Data Quality Fundamentals - Helion

ebook

Autor: Barr Moses, Lior Gavish, Molly Vorwerck
ISBN: 9781098111991
stron: 312, Format: ebook
Data wydania: 2022-09-01
Księgarnia: Helion

Cena książki: 186,15 zł (poprzednio: 216,45 zł)
Oszczędzasz: 14% (-30,30 zł)

Osoby, które kupiły tę książkę, wybierały także »

Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you.

Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies.

Build more trustworthy and reliable data pipelines
Write scripts to make data checks and identify broken pipelines with data observability
Learn how to set and maintain data SLAs, SLIs, and SLOs
Develop and lead data quality initiatives at your company
Learn how to treat data services and systems with the diligence of production software
Automate data lineage graphs across your data ecosystem
Build anomaly detectors for your critical data assets

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Spis treści

Data Quality Fundamentals eBook -- spis treści

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