Structuring Your Data Lake Build in Agile Sprints: A Practical Guide

Building a modern data lake is a complex endeavor that challenges traditional Agile methodologies. While Agile principles work well for software development, data platforms present unique complications that require a specialized approach to sprint planning and execution:

• Source system dependencies
• Data quality requirements
• The interconnected nature of data zones

Many data teams struggle to break down their lake development into manageable sprints while maintaining data quality and meeting security requirements. The challenge intensifies when teams must balance immediate business needs with building a sustainable architecture that can evolve over time.

This guide will show you how to structure your data lake development using Agile sprints, with specific focus on:

• Breaking down data lake zones into workable epics and features
• Creating a logical sprint structure across bronze, silver, and gold zones
• Managing complex dependencies between zones and source systems
• Developing reliable estimation approaches for data ingestion tasks

Whether you’re building a new data lake or restructuring an existing one, you’ll learn practical approaches that balance immediate delivery needs with long-term architectural goals. We’ll use real examples from production implementations while addressing critical concerns around security, monitoring, and documentation.

Let’s begin by examining how to structure your initial epics to set your project up for success.

Creating Your Data Lake Epics

Breaking down a data lake implementation into meaningful epics requires balancing architectural layers with business value delivery. We recommend our epics to align with both technical architecture and business outcomes.

Core Infrastructure Epic

Your foundation epic should focus on the infrastructure that supports all subsequent development:

Epic: Data Lake Infrastructure Foundation

Features:

• Storage Account Configuration
• Network Security Setup
• Private Endpoint Implementation
• Key Vault Integration
• Identity and Access Management

Data Zone Epics

Following the medallion architecture pattern, create distinct epics for each zone:

Epic: Bronze Zone Implementation

Features:

• Source System Connectivity Framework
• Raw Data Landing Architecture
• Data Ingestion Pipeline Templates
• Bronze Zone Monitoring
• Data Freshness Validation

Epic: Silver Zone Implementation

Features:

• Data Quality Framework
• Business Rule Engine
• Schema Standardization
• Historical Data Management
• Data Validation Framework

Epic: Gold Zone Implementation

Features:

• Business Domain Models
• Performance Optimization
• Access Layer Implementation
• Semantic Layer Development
• Self-Service Analytics Support

Epic: Platform Operations

This is a cross-cutting epic for capabilities that span all zones.

Features:

• Monitoring & Alerting Framework
• Documentation & Lineage Tracking
• Security Compliance Framework
• Cost Management
• Performance Monitoring

Epic Prioritization

When prioritizing these epics, consider:

1. Infrastructure must come first – without proper security and network setup, no data can flow.
2. Bronze zone development typically runs in parallel with infrastructure after initial setup.
3. Silver zone features should begin once you have stable bronze zone pipelines.
4. Gold zone development can start for specific domains once their silver zone processes stabilize.

Remember, the first week of any project should focus heavily on backlog definition. Take time to detail these epics and their features before diving into development. This upfront planning helps prevent security gaps and reduces technical debt.

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Defining Features and Stories

Breaking down epics into actionable features and user stories requires careful consideration of both technical requirements and business needs. Let’s explore how to create effective user stories for data lake development while maintaining clear acceptance criteria.

Infrastructure & Security Features

Infrastructure stories need clear, testable criteria. Here’s a practical example:

Feature: Private Endpoint Implementation

User Story: As a data engineer, I want secure private endpoints configured for all data services so that all data traffic remains within our virtual network.

Acceptance Criteria:

• Private endpoints configured for storage account
• Private endpoints configured for Azure Data Factory
• DNS resolution verified for all endpoints
• Network security group rules implemented
• Successful connection testing documented

Bronze Zone Features

For data ingestion, structure stories around source systems and data validation:

Feature: Salesforce Data Ingestion

User Story: As a data analyst, I want raw Salesforce customer data in the bronze zone so that I can begin data quality assessment.

Acceptance Criteria:

• API connection established with error handling
• Raw data landing in bronze container
• Data freshness monitoring implemented
• Pipeline logs configured
• Source-to-target reconciliation process in place

Remaining Work: 24 hours

Tasks:

• Configure API connection (8 hours)
• Create ingestion pipeline (8 hours)
• Implement monitoring (4 hours)
• Set up validation checks (4 hours)

Story Writing Best Practices

When creating data platform stories, keep these best practices in mind:

1. Atomic Scope – Each story should cover one specific piece of functionality.
   • Good: “Implement Salesforce customer table ingestion”
   • Poor: “Implement all Salesforce ingestion”
2. Clear Acceptance Criteria – Include:
   • Performance requirements
   • Data quality expectations
   • Monitoring requirements
   • Documentation needs
3. Task Breakdown – Break stories into discrete tasks with hour-based estimates rather than story points for clearer tracking.

Common Pitfalls to Avoid

1. Oversized Stories: Break down any story estimated over 24 hours
2. Missing Dependencies: Clearly document required source system access
3. Incomplete Acceptance Criteria: Always include monitoring and documentation requirements
4. Unclear Value Proposition: Each story should tie to business value

Based on our experience, technical leads and architects typically spend about 25% of their time on story definition and grooming. This investment pays off in clearer execution and fewer blockers during development.

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Planning Your Sprint Structure

Creating an effective sprint structure for data lake development requires careful orchestration of dependencies and parallel work streams. Let’s explore a practical approach to organizing your sprints while maintaining data quality and security.

