Contact

Enterprise Data Platform Modernization, Analytical Pipeline Development

How to improve access to trusted business data across multiple enterprise systems?

Azati helped a large chemical enterprise modernize and continuously evolve its analytical platform by developing data pipelines, analytical data marts, and orchestration workflows. The engagement strengthened access to trusted business data, improved operational visibility, and established scalable foundations for future analytics initiatives.

Improve access to trusted business data
Dozens

of analytical data marts and pipelines built and enhanced

Hundreds

of flows within the enterprise data ecosystem

Thousands

of analytical structures and relationships supported

Technologies used

SQL
SQL
Python
Python
dbt
dbt
Greenplum
Greenplum
PostgreSQL
PostgreSQL
ClickHouse
ClickHouse
Apache NiFi
Apache NiFi
Apache Airflow
Apache Airflow

Motivation

Large industrial organizations often operate dozens of business systems and generate continuously growing volumes of operational data. To support reporting, analytics, and decision-making, they require scalable mechanisms to collect, transform, and deliver trusted information to downstream systems.

The client operated a large platform that played an important role in enterprise information management by consolidating data from multiple systems and providing trusted analytical assets for reporting and decision-making.

Azati joined the engagement as part of an extended delivery team and contributed to the ongoing development of data pipelines, analytical data marts, and optimization of data processing mechanisms.

How to support growing analytical requirements across hundreds of interconnected data flows?

Challenge 01

Supporting growing analytical requirements

As new business initiatives and systems emerged, the organization continuously required new data pipelines and analytical structures capable of supplying reporting and downstream analytics.

#1
Challenge 02

Managing complexity across a large data ecosystem

The environment included numerous systems, hundreds of data flows, and extensive interdependencies. Maintaining consistency while expanding the platform required careful coordination and scalable approaches to data engineering.

#2
Challenge 03

Delivering trusted data for reporting and analytics

Business teams depended on reliable information extracted from multiple sources. Data preparation processes had to ensure that analytical consumers could access consistent, trustworthy datasets capable of supporting reporting and operational decision-making.

#3
Challenge 04

Maintaining performance as the platform evolved

Growing volumes of information and increasingly complex transformations required continuous improvements to data processing logic and optimization of existing workflows.

#4

Why do enterprises choose Azati for large-scale data platform modernization?

Experience supporting massive analytical ecosystems

The engagement involved hundreds of flows, thousands of analytical structures, and continuously evolving reporting requirements across multiple systems.

Embedded delivery inside enterprise data organizations

Azati specialists integrated into established architectures and processes while providing additional engineering capacity.

Long-term support for continuously evolving analytical platforms

Rather than isolated initiatives, the Azati team contributed to an environment where new systems and reporting requirements emerged continuously.

Ability to operate within highly interconnected environments

Enterprise analytical platforms depend on numerous systems and constantly changing business requirements. Azati supported the evolution of a complex ecosystem while maintaining continuity across interconnected workflows and enabling the gradual expansion of analytical capabilities.

Strengthen enterprise reporting foundations

Azati helps organizations improve analytical platforms, develop data pipelines, and provide trusted information for reporting and operational decision-making.

Discuss your data platform

How to build scalable analytical pipelines and data marts for enterprise reporting?

Azati developed analytical pipelines, data marts, and orchestration mechanisms using SQL, Python, dbt, Apache Airflow, Apache NiFi, Greenplum, PostgreSQL, and ClickHouse. The resulting platform delivered trusted datasets that supported reporting and self-service analytics across multiple business domains.

Enterprise data platform enhancement and analytical pipeline development

Azati contributed to the continuous evolution of a large-scale data platform responsible for integrating information from multiple systems and delivering datasets used by downstream reporting and self-service analytics. The improvements strengthened the reliability and consistency of analytical workflows that support reporting and operational analysis.

