Picture this: Your marketing team needs campaign performance data from Sprinklr Marketing, your analytics team requires user behavior insights from Adobe Analytics and your security team monitors authentication activities from Auth0. To make informed strategic decisions, leadership needs a complete view that combines customer behavior patterns, campaign effectiveness, security metrics and operational performance across all these platforms. Each system operates in isolation, creating data silos that force decision-makers to wait days for unified insights while teams manually reconcile inconsistent reports.

This scenario isn't uncommon in enterprise environments where business growth often occurs faster than the infrastructure needed to provide unified data insights. The result? Critical business decisions are delayed by data availability, operational inefficiencies from manual processes and frustrated stakeholders lacking the unified view they need to drive results.

The solution lies in building a unified data lake architecture that can ingest data from multiple sources, standardize processing across different data formats and provide a single source of truth for enterprise analytics. This post shares the architectural decisions we took here at Groundfog, implementation patterns and lessons learned from building such a system that reduced time-to-insight from days to minutes while maintaining data quality and compliance requirements.

Taking a more technical lens, we'll explore how event-driven architecture, source-specific processing patterns and shared infrastructure components can transform fragmented data ecosystems into cohesive, scalable platforms that turn fragmented data into meaningful and actionable insights, empowering both technical teams and business stakeholders alike.

The Multi-Source Challenge

Business Context

Data silos represent one of the most significant barriers to effective decision-making in modern enterprises. When data is scattered across disconnected systems, organizations face a perfect storm of operational and strategic challenges.

The cost of these silos extends far beyond technical inconvenience. Leaders often wait days for consolidated insights, forcing them to make critical business calls based on partial or outdated data. Teams spend countless hours manually reconciling data across systems, introducing human error and consuming valuable resources that could be better allocated to other day-to-day tasks.

Regulatory compliance adds another layer of complexity. GDPR requirements demand comprehensive data lineage and the ability to quickly locate and manage personal information across all systems. When data is scattered across multiple platforms with different access patterns and retention policies, maintaining compliance becomes a significant operational burden.

Scalability issues compound these challenges. Point-to-point integrations between systems create a web of dependencies that becomes increasingly difficult to manage as the organization grows. For example, when Adobe Analytics changes its export format, it might break the custom ETL pipeline feeding the marketing dashboard, which in turn affects the executive reporting system that combines Adobe data with Sprinklr metrics. Each new data source requires custom integration work and changes to existing systems can have cascading effects across the entire data ecosystem.

Technical Challenges

The technical landscape of multi-source data integration presents its own set of complex challenges. Different data sources produce vastly different formats: In our case, JSON from Adlytics, TSV files from Adobe Analytics, DynamoDB exports in JSON and proprietary formats and OpenSearch dumps with nested structures. Each format requires specialized processing logic while maintaining consistency in the final output.

Ingestion schedules vary across sources, creating an orchestration challenge. Real-time events from user interactions must be processed immediately, while some systems provide hourly data exports, others deliver daily batches and certain sources only support nightly bulk exports. Coordinating these different cadences while maintaining data freshness and consistency requires sophisticated scheduling and processing logic.

Data quality and consistency across sources present ongoing challenges. Schema evolution occurs independently across systems with files changing structure, API responses evolving over time and log formats being updated. The data lake architecture must handle these changes gracefully while maintaining backward compatibility and data integrity.

Architecture Design Principles

Building a successful multi-source data lake requires adherence to fundamental architectural principles that ensure scalability, maintainability and operational excellence.

Separation of Concerns forms the foundation of our approach. The architecture is structured into ingestion, processing and consumption layers. Ingestion focuses solely on reliably capturing data from source systems, processing handles transformation and standardisation and consumption provides optimised access patterns for different use cases. This separation allows teams to modify components independently without affecting the entire system.

Event-Driven Architecture enables real-time responsiveness and loose coupling between components. S3 events trigger immediate processing when new data arrives, while EventBridge schedules coordinate batch operations. This approach ensures that data becomes available for analysis as quickly as possible while maintaining system reliability and scalability.

