Accelerating Data Engineering with Agent DataOps on AWS

5:38

A leading WhatsApp-based engagement platform had already built a robust AWS-native analytics pipeline for customer behavior tracking using DynamoDB, Kinesis, S3, Iceberg, and Athena. However, the platform struggled with pipeline maintenance, schema drift, cataloging complexity, and delayed feature onboarding.

To address these challenges, we introduced Agent DataOps—a layer of intelligent agents that automate operations like schema detection, ETL tuning, and pipeline governance. This reduced manual engineering effort by 70%, enabled onboarding of new message types within hours, and doubled the cadence of dashboard enhancements—all while preserving compliance, data quality, and performance.

Customer Profile

Industry: Customer Engagement / Messaging AI
Location: Italy
Company Size: ~50 employees

Business Challenges

The client had an AWS-based data analytics platform that encountered scale and agility issues due to growing user activity and complex message formats. Key pain points included:

Frequent schema drift from evolving WhatsApp data structures.
High manual effort for updating Glue jobs and Athena schemas.
Delays in dashboard feature onboarding.
Lack of metadata versioning and ETL observability.
Limited debugging capabilities and root-cause analysis.
No automation for cost-performance tuning.
Engineering bottlenecks constrained business users’ access to insights.

Technical Challenges

Complex schema evolution in JSON event payloads.
Tight coupling between DynamoDB Streams, Firehose, and Glue.
Manual updates for Glue jobs and ETL pipelines.
Non-optimized Iceberg partitioning strategies.
Poor error traceability due to missing lineage.
No automated security checks for PII, encryption, or query regressions.

Partner Solution

Solution Overview

We implemented Agent DataOps on top of the client’s AWS-based data lakehouse. This introduced autonomous agents to manage schema drift, evolve ETL jobs, update catalogs, tune performance, and run security audits.

These agents used metadata reasoning, prompt-based orchestration, and rule-learning to automate repetitive engineering tasks. The result: a self-adaptive pipeline that scales insight, not overhead.

AWS Services Used

Architecture Diagram

Implementation Details

Agent Framework: LLM-powered agents monitored metadata, logs, and schema evolution via Bedrock and Lambda-based orchestration.
Schema Drift Detection: Agents parsed incoming Firehose → S3 payloads to detect changes and update Glue table definitions.
ETL Management: Agents suggested Spark job optimizations, triggered catalog versioning, and enforced consistent definitions.
Metadata Reasoning: Agents aligned Iceberg schema evolution with Athena/QuickSight, reducing manual alignment.
Security & Compliance: PII tagging, encryption checks, and IAM audits were automated through agent logic.
CI/CD Integration: Agent output converted to config diffs and pushed through Git-based pipelines.
Observability: CloudWatch & EventBridge captured anomalies and triggered agentic rollback or repair.

Timeline

Week 1–2: Agent setup, schema drift analysis
Week 3–4: Glue job agents, metadata governance
Week 5–6: Schema sync with Athena/QuickSight
Week 7–8: Security agents, CI/CD pipeline integration
Week 9–10: Final QA, rollback testing, benchmarking

Innovation and Best Practices

Autonomous schema adaptation via LLMs over logs and metadata
ETL optimization from Spark job patterns and execution plans
Metadata drift control through reasoning over Iceberg and Glue deltas
End-to-end lineage tracking and explainable anomaly detection
Built on AWS Well-Architected Framework: Security, Reliability, Performance, and Operational Excellence

Business Outcomes

70% reduction in engineering workload for schema/ETL updates
2x faster dashboard feature onboarding
Near real-time detection of schema changes, PII, and failures
30% faster time-to-insight from feature release
Zero downtime post-agent deployment
Continuous learning: Agents evolve and reduce future manual tasks

Technical Benefits

Schema-resilient pipelines that adapt automatically
Self-healing infrastructure with rollback and repair agents
Dynamic ETL tuning based on performance/cost trends
Metadata version control for consistent schema evolution
Security automation for PII and access governance

Lessons Learned

Challenges Overcome

Initial prompt-tuning for schema detection agents needed refinement
Debugging Glue jobs became easier through agent-generated annotations
IAM hardening required to scope agent actions safely

Best Practices

Combine LLM agents with logs and structured metadata
Start with schema and ETL automation before broader use cases
Use diff-based deployment and rollback for production safety
Version prompts and track agent performance SLAs

Future Plans

The platform plans to expand Agentic AI to:

Orchestrate ML pipelines for churn prediction using SageMaker
Detect anomalies in message delivery and latency
Automate business rule engines for campaign personalization
Enable real-time ingestion via Apache Hudi or DeltaStreamer

A long-term roadmap includes quarterly reviews, agent tuning, and co-developing intelligent observability layers.

Take Next Step for Data Engineering with Agentic AI

alk to our experts about accelerating data engineering with Agentic AI on AWS.
Discover how industries and data-driven teams are transforming analytics pipelines by adopting Agentic Workflows and Decision Intelligence. Shift from reactive operations to proactive, decision-centric data engineering.
Leverage AI agents to automate schema management, optimize ETL performance, and govern metadata at scale—reducing manual workload while increasing agility and insight delivery.

Accelerating Data Engineering with Agent DataOps on AWS

Customer Profile

Business Challenges

Technical Challenges