Data Quality Fundamentals

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• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Why Data Quality Deserves AttentionNow
  • What Is Data Quality?
  • Framing the Current Moment
    • Understanding the Rise of Data Downtime
      • Migration to the cloud
      • More data sources
      • Increasingly complex data pipelines
      • More specialized data teams
      • Decentralized data teams
    • Other Industry Trends Contributing to the Current Moment
      • Data mesh
      • Streaming data
      • Rise of the data lakehouse
  • Summary
• 2. Assembling the Building Blocks of a Reliable Data System
  • Understanding the Difference Between Operational and Analytical Data
  • What Makes Them Different?
  • Data Warehouses Versus Data Lakes
    • Data Warehouses: Table Types at the Schema Level
    • Data Lakes: Manipulations at the File Level
    • What About the Data Lakehouse?
    • Syncing Data Between Warehouses and Lakes
  • Collecting Data Quality Metrics
    • What Are Data Quality Metrics?
    • How to Pull Data Quality Metrics
      • Scalability
      • Monitoring across other parts of your stack
      • Example: Pulling data quality metrics from Snowflake
        • Step 1: Map your inventory
        • Step 2: Monitor for data freshness and volume
        • Step 3: Build your query history
        • Step 4: Health check
    • Using Query Logs to Understand Data Quality in the Warehouse
    • Using Query Logs to Understand Data Quality in the Lake
  • Designing a Data Catalog
  • Building a Data Catalog
  • Summary
• 3. Collecting, Cleaning, Transforming, and Testing Data
  • Collecting Data
    • Application Log Data
    • API Responses
    • Sensor Data
  • Cleaning Data
  • Batch Versus Stream Processing
  • Data Quality for Stream Processing
    • AWS Kinesis
    • Apache Kafka
  • Normalizing Data
    • Handling Heterogeneous Data Sources
      • Warehouse data versus lake data: heterogeneity edition
    • Schema Checking and Type Coercion
    • Syntactic Versus Semantic Ambiguity in Data
    • Managing Operational Data Transformations Across AWS Kinesis and Apache Kafka
      • AWS Kinesis
      • Apache Kafka
  • Running Analytical Data Transformations
    • Ensuring Data Quality During ETL
    • Ensuring Data Quality During Transformation
  • Alerting and Testing
    • dbt Unit Testing
    • Great Expectations Unit Testing
    • Deequ Unit Testing
  • Managing Data Quality with Apache Airflow
    • Scheduler SLAs
    • Installing Circuit Breakers with Apache Airflow
    • SQL Check Operators
  • Summary
• 4. Monitoring and Anomaly Detection for Your Data Pipelines
  • Knowing Your Known Unknowns and Unknown Unknowns
  • Building an Anomaly Detection Algorithm
    • Monitoring for Freshness
    • Understanding Distribution
  • Building Monitors for Schema and Lineage
    • Anomaly Detection for Schema Changes and Lineage
    • Visualizing Lineage
    • Investigating a Data Anomaly
  • Scaling Anomaly Detection with Python and Machine Learning
    • Improving Data Monitoring Alerting with Machine Learning
    • Accounting for False Positives and False Negatives
    • Improving Precision and Recall
    • Detecting Freshness Incidents with Data Monitoring
    • F-Scores
    • Does Model Accuracy Matter?
  • Beyond the Surface: Other Useful Anomaly Detection Approaches
  • Designing Data Quality Monitors for Warehouses Versus Lakes
  • Summary
• 5. Architecting for Data Reliability
  • Measuring and Maintaining High Data Reliability at Ingestion
  • Measuring and Maintaining Data Quality in the Pipeline
  • Understanding Data Quality Downstream
  • Building Your Data Platform
    • Data Ingestion
    • Data Storage and Processing
    • Data Transformation and Modeling
    • Business Intelligence and Analytics
    • Data Discovery and Governance
  • Developing Trust in Your Data
    • Data Observability
    • Measuring the ROI on Data Quality
      • Calculating the cost of data downtime
      • Updating your data downtime cost to reflect external factors
    • How to Set SLAs, SLOs, and SLIs for Your Data
      • Step 1: Defining data reliability with SLAs
      • Step 2: Measuring data reliability with SLIs
      • Step 3: Tracking data reliability with SLOs
  • Case Study: Blinkist
  • Summary
• 6. Fixing Data Quality Issues at Scale
  • Fixing Quality Issues in Software Development
  • Data Incident Management
    • Incident Detection
    • Response
    • Root Cause Analysis
      • Step 1: Look at your lineage
      • Step 2: Look at the code
      • Step 3: Look at your data
      • Step 4: Look at your operational environment
      • Step 5: Leverage your peers
    • Resolution
    • Blameless Postmortem
  • Incident Response and Mitigation
    • Establishing a Routine of Incident Management
      • Step 1: Route notifications to the appropriate team members
      • Step 2: Assess the severity of the incident
      • Step 3: Communicate status updates as often as possible
      • Step 4: Define and align on data SLOs and SLIs to prevent future incidents and downtime
    • Why Data Incident Commanders Matter
  • Case Study: Data Incident Management at PagerDuty
    • The DataOps Landscape at PagerDuty
    • Data Challenges at PagerDuty
    • Using DevOps Best Practices to Scale Data Incident Management
      • Best practice #1: Ensure your incident management covers the entire data life cycle
      • Best practice #2: Incident management should include noise suppression
      • Best practice #3: Group data assets and incidents to intelligently route alerts
  • Summary
• 7. Building End-to-End Lineage
  • Building End-to-End Field-Level Lineage for Modern Data Systems
    • Basic Lineage Requirements
    • Data Lineage Design
    • Parsing the Data
    • Building the User Interface
  • Case Study: Architecting for Data Reliability at Fox
    • Exercise Controlled Freedom When Dealing with Stakeholders
    • Invest in a Decentralized Data Team
    • Avoid Shiny New Toys in Favor of Problem-Solving Tech
    • To Make Analytics Self-Serve, Invest in Data Trust
  • Summary
• 8. Democratizing Data Quality
  • Treating Your Data Like a Product
  • Perspectives on Treating Data Like a Product
    • Convoy Case Study: Data as a Service or Output
    • Uber Case Study: The Rise of the Data Product Manager
    • Applying the Data-as-a-Product Approach
      • Gain stakeholder alignment earlyand often
      • Apply a product management mindset
      • Invest in self-serve tooling
      • Prioritize data quality and reliability
      • Find the right team structure for your data organization
  • Building Trust in Your Data Platform
    • Align Your Products Goals with the Goals of the Business
    • Gain Feedback and Buy-in from the Right Stakeholders
    • Prioritize Long-Term Growth and Sustainability Versus Short-Term Gains
    • Sign Off on Baseline Metrics for Your Data and How You Measure Them
    • Know When to Build Versus Buy
  • Assigning Ownership for Data Quality
    • Chief Data Officer
    • Business Intelligence Analyst
    • Analytics Engineer
    • Data Scientist
    • Data Governance Lead
    • Data Engineer
    • Data Product Manager
    • Who Is Responsible for Data Reliability?