Source-Specific Processing acknowledges that different data sources have unique characteristics and requirements. Rather than forcing all sources through a generic processing pipeline, the architecture provides tailored manifest generation and processing logic for each source while maintaining standardised output formats. A manifest is essentially a metadata file that describes the data's structure, location and processing requirements - we'll explore this concept in detail later. This approach maintains the integrity and accuracy of the data while enabling unified consumption patterns.

Shared Infrastructure promotes efficiency and consistency across all data sources. Common S3 buckets, IAM roles, monitoring dashboards and alerting mechanisms reduce operational overhead while ensuring consistent security and observability practices. Teams can focus on source-specific logic rather than rebuilding infrastructure components.

Infrastructure as Code ensures consistent, repeatable deployments across environments. Terraform modules encapsulate common patterns and enable rapid onboarding of new data sources. This approach reduces deployment risks, improves documentation, and enables teams to quickly replicate successful patterns.

Implementation Deep Dive

Ingestion Layer Architecture

The ingestion layer serves as the critical interface between the different data sources and the unified data lake. Shared Infrastructure forms the backbone of this layer, with a centralised S3 bucket organized using logical prefixes that reflect data source characteristics. The `adlytics/data/`, `sprinklr/data/`, and `auth0/logs/` prefixes provide clear organizational structure while enabling source-specific processing logic and access control.

Source-Specific Lambda Functions handle the unique requirements of each data source without compromising the overall architecture's consistency. Each Lambda function encapsulates the processing logic required for its specific source, from handling Sprinklr's API responses to processing Adobe Analytics' complex TSV structures. This approach ensures that changes to one source don't impact others.

Event-Driven Triggers provide the orchestration mechanism that determines how and when data processing occurs. S3 notifications trigger immediate processing for sources like Adobe Analytics events, while EventBridge schedules coordinate batch processing for sources like dynamoDB exports that provide periodic data dumps. This hybrid approach optimises for both latency and reliability based on each source's characteristics.

Data Source Integration Patterns

Data sources can be categorised into two fundamental patterns: push-based sources that automatically deliver data to designated locations (like Adobe Analytics exports) and pull-based sources that require active retrieval from storage systems (like OpenSearch dumps). Understanding this distinction is crucial for designing appropriate integration patterns.

Real-time Event Processing demonstrates the architecture's ability to handle high-velocity data streams. When Adobe Analytics events arrive in S3, notifications trigger Lambda functions that immediately generate manifests and initiate processing. This pattern achieves sub-minute data availability and operational monitoring. The processing pipeline validates data integrity, enriches metadata, and ensures compatibility before making data available for consumption.

Scheduled Batch Processing addresses the needs of sources that provide periodic data exports. Some integrations use EventBridge cron triggers to initiate Step Functions workflows that coordinate multi-stage processing. These workflows validate export completeness before generating manifests, ensuring data quality and preventing incomplete datasets from entering the lake. The approach provides reliable processing of large datasets while maintaining predictable resource utilisation.

API-Based Ingestion handles sources that require active data retrieval rather than passive receipt. These integrations use scheduled Lambda functions to make API calls, manage authentication tokens and store retrieved data in S3. This pattern includes sophisticated error handling for rate limiting, token refresh and credential management, ensuring reliable data acquisition from external platforms.

Data Processing standardisation

Manifest Generation ensures that all data sources produce compatible metadata regardless of their original format or structure. Each processing pipeline generates standardised manifests that include file locations, record counts, data schemas and processing timestamps. This standardisation enables downstream systems to consume data from any source through consistent patterns and expectations.

Metadata Enrichment adds valuable operational information to each dataset. File sizes, MD5 checksums and record counts provide data quality validation, while processing timestamps and source identifiers enable comprehensive data lineage tracking. This enrichment supports both operational monitoring and compliance requirements without requiring changes to source systems.

Error Handling implements consistent patterns across all processing pipelines. Dead letter queues capture failed processing attempts, CloudWatch logs provide detailed error information and SNS notifications alert operations teams to issues requiring attention. This standardised approach to error handling ensures that problems are quickly identified and resolved regardless of which data source is affected.

Governance and Security

Access Control and Permissions

Lake Formation provides fine-grained access control that enables secure data sharing across teams while maintaining appropriate boundaries. Data engineers can access raw ingestion data for troubleshooting, analysts can query processed datasets for insights and business users can consume aggregated reports without exposure to sensitive underlying data. This layered approach to permissions ensures that each user has access to exactly the data they need for their role.

