Enterprise Data Infrastructure

Data Engineering & Pipeline Development

Modernize your existing data pipelines with custom ETL scripts and integrations—or implement FedRAMP ETL platforms (Informatica Gov, Talend, AWS Glue) when enterprise-scale data orchestration and governance are needed. Reliable, scalable data infrastructure that powers analytics and AI initiatives.

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Our Methodology

4-Phase Pipeline Development Methodology

Typically completed in 14-20 weeks depending on pipeline complexity and system integrations

1
Phase 1

Data Architecture Assessment

Duration: 2-3 weeks
  • Inventory existing data sources, pipelines, and storage systems
  • Document data flows, dependencies, and transformation logic
  • Assess data quality issues and governance gaps
  • Evaluate FedRAMP ETL platform options (Informatica Gov, Talend, AWS Glue)
2
Phase 2

Pipeline Design & Architecture

Duration: 3-4 weeks
  • Design target-state data architecture and pipeline topology
  • Define data models, schemas, and transformation specifications
  • Establish data quality rules and validation frameworks
  • Plan migration strategy for legacy pipelines
3
Phase 3

Pipeline Development & Testing

Duration: 6-10 weeks
  • Build automated ETL/ELT pipelines with error handling
  • Implement data quality monitoring and alerting
  • Develop data lineage tracking and audit capabilities
  • Conduct integration testing with source and target systems
4
Phase 4

Deployment & Operations

Duration: 2-3 weeks
  • Deploy pipelines to FedRAMP-authorized infrastructure
  • Configure monitoring dashboards and alerting
  • Train operations team on pipeline management
  • Establish runbooks for incident response and maintenance

Pipeline Capabilities

Enterprise Data Engineering Services

End-to-end data pipeline capabilities from batch processing to real-time streaming

Batch ETL Pipelines

Scheduled data extraction, transformation, and loading for periodic data processing

Best For:
Nightly data warehouse loads, monthly reporting, historical analysis

Real-Time Streaming

Sub-second data processing for operational dashboards and event-driven architectures

Best For:
Fraud detection, operational monitoring, IoT sensor data

Data Lake Architecture

Scalable storage for structured and unstructured data with schema-on-read flexibility

Best For:
Big data analytics, data science workloads, archival storage

Data Warehouse Modernization

Migration from legacy warehouses to cloud-native platforms (Snowflake Gov, Redshift)

Best For:
Performance optimization, cost reduction, scalability

Data Quality Frameworks

Automated validation, profiling, and anomaly detection for data integrity

Best For:
Regulatory compliance, mission-critical reporting, analytics accuracy

Master Data Management

Single source of truth for critical entities across agency systems

Best For:
Customer/citizen data, vendor management, asset tracking

Technology Stack

FedRAMP-Authorized Data Platforms

Deep expertise across government-approved data engineering platforms

ETL/ELT Platforms

Informatica GovernmentTalend GovernmentAWS GlueAzure Data Factory Gov

Streaming Platforms

Apache KafkaAmazon KinesisAzure Event HubsApache Flink

Data Warehouses

Snowflake GovernmentAmazon RedshiftAzure Synapse GovGoogle BigQuery

Data Lakes

AWS S3 (GovCloud)Azure Data Lake GovDatabricks GovernmentDelta Lake

Success Story

Real-World Pipeline Implementation Results

Department of Defense

Challenge

DOD was spending 85,000 hours annually on manual data processing across 14 legacy systems. Data latency of 48+ hours prevented real-time decision-making.

Solution

Built automated ETL pipelines using Informatica Gov to consolidate data from all legacy systems with real-time streaming for critical operational data.

$12M
Annual cost savings
48hrs → 15min
Data latency reduction
99.7%
Data accuracy achieved
85,000
Manual hours eliminated

What You Receive

Pipeline Implementation Deliverables

Data Architecture Blueprint

40-60 pages

Comprehensive documentation of target-state architecture including data flows, schemas, and integration specifications.

Pipeline Documentation

50-75 pages

Technical documentation for all pipelines including transformation logic, scheduling, error handling, and dependencies.

Production Pipeline System

Full system

Deployed, monitored data pipelines with automated scheduling, error handling, and data quality validation.

Operations & Runbook

30-40 pages

Operational procedures for pipeline monitoring, incident response, maintenance, and capacity planning.

Ready to Modernize Your Data Infrastructure?

Schedule a complimentary consultation to discuss your data engineering needs and learn how we can help you build reliable, scalable data pipelines.

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