Key capabilities:
01

Data integration and ingestion workflows

The platform continuously collected information from multiple enterprise systems. Azati supported the development of mechanisms responsible for bringing data into the analytical environment and preparing it for downstream processing.

Key capabilities:
  • Data ingestion
  • Integration workflows
  • External systems
  • ETL pipelines
  • Incremental loading
02

Enterprise data modeling and analytical structures

Working together with architects and delivery teams, Azati contributed to analytical models and internal structures supporting the storage and organization of information used across reporting processes.

Key capabilities:
  • Data models
  • Table structures
  • Relationship design
  • Analytical schemas
  • Enterprise data warehouse support
03

Analytical data products for reporting and analytics

The engagement focused on developing business-oriented analytical data products that simplified access to information required by reporting and analytics teams. Instead of requiring users to work directly with complex enterprise datasets, the platform provided curated analytical structures optimized for downstream reporting and self-service analytics.

Key capabilities:
04

Workflow orchestration and automation

The platform relied on orchestration mechanisms that enabled continuous processing and delivery of information across multiple systems.

Key capabilities:
05

Performance optimization and maintainability

As analytical requirements expanded, existing transformations and loading mechanisms required ongoing improvements. Azati contributed to optimization initiatives that enhanced efficiency and maintainability across data workflows.

Key capabilities:
  • SQL optimization
  • Join improvements
  • Transformation refinement
  • Query tuning
  • Workflow optimization
06

Scalable analytical foundations

The engagement supported architectural approaches designed to accommodate growing analytical requirements and ensure the scalability of enterprise analytics initiatives.

Key capabilities:

What Azati did

AreaAzati contribution
Data engineeringDeveloped and maintained analytical pipelines
Data modelingContributed to structures supporting new systems
Data martsBuilt reporting-oriented analytical datasets
Workflow automationSupported orchestration and processing mechanisms
PerformanceImproved SQL logic and optimized transformations
Platform evolutionEnhanced existing loading scripts and workflows
Reporting supportSupplied trusted datasets for BI teams
DeliveryWorked within a long-term Scrumban process

What challenges arise when supporting enterprise analytical ecosystems?

Azati worked inside a highly interconnected environment consisting of hundreds of flows and thousands of analytical structures. The engagement required balancing evolving business requirements with performance optimization and platform stability.

Operational challenges Azati encountered

Large-scale platform complexity

The environment involved hundreds of data flows, thousands of tables, and extensive dependencies between systems. Supporting platform evolution required understanding complex relationships and maintaining consistency across the analytical ecosystem.

Continuously evolving requirements

New systems and reporting needs regularly appeared, requiring the platform to accommodate changing business priorities.

Performance optimization in growing environments

As analytical assets expanded, transformations and loading processes required ongoing refinement to maintain efficiency.

Working inside an established architecture

Enhancements had to integrate with existing structures while preserving platform stability and continuity.

Improve enterprise data platforms

Azati helps organizations modernize analytical infrastructures, develop data pipelines, and support reporting ecosystems that depend on trusted information.

Talk to a data engineering expert

Key delivery outcomes

MetricValue
Engagement duration24+ months
Delivery modelOngoing
Data marts and pipelines developedDozens
Data ecosystem complexityHundreds of flows and thousands of tables
Collaboration modelEmbedded delivery

What business outcomes can enterprise data platform modernization deliver?

Improved access to trusted business data

Curated analytical datasets simplified information consumption for BI teams and supported self-service analytics without requiring direct interaction with complex source systems.

Better operational visibility

Reliable analytical structures improved operational analytics capabilities and enabled business stakeholders to access information required for monitoring and decision-making across multiple domains.

More sustainable analytical platform evolution

Continuous enhancements helped accommodate changing reporting requirements and new systems.

Stronger foundations for future analytics initiatives

Optimization efforts and scalable architectures improved maintainability of enterprise data workflows. Business-oriented analytical structures helped enable reporting and self-service analytics initiatives.