Foundation Sprint (Sprint 0)

Your initial sprint should focus on core infrastructure and security:

Sprint 0 Goals:

• Storage account deployment
• Network security configuration
• Identity management setup
• Monitoring foundation
• Documentation framework initiation

Duration: 2 weeks
Key Deliverable: Secure, compliant infrastructure ready for data

Bronze Zone Sprints

Structure bronze zone development by source system priority:

Sprint 1-2: Primary Source Systems

Focus: High-priority operational systems

• Salesforce customer data
• ERP transaction data
• Core reference data

Expected Velocity: 2-3 source systems per sprint

Sprint 3-4: Secondary Sources

Focus: Supporting business systems

• Legacy system migration
• Batch file processing
• Additional reference data

Parallel Development Strategy

Based on our experience, employ this sprint structure:

1. Current Sprint: Active development of bronze zone pipelines.
2. Next Sprint: Silver zone work for previously ingested data.
3. Future Sprint: Gold zone development for validated data domains.

This creates a continuous flow while managing dependencies.  Here’s an example timeline:

Week 1-2 (Sprint 1)

• Bronze: Salesforce ingestion
• Infrastructure: Security refinements
• Planning: Silver zone design

Week 3-4 (Sprint 2)

• Bronze: ERP ingestion
• Silver: Salesforce data quality
• Planning: Gold zone design

Quality Gates and Checkpoints

Implement these quality gates between sprints:

1. Bronze to Silver
   • Data completeness verified
   • Raw format validated
   • Monitoring confirmed
2. Silver to Gold
   • Business rules validated
   • Data quality metrics met
   • Performance requirements achieved

Maintain a single active task per engineer while keeping upcoming work clearly defined in the backlog. This approach helps manage the complex dependencies inherent in data lake development.

Managing Dependencies and Estimation

Managing dependencies and creating accurate estimates are critical challenges in data lake development. Let’s explore practical approaches to handling these complexities while maintaining predictable delivery.

Source System Dependencies

Track and manage external dependencies systematically.

Dependency Type: Source System Access

Tracking Items:

• API authentication credentials
• Rate limiting constraints
• Data refresh schedules
• Network access requirements
• Source system SLAs

Statuses to use: New > Requested > Approved > Configured > Validated

Inter-zone Dependencies Matrix

Create a clear dependency map for each data domain.

Domain: Customer Data

Bronze Requirements:

• Source system access
• Raw storage configured
• Monitoring active

Silver Dependencies:

• Bronze pipeline stable
• Quality rules defined
• Business logic approved

Gold Dependencies:

• Silver zone validation complete
• Performance benchmarks met
• Business sign-off received

Estimation Framework

Break down estimation into concrete components.

Pipeline Development

Base Components (8-16 hours):

• Source connector: 4 hours
• Pipeline template: 2 hours
• Basic validation: 2 hours
• Monitoring setup: 2 hours

Complexity Multipliers:

• High volume data: 1.5x
• Complex transformations: 1.3x
• Custom connectors: 2x

Quality Implementation

Standard Tasks:

• Data profiling: 4 hours
• Rule implementation: 4 hours/rule
• Documentation: 2 hours
• Testing: 4 hours

Buffer Allocation

Here’s some realistic capacity planning items to consider:

• Reserve 20% capacity for unexpected issues
• Allocate 10% for documentation and knowledge transfer
• Plan 15% for technical debt and optimization

Common Estimation Pitfalls

1. Overlooking Operations Tasks – Examples include monitoring implementation, alert configuration and documentation updates.
2. Underestimating Validation – Examples include data reconciliation, quality checks and performance testing.
3. Missing Integration Time – Examples include security verification, network configuration and access management.

Encourage engineers to focus on one task at a time and move items to “blocked” status when dependencies arise rather than starting multiple tasks simultaneously.

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Practical Tips and Best Practices

Let’s conclude with essential practices that will help ensure your Agile data lake implementation succeeds. These recommendations are drawn from real project experiences and address common challenges in data platform development.

Documentation Requirements

Implement a “documentation-as-code” approach for each zone:

Bronze Zone

• Source system specifications
• Ingestion pipeline details
• Raw schema definitions
• Known data quality issues

Silver Zone

• Data quality rules
• Transformation logic
• Business rule documentation
• Schema changes and rationale

Gold Zone

• Business domain models
• Access patterns
• Performance benchmarks
• Usage guidelines

Monitoring Framework

Establish monitoring at each layer, here’s key metrics to track:

1. Pipeline Health
   • Execution success rate
   • Data freshness
   • Processing duration
2. Data Quality
   • Completeness metrics
   • Validation results
   • Business rule compliance
3. Performance
   • Query response times
   • Resource utilization
   • Cost metrics

Sprint Execution Tips

1. Daily Stand-ups
   • Focus on blocked items first
   • Track dependencies actively
   • Keep technical discussions offline
2. Sprint Reviews
   • Demo working pipelines
   • Show quality metrics
   • Present monitoring dashboards
3. Retrospectives
   • Review estimation accuracy
   • Assess dependency impacts
   • Identify process improvements

Tip: Focus on practical improvements that work for your team rather than striving for theoretical perfection.

Conclusion

Building a data lake using Agile methodologies requires thoughtful adaptation of traditional Agile practices. Success comes not from rigid adherence to framework rules, but from pragmatic application of Agile principles to data-specific challenges.

Key takeaways for your implementation:

• Start with well-defined epics that align with your data lake zones
• Create clear, atomic stories with specific acceptance criteria
• Structure sprints to manage dependencies between zones
• Use realistic, hour-based estimates for data engineering tasks
• Implement appropriate quality gates between zones

Remember that the goal isn’t perfect Agile execution, but rather establishing a workable system that helps your team define what work needs to get done for any given week so that you can forecast accurately.

Begin with these basics, adjust based on your team’s needs, and gradually refine your process. Remember to focus first on maintaining a healthy backlog and ensuring clear communication about dependencies and blockers.