Row-level and column-level security policies enforce data privacy requirements automatically. Personal identifiable information from logs is automatically anonymized based on user roles, while sensitive campaign data is restricted to authorized marketing personnel. These policies are enforced at the processing layer, ensuring consistent security regardless of the consumption method.

Data Privacy and Compliance

Automated Anonymization addresses GDPR and other privacy requirements without manual intervention. Files undergo automated anonymization processes that remove or hash personal identifiers while preserving analytical value. This automation ensures consistent compliance while reducing operational overhead and the risk of human error.

Audit Trails provide comprehensive tracking of data access and processing activities. Every query, transformation and export is logged with user identification, timestamps and data scope information. This detailed logging supports compliance audits while enabling operational teams to understand data usage patterns and optimize performance accordingly.

Data Lineage Tracking maintains complete visibility into data flow from source systems through processing pipelines to final consumption. This lineage information supports impact analysis when source systems change, enables root cause analysis when data quality issues arise and provides the necessary documentation for regulatory reviews and compliance validation.

Monitoring and Observability

Unified CloudWatch Dashboard provides operational visibility across all data sources and processing pipelines. Real-time metrics track ingestion rates, processing latencies, error rates and data quality indicators. This centralized monitoring enables operations teams to quickly identify and respond to issues regardless of which component is affected.

Custom CloudWatch metrics track business-relevant indicators such as data freshness, completeness and accuracy. These metrics enable early detection and proactive resolution of issues that could impact business decisions, such as delayed data from critical sources or quality degradation in key datasets.

Lessons Learned and Best Practices

What Worked Well

Event-Driven Architecture delivered significant improvements in data availability and system responsiveness. Processing latency dropped from days to minutes across all data sources, enabling near real-time analytics and operational monitoring. The loose coupling between components improved system reliability and simplified iterative enhancements without disrupting the entire pipeline.

Terraform Modules accelerated new data source onboarding from weeks to days. Standardized infrastructure patterns reduced the complexity of adding new sources while ensuring consistent security, monitoring and operational practices. Teams could focus on source-specific processing logic rather than rebuilding infrastructure components from scratch.

Centralized Monitoring provided operational visibility across the entire data ecosystem. Unified dashboards enabled operations teams to quickly identify issues, understand system performance and optimize resource allocation. The consistent monitoring approach reduced mean time to resolution for operational issues while improving overall system reliability.

Source-Specific Processing maintained data quality while enabling standardisation across diverse sources. Rather than forcing all data through generic processing pipelines, tailored processing logic preserved the unique characteristics of each source while producing standardized outputs. This approach reduced data quality issues and improved the reliability of downstream analytics.

Challenges Overcome

Schema Evolution required complex handling of changes in source system data structures. Lookup files changed format unexpectedly, requiring processing pipelines to handle both old and new structures gracefully. The solution involved versioned processing logic and comprehensive testing to ensure backward compatibility while supporting new features.

Rate Limiting from external APIs required careful orchestration and retry logic. Rate limits made it necessary to add intelligent request spacing and backoff strategies. The implementation included circuit breakers to prevent cascading failures and comprehensive monitoring to track API usage patterns and optimize request strategies.

Error Recovery demanded robust retry mechanisms and dead letter queue processing. Network failures, temporary service outages and data quality issues required complex error handling that could distinguish between transient and permanent failures. The solution included automated retry logic, manual intervention workflows and alerting to ensure no data was lost.

Cost Optimization balanced real-time processing requirements with operational efficiency. Initial implementations prioritized speed over cost, leading to higher than expected AWS bills. Optimisation efforts included right-sizing Lambda functions, implementing intelligent scheduling for batch processes and using S3 lifecycle policies to manage storage costs without compromising data availability.

Key Success Factors

Start with Shared Infrastructure before implementing source-specific processing logic. Building common S3 buckets, IAM roles and monitoring infrastructure first provided a solid foundation that accelerated subsequent development. This approach also ensured consistency across all data sources and reduced operational complexity.