Additional data engineering capacity without disrupting delivery

Azati integrated into the client's established environment and contributed to long-term analytical platform development.

What strategic advantages did the client gain from enterprise data platform modernization?

Improved access to trusted business data

Curated analytical datasets enabled reporting and BI teams to access consistent information without interacting directly with the complexity of underlying systems, strengthening trust in analytical processes.

Better operational visibility

Reliable analytical structures improved access to information used for reporting and operational analysis across multiple business domains.

More sustainable analytical platform evolution

Continuous optimization and scalable architectural approaches established a stronger foundation for accommodating new systems and changing reporting requirements.

Additional engineering capacity supporting continuous delivery

Azati integrated into the client's established delivery organization and contributed to the ongoing development of analytical infrastructure without disrupting existing processes.

Stronger foundations for self-service analytics enablement

Business-oriented analytical data products simplified information consumption, supporting self-service analytics initiatives and reporting teams in accessing curated datasets tailored to their needs.

How did Azati deliver continuous analytical platform evolution?

Embedded delivery model

Azati participated as part of a broader engineering organization supporting enterprise data initiatives.

Continuous collaboration

The engagement followed a Scrumban approach with evolving requirements and ongoing delivery activities.

Team composition

Azati provided a dedicated data engineering specialist responsible for analytical pipelines, data marts, and optimization activities.

The described expertise is relevant for

  • Enterprise data platforms
  • Data warehouse development
  • Data engineering services
  • Analytical data marts
  • ETL pipeline development
  • Reporting data preparation
  • Data integration platforms
  • Workflow orchestration
  • Apache Airflow development
  • Apache NiFi integration
  • SQL optimization
  • Greenplum development
  • ClickHouse analytics
  • dbt implementation
  • Enterprise reporting systems
  • Operational analytics platforms
  • Industrial data platforms

Related case studies

Explore our successful projects and see how Azati delivers measurable results for our clients.

SAP ERP Data Bus Integration for a Governmental Energy Corporation
Energy, Oil & Gas

SAP ERP Data Bus Integration for a Governmental Energy Corporation

2–4x increase in data transfer and processing capacity
25–50% decrease in document handling errors
100% compliance with required business protocols
  • Apache Camel
  • Spring
  • Groove
  • SOAP Web Services
  • SAP ERP Integration

⚡ Pain Points We Tackled

The client needed to optimize document generation and management workflows across internal enterprise systems. Their existing processes relied on fragmented integrations between SAP ERP and a document generation platform, causing delays, manual overhead, inconsistent data exchange, and frequent processing errors. The client required a middleware integration layer capable of securely orchestrating enterprise document workflows while supporting different protocols, formats, and business logic requirements.

Our Approach

Azati developed a middleware data bus integrating SAP ERP with the client’s document generation system. Using Apache Camel, Spring, and SOAP web services, the solution automated enterprise document workflows and enabled reliable real-time data exchange between systems. The platform standardized message processing, automated document-related operations, transformed and enriched incoming data, improved validation and error handling, and ensured secure communication between systems.

Applied Methods and Practices

  • Middleware integration with Apache Camel: Built a centralized enterprise data bus using Apache Camel and CXF to orchestrate communication between SAP ERP and the document generation system.
  • SOAP web service integration: Implemented SOAP-based services for handling enterprise document operations and system synchronization.
  • XML and JSON transformation: Developed custom processing pipelines for XML-to-JSON conversion, metadata enrichment, and downstream message routing.
  • Security and authentication controls: Implemented authentication mechanisms and protected enterprise data exchange workflows.
  • Error validation and logging: Introduced advanced validation, descriptive error handling, and logging mechanisms for troubleshooting and workflow continuity.