  • Creating Accountability for Data Quality
  • Balancing Data Accessibility with Trust
  • Certifying Your Data
  • Seven Steps to Implementing a Data Certification Program
    • Step 1: Build out your data observability capabilities
    • Step 2: Determine your data owners
    • Step 3: Understand what good data looks like
    • Step 4: Set clear SLAs, SLOs, and SLIs for your most important data sets
    • Step 5: Develop your communication and incident management processes
    • Step 6: Determine a mechanism to tag the data as certified
    • Step 7: Train your data team and downstream consumers
  • Case Study: Toasts Journey to Finding the Right Structure for Their Data Team
    • In the Beginning: When a Small Team Struggles to Meet Data Demands
    • Supporting Hypergrowth as a Decentralized Data Operation
    • Regrouping, Recentralizing, and Refocusing on Data Trust
    • Considerations When Scaling Your Data Team
      • Hire data generalists, not specialistswith one exception
      • Prioritize building a diverse data team from day one
      • Overcommunication is key to change management
      • Dont overvalue a single source of truth
  • Increasing Data Literacy
  • Prioritizing Data Governance and Compliance
    • Prioritizing a Data Catalog
      • In-house
      • Third-party
      • Open source
    • Beyond Catalogs: Enforcing Data Governance
  • Building a Data Quality Strategy
    • Make Leadership Accountable for Data Quality
    • Set Data Quality KPIs
    • Spearhead a Data Governance Program
    • Automate Your Lineage and Data Governance Tooling
    • Create a Communications Plan
  • Summary
• 9. Data Quality in the Real World: Conversations and Case Studies
  • Building a Data Mesh for Greater Data Quality
    • Domain-Oriented Data Owners and Pipelines
    • Self-Serve Functionality
    • Interoperability and Standardization of Communications
  • Why Implement a Data Mesh?
    • To Mesh or Not to Mesh? That Is the Question
    • Calculating Your Data Mesh Score
  • A Conversation with Zhamak Dehghani: The Role of Data Quality Across the Data Mesh
    • Can You Build a Data Mesh from a Single Solution?
    • Is Data Mesh Another Word for Data Virtualization?
    • Does Each Data Product Team Manage Their Own Separate Data Stores?
    • Is a Self-Serve Data Platform the Same Thing as a Decentralized Data Mesh?
    • Is the Data Mesh Right for All Data Teams?
    • Does One Person on Your Team Own the Data Mesh?
    • Does the Data Mesh Cause Friction Between Data Engineers and Data Analysts?
  • Case Study: Kolibri Games Data Stack Journey
    • First Data Needs
    • Pursuing Performance Marketing
    • 2018: Professionalize and Centralize
    • Getting Data-Oriented
    • Getting Data-Driven
    • Building a Data Mesh
    • Five Key Takeaways from a Five-Year Data Evolution
  • Making Metadata Work for the Business
  • Unlocking the Value of Metadata with Data Discovery
    • Data Warehouse and Lake Considerations
    • Data Catalogs Can Drown in a Data Lakeor Even a Data Mesh
    • Moving from Traditional Data Catalogs to Modern Data Discovery
  • Deciding When to Get Started with Data Quality at Your Company
    • Youve Recently Migrated to the Cloud
    • Your Data Stack Is Scaling with More Data Sources, More Tables, and More Complexity
    • Your Data Team Is Growing
    • Your Team Is Spending at Least 30% of Their Time Firefighting Data Quality Issues
    • Your Team Has More Data Consumers Than They Did One Year Ago
    • Your Company Is Moving to a Self-Service Analytics Model
    • Data Is a Key Part of the Customer Value Proposition
    • Data Quality Starts with Trust
  • Summary
• 10. Pioneering the Future of Reliable Data Systems
  • Be Proactive, Not Reactive
  • Predictions for the Future of Data Quality and Reliability
    • Data Warehouses and Lakes Will Merge
    • Emergence of New Roles on the Data Team
    • Rise of Automation
    • More Distributed Environments and the Rise of Data Domains
  • So Where Do We Go from Here?
• Index

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