processing while maintaining event order within contexts.
Implement idempotent ingestion logic to handle duplicate messages from unreliable transport layers.
Select between batch and streaming ingestion based on downstream use cases—analytics dashboards versus real-time alerts.
Deploy ingestion workers in isolated environments to contain failures and prevent cascading system outages.
Monitor ingestion pipeline lag in real time to detect bottlenecks before data becomes stale.
Encrypt sensitive data payloads in transit and at rest, even within internal networks, to comply with data protection policies.

Module 4: Ensuring Data Quality and Validation

Implement field-level validation rules (e.g., email format, numeric range) at the earliest ingestion point to prevent garbage data propagation.
Define and monitor data completeness SLAs, such as 99% of daily sales records received by 2 AM.
Use statistical profiling to detect anomalies like sudden drops in event volume or unexpected value distributions.
Flag records with missing critical fields (e.g., transaction amount) for quarantine and manual review rather than automatic rejection.
Establish reconciliation processes between source systems and data warehouse counts to identify silent failures.
Version data validation rules to track changes and support rollback during debugging.
Integrate data quality dashboards into operational monitoring to ensure visibility for engineering and business teams.

Module 5: Managing Data Privacy and Compliance

Classify data elements as PII, PHI, or sensitive business information during schema design to enforce access controls.
Implement data masking or tokenization for customer identifiers in non-production environments used for development and testing.
Configure data retention policies to automatically purge records after regulatory periods (e.g., 7 years for financial audits).
Obtain documented consent for data collection in user-facing applications, particularly under GDPR or CCPA.
Conduct DPIA (Data Protection Impact Assessments) for new data collection initiatives involving high-risk processing.
Restrict cross-border data transfers by configuring regional storage and processing zones in cloud infrastructure.
Audit access logs for sensitive datasets to detect unauthorized queries or exports.

Module 6: Implementing Metadata and Data Cataloging

Automate technical metadata extraction (e.g., table size, update frequency) from source systems to reduce manual documentation.
Enforce mandatory business glossary tagging for all new datasets to ensure consistent interpretation across teams.
Link data assets to upstream sources and downstream reports to enable impact analysis during system changes.
Integrate catalog search with SQL IDEs and BI tools to increase adoption and reduce redundant data requests.
Assign data stewardship roles for critical datasets to ensure accountability for accuracy and documentation.
Track dataset usage patterns to identify underutilized assets for archival or decommissioning.
Version dataset definitions to support reproducibility of historical analyses.

Module 7: Operational Monitoring and Alerting

Define SLOs for data pipeline uptime and set thresholds for alerting (e.g., >5% failure rate in 15-minute window).
Configure alerts on data drift metrics, such as changes in categorical distribution of customer segments.
Integrate pipeline monitoring with incident response tools (e.g., PagerDuty) to ensure timely intervention.
Differentiate between transient errors (e.g., network timeout) and systemic failures (e.g., schema corruption) in alert routing.
Log detailed context with each alert, including affected tables, time range, and recent deployment history.
Conduct blameless post-mortems for major data outages to update runbooks and prevent recurrence.
Rotate credentials and API keys automatically and monitor for unauthorized access attempts.

Module 8: Governance and Cross-Functional Collaboration

Establish a data governance council with representatives from legal, IT, and business units to review new collection initiatives.
Define data ownership and stewardship models to clarify accountability for quality and access management.
Implement a change approval process for schema modifications that impact downstream consumers.
Facilitate data literacy workshops for non-technical stakeholders to improve request precision and reduce ambiguity.
Negotiate data sharing agreements between departments to resolve conflicts over access and usage rights.
Document data lifecycle policies, including archival, deletion, and disaster recovery procedures.
Conduct quarterly data inventory audits to identify shadow data systems and enforce compliance.

Module 9: Optimizing Cost and Performance

Right-size cloud storage tiers (e.g., hot vs. cold storage) based on access frequency and retrieval latency needs.
Implement data sampling strategies for exploratory analysis to reduce compute costs during prototyping.
Compress and encode data formats (e.g., Parquet with Snappy) to minimize storage footprint and query time.
Monitor query patterns to identify redundant or inefficient data requests that can be cached or pre-aggregated.
Set budget alerts and enforce cost allocation tags to prevent uncontrolled spending in shared environments.
Archive historical data to lower-cost storage while maintaining query access through federated querying.
Evaluate the total cost of ownership when selecting managed services versus self-hosted solutions.