Solution Features

  • Middleware data bus: Central integration hub connecting SAP ERP with the document generation system.
  • Automated document workflows: Reduced manual effort in creating, processing, and managing standardized governmental documents.
  • Real-time enterprise data exchange: Enabled stable synchronization between enterprise systems.
  • Data transformation engine: Validated, enriched, and converted messages between required formats and protocols.
  • Enterprise security controls: Protected internal data flows with authentication and controlled access mechanisms.
  • Validation and monitoring: Provided detailed logging and error detection for reliable workflow execution.
Read Full Case Study
AI-Powered Data Recognition for an Oil & Gas Enterprise
Energy, Oil & Gas

AI-Powered Data Recognition for an Oil & Gas Enterprise

40% better data recognition accuracy
30% less manual data processing effort
2X faster data extraction and analysis
  • Python
  • Machine Learning
  • Computer Vision
  • Elasticsearch
  • REST APIs

⚡ Pain Points We Tackled

The client needed to process large volumes of unstructured industrial data (documents, schemes, technical records), but faced low accuracy of manual data extraction, time-consuming processing workflows, inconsistent data formats, and difficulty integrating extracted data into operational systems. These issues slowed down decision-making and increased operational costs.

Our Approach

Azati developed an AI-powered data recognition system to automate the extraction, structuring, and validation of industrial data. The solution focused on: applying machine learning and computer vision for data extraction, structuring unstructured data into usable formats, integrating outputs into existing workflows and systems, continuously improving accuracy through validation and feedback loops. This enabled faster and more reliable data processing at scale.

Applied Methods and Practices

  • AI-based data extraction: Used machine learning and computer vision to extract information from complex documents and technical files.
  • Data structuring & normalization: Converted unstructured inputs into standardized, system-ready formats.
  • Validation & feedback loops: Improved model accuracy over time through continuous validation and refinement.
  • Workflow integration: Embedded AI outputs into operational systems via APIs.
  • Manual and automated QA: Combined automated processing with manual validation for critical data points.

Solution Features

  • Automated data recognition: AI extracts and processes data from diverse industrial sources.
  • Improved accuracy: Higher reliability compared to manual or rule-based approaches.
  • Faster processing: Reduced time required for data extraction and analysis.
  • Integration-ready outputs: Structured data ready for downstream systems and workflows.
  • Scalable architecture: Handles growing volumes of industrial data efficiently.
Read Full Case Study
Insurance Company Self-Service System
Insurance

Insurance Company Self-Service System

92% fewer production bugs
2.5x faster regression testing
99% test-case pass rate before release
  • Web QA
  • Mobile QA
  • JUnit
  • TestNG
  • Custom Software

⚡ Pain Points We Tackled

The client, an insurance company with a self-service web platform, faced complex workflow inconsistencies, data integrity risks, and limited QA coverage, which affected customer experience and operational efficiency. Multi-step processes like policy management and customer interactions were prone to errors, slowing down releases and increasing support overhead. Our task was to provide full-cycle quality assurance to stabilize the platform, ensure workflow accuracy, and improve release reliability.

Our Approach

Azati's QA team worked closely with the client to analyze insurance-specific workflows such as policy management, customer self-service actions, and backend integrations. We combined manual and automated testing, focusing on validating business logic, ensuring data consistency across systems, and covering real-world user scenarios. Special attention was given to multi-step workflows, integrations with backend systems (CRM, billing, databases), and edge cases that could impact compliance and customer trust.

Applied Methods and Practices

  • Comprehensive Test Plan: Designed a full QA strategy covering functional, regression, integration, and usability testing, ensuring all insurance workflows and user scenarios were validated.
  • Automated Regression Testing: Implemented automation for critical user journeys such as policy updates, account management, and data submission, reducing regression time and improving release speed.
  • Manual Exploratory Testing: Focused on uncovering edge cases in complex workflows, including multi-step user interactions, data validation issues, and rare failure scenarios.
  • Integration Testing: Validated interactions between the self-service platform and backend systems (CRM, billing, and data services), ensuring consistency and reliability across the ecosystem.
  • Data Integrity Validation: Ensured accuracy and synchronization of customer and policy data across all stages of the workflow.
  • QA Integration in CI/CD: Embedded QA processes into the development pipeline, enabling continuous testing and faster feedback.