Invest in Comprehensive Monitoring from the beginning rather than adding it later. Early investment in CloudWatch dashboards, custom metrics and alerting mechanisms paid off throughout the project lifecycle. Comprehensive monitoring enabled proactive issue identification and provided the visibility needed to optimize system performance.

Design for Failure with proper error handling and alerting mechanisms. Assuming that components will fail and designing recovery mechanisms accordingly improved overall system reliability. Dead letter queues, retry logic and error logging ensured that temporary failures didn't result in data loss or extended outages.

Maintain Clear Separation between ingestion and processing logic to enable independent evolution of components. This separation, combined with standardized manifests that provide a consistent interface between layers, allowed teams to modify processing logic without affecting data ingestion and vice versa. The standardized manifest format acts as a contract between ingestion and processing systems, ensuring compatibility even as individual components evolve. The approach also enabled different team members to work on different components simultaneously without coordination overhead.

Measurable Business Impact

Operational Improvements

Time to Insight transformation represents the most significant business impact of the unified data lake architecture. Decision-makers who previously waited days for unified reports now access insights across all data sources as soon as they become available. Marketing teams can adjust campaigns based on performance data and executives can make strategic decisions with current information rather than outdated snapshots.

Operational Efficiency improvements eliminated redundant manual data reconciliation and reporting tasks that previously consumed significant team resources. The reduction in manual effort freed analysts to focus on insight generation rather than data preparation. Teams that previously spent hours each day reconciling reports across systems now receive consistent, automatically updated dashboards that deliver unified, reliable views of business performance

Cost Savings emerged from eliminating duplicate infrastructure across teams. Previously, each team maintained separate data processing pipelines, storage systems and monitoring tools. The unified architecture reduced infrastructure costs while improving capabilities, demonstrating that consolidation can deliver both economic and operational benefits.

Scalability and Growth

Seamless Addition of New Data Sources validated the architecture's scalability principles. Adding integrations was completed in days rather than weeks, leveraging existing Terraform modules and processing patterns. This rapid onboarding capability enables the organization to quickly integrate new systems and data sources as business requirements evolve.

Elastic Resource Utilisation ensures that the system scales automatically with data volume and processing requirements. Lambda functions scale to handle peak loads without manual intervention, while S3 storage grows seamlessly with data accumulation. This elasticity eliminates capacity planning concerns while ensuring consistent performance regardless of data volume.

Compliance and Risk Management

Automated GDPR Compliance for user data eliminated manual processes that were both time-consuming and error-prone. Automated anonymization of logs ensures consistent privacy protection while maintaining analytical value. This automation reduces compliance risk while freeing teams to focus on business value rather than regulatory requirements.

Improved Data Quality through standardized processing and validation reduces the risk of business decisions based on incorrect information. Consistent manifest generation, metadata enrichment and error handling ensure that data quality issues are identified and resolved quickly rather than propagating through downstream systems.

Conclusion

Building a multi-source data lake requires careful attention to architectural principles, implementation patterns and operational practices. The journey from siloed data systems to unified analytics platforms delivers transformative business value, but success depends on making thoughtful decisions about event-driven architecture, source-specific processing and shared infrastructure components.

The key architectural decisions that drove success included embracing event-driven patterns for real-time responsiveness, implementing source-specific processing to maintain data quality and investing in shared infrastructure to ensure operational consistency. At Groundfog, we apply these principles every day, designing, building, and running scalable data platforms that turn complex, fragmented ecosystems into reliable, insight-driven systems. These decisions enabled the transformation from days of data delays to near real-time insights while maintaining data quality and compliance requirements.

The business impact extends far beyond technical improvements. Operational efficiency gains, cost savings and improved decision-making capabilities demonstrate that well-designed data architecture delivers measurable value across the organization. The ability to rapidly onboard new data sources and scale processing capabilities positions the organization for continued growth and evolution.

As data volumes continue to grow and business requirements become more sophisticated, the architectural patterns and lessons learned from this implementation provide a foundation for continued innovation. By combining proven patterns with emerging technologies, organizations can unlock new levels of analytical power and operational agility, turning data into an engine for constant improvement. These same principles continue to guide our work at Groundfog, where we help organizations transform complex, fragmented data landscapes into cohesive ecosystems that deliver real-time insights and measurable business impact.