Solution Features

  • End-to-End Workflow Coverage: Comprehensive validation of insurance processes, including policy management, customer interactions, and backend operations.
  • Automated Regression Suite: Fast verification of critical workflows after each release, enabling safer and quicker deployments.
  • Integration Reliability: Stable communication between frontend and backend systems, reducing data inconsistencies and system errors.
  • Risk-Based Bug Prioritization: Issues prioritized based on business impact (e.g., policy errors, data inconsistencies), ensuring critical problems were addressed first.
Read Full Case Study
Insurance MDM and CRM modernization platform
Insurance

Insurance MDM and CRM modernization platform

70–90% faster CRM process execution
3–4x increase in data processing and reporting capacity
6-digit USD annual software licensing cost savings
  • Java
  • JavaScript
  • Apache Maven
  • Oracle Database
  • Custom Software

⚡ Pain Points We Tackled

A large insurance organization struggled with fragmented customer information distributed across multiple departments and disconnected applications. Sales, marketing, customer support, and operational teams relied on separate systems that stored overlapping and often inconsistent customer records.

As the business expanded, manual data consolidation became increasingly time-consuming and error-prone. Teams lacked a unified customer view, reporting required significant manual effort, and business processes varied across departments. The organization needed a centralized platform capable of improving customer engagement while establishing consistent and trustworthy master data across the enterprise.

Our Approach

Azati implemented an integrated CRM and Master Data Management (MDM) platform that consolidated customer, policy, and product information into a single source of truth. The solution automated data synchronization, standardized business processes, enabled real-time performance monitoring, and replaced multiple disconnected legacy systems.<br><br>The platform improved operational efficiency, increased reporting capacity, reduced software costs, and provided a scalable foundation for future business growth.

Applied Methods and Practices

  • Unified customer data platform: Developed a centralized CRM system that consolidated customer information from multiple departments, databases, and legacy applications into a single reliable repository.
  • Master data management integration: Implemented MDM capabilities to maintain authoritative customer, policy, and product records while ensuring consistency across all connected business systems.
  • Automated data synchronization: Built automated processes for data integration, validation, standardization, and deduplication, eliminating manual consolidation activities and improving data quality.
  • Business process standardization: Aligned sales, marketing, customer service, and operational workflows around common processes, rules, and templates to improve consistency across departments.
  • Real-time reporting and analytics: Implemented dashboards and KPI monitoring capabilities that provided visibility into sales performance, campaign effectiveness, operational bottlenecks, and process efficiency.
  • Legacy system consolidation: Replaced multiple disconnected applications with a unified platform, reducing licensing costs, simplifying user training, and lowering long-term maintenance overhead.

Solution Features

  • Single source of truth for customer data: Customer information is centralized across departments, enabling employees to access accurate and consistent records throughout the customer lifecycle.
  • Faster CRM operations: Automated workflows and data synchronization significantly reduced processing times for customer management, reporting, and operational activities.
  • Improved business visibility: Real-time dashboards provide management teams with immediate access to performance metrics, bottlenecks, and operational trends.
  • Lower operational costs: Consolidation of legacy systems reduced software licensing expenses and minimized the effort required to maintain multiple platforms.
  • Better customer engagement: Sales, marketing, and support teams gained a complete customer view, enabling more personalized interactions and improved service quality.
  • Scalable enterprise data foundation: The integrated CRM and MDM architecture established a reliable foundation for future modernization initiatives, analytics projects, and business application integrations.
Read Full Case Study
Digital Asset Management Platform Support & Migration
Retail & Logistics

Digital Asset Management Platform Support & Migration

10,000+ digital assets migrated with full metadata integrity
1.5 years of continuous platform support and customization
2 months to complete end-to-end market migration
  • Java
  • JavaScript
  • AWS
  • Adobe AEM
  • Amazon S3
  • Digital asset management

⚡ Pain Points We Tackled

A global beverage enterprise relied on an Adobe AEM-based digital asset management platform supporting marketing operations across multiple regional markets. However, the company lacked internal engineering capacity to manage platform customizations, resolve support issues, and coordinate vendor escalations.

As the organization prepared to transition to a new DAM solution, the migration introduced additional complexity. Asset metadata was inconsistent, APIs contained gaps and inaccuracies, and the migration needed to preserve regional governance rules and access policies without disrupting ongoing operations. The client required a scalable approach that could support future market rollouts rather than a one-time migration effort.

Our Approach

Azati became the client's dedicated technical partner for Adobe AEM operations and later designed a reusable migration framework for transferring digital assets and metadata to a new platform. The engagement combined ongoing platform support, workflow customization, metadata management, and automated migration capabilities.<br><br>The approach prioritized business continuity, data integrity, and long-term scalability, allowing the client to maintain uninterrupted marketing operations while building a foundation for future migrations across additional markets.

Applied Methods and Practices

  • Platform support and customization: Provided ongoing technical ownership of the Adobe AEM environment, resolving support requests, managing vendor escalations, and implementing platform customizations aligned with regional business requirements and governance policies.
  • Migration pipeline development: Designed and implemented an automated migration pipeline capable of transferring assets and metadata to the client's new platform through Amazon S3, reducing manual effort and supporting large-scale asset volumes.
  • Metadata integrity management: Developed structured mapping and verification processes to preserve metadata attributes, regional filters, collections, and access policies while addressing inconsistencies in source APIs and datasets.
  • Reusable migration architecture: Built a configurable, market-agnostic migration framework capable of supporting additional regional rollouts without significant architectural changes or redevelopment.
  • Operational continuity: Maintained uninterrupted access to digital assets throughout the engagement, ensuring that marketing teams continued to operate without disruptions during support activities and migration execution.

Solution Features

  • Centralized technical ownership: Established dedicated engineering support for a business-critical digital asset platform, providing continuous operational management and faster issue resolution.
  • Successful large-scale migration: Transferred more than 10,000 digital assets and associated metadata while preserving data quality and governance requirements.
  • Reduced manual effort: Automated complex migration activities that would otherwise have required extensive manual analysis, extraction, and validation.
  • Scalable migration foundation: Delivered a reusable migration framework supporting future market transitions and reducing the cost and complexity of subsequent rollouts.
  • Business continuity throughout transition: Maintained uninterrupted platform availability during both support and migration phases, enabling marketing teams to continue operations without downtime.
Read Full Case Study

Related insights

200,000 SKUs and Nobody Knows Which Ones Matter
AI
Read more

200,000 SKUs and Nobody Knows Which Ones Matter
Why P&ID Data Never Reaches Your Warehouse
AI
Read more

Why P&ID Data Never Reaches Your Warehouse
Why Asset-Intensive Operators Pay Twice for Bad Data
AI
Read more

Why Asset-Intensive Operators Pay Twice for Bad Data
When Engineering Data Becomes an Execution Risk
AI
Read more

When Engineering Data Becomes an Execution Risk
Automated Data Labeling With Machine Learning
Technology
Read more

Automated Data Labeling With Machine Learning

Last updated

Got a job for Azati? Let’s talk business!

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

What's next?

  • 1. Tell Us Your Story
    Describe your project. We come back within 24 hours with team availability and a rough plan. NDA on request before the first call.
  • 2. Get Your Roadmap
    Receive a detailed proposal with scope, team composition, timeline, and costs tailored to your goals.
  • 3. Start Building
    Azati aligns on details, finalize terms, and launch your project with full transparency.