Data Engineering

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Modern Data Engineering builds scalable, reliable, real-time data pipelines with automation and AI/ML on cloud-native architectures for efficient and insightful data ecosystems.
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Data Lake, Warehouse, Marts, Feature Stores
Data to Cloud
Data Ops & Security
RDM, MDM, Metadata Management
Business Intelligence
Synthetic Data Generation
AI-Driven Data Pipelines
Self-Healing Data Systems
Data Governance
Predictive Analysis
Data Virtualization
Advanced Analytics
Why Reflections

Data Architecture, Done Right

We design and implement modern data lakes, warehouses, and marts tailored for scale, performance, and agility—on-prem, hybrid, or cloud-native.

AI-Driven, Self-Healing Data Systems

Our data pipelines don’t just move data—they monitor, detect, and fix issues on their own. With AI at the core, we build systems that learn, adapt, and stay healthy with minimal manual intervention.

Business Intelligence That Delivers

We build visually rich, high-impact dashboards using tools like Power BI, Tableau, Looker, and more—so your teams can make decisions backed by clean, reliable data.

Built-In Governance & Security

Security and compliance are embedded from day one. We implement role-based access, data lineage, auditability, and full regulatory alignment, so you never have to compromise on trust.

A-Grade Talent, Always

Our engineers, architects, and analysts bring deep technical expertise, cross-industry experience, and a problem-solving mindset. We don’t just execute - we elevate.

Our Accelerators

Smart Data Pipeline Generator: Supercharging Data Workflows

Reduced Development Time & Costs: Shortens pipeline creation for ingestion, transformation, and DQ checks, leading to significant time and cost savings.

AI-Powered Pipeline Generation: Leverages LLM/Gen AI to intelligently create pipelines or jobs in your Data Lake/Data Warehouse based on simple user prompts.

Streamlined Data Engineering: Reduces maintenance efforts and simplifies data engineering processes for faster and more efficient data workflows.

Data Cataloguer: Streamlining Data Asset Management

Enhanced Data Discovery & Understanding: Provides a centralized repository enabling users to easily discover and understand data assets.

Automated Metadata Management: Utilizes tools like OpenMetadata, Python, and MLlibs to automatically crawl, ingest, and manage metadata and the business catalog.

Unified Data Governance & Access: Delivers a unified view of data assets, facilitating improved data governance integration and enabling a virtual semantic layer for streamlined access.

Metadata-Driven Semantic Layer: Supercharging Data Access

Simplified Data Delivery: A dynamic, virtual access layer driven by metadata, usage data, and access privileges, facilitating easy data product delivery.

Intelligent Data Access Control: Leverages a central metastore in GraphDB with LLM and RAG for quicker data product delivery via access-controlled semantic layers.

Optimized Resource Utilization: Results in lesser compute costs due to minimal ETL and reduced storage costs thanks to the virtual layer, alongside quicker development cycles.

Impact Insights
Data Minimization for a leading US Bank
Data Minimization for a leading US Bank
Backdrop: Our client, overwhelmed by extensive data across applications and databases leading to increased costs, aimed to understand data usage and implement effective purging and archival in line with data minimization principles.
Solution: We implemented Data Quality & Minimization (cleaning, classification, deidentification, retention) and Data Governance aligned with business and compliance (including PCI DSS for payment data). We also established restoration, monitoring, and reporting for continuous improvement.
Impact

15% decrease in operational cost, 30% improved data processing, 40% improvement in data accessibility

Streamlining Data Processing Pipeline with Databricks Delta Lake for a Food Processing Company
Streamlining Data Processing Pipeline with Databricks Delta Lake for a Food Processing Company
Backdrop: The existing ingestion framework faced limitations including limited CDC support in the RAW Layer, a steep learning curve for developers due to a lack of user-friendliness, scalability issues hindering development cycles, and minimal auditing & logging across key data processing frameworks.
Solution: We implemented a comprehensive, user-friendly ingestion framework with enhanced CDC support, optimized scalability using Delta Lake and Spark for fast processing of massive data and added robust auditing/logging. We also expanded connector compatibility (including ADF) and introduced Low Code/No Code features for transformation and DQ.
Impact

15% optimized development cycle, 25% improved decision-making, 35% reduction in data processing time.

Implementing Master Data Management for a Manufacturing Company
Implementing Master Data Management for a Manufacturing Company
Backdrop: Managing vast master, reference, and meta data internally with existing MDM tools was a cumbersome process for the client, negatively impacting data integrity across their operational and analytical systems.
Solution: In four months, we developed a robust MDM solution using Apache Griffin, establishing 25 pipelines for seamless data lifecycle management and implementing strong governance with data virtualization, resulting in near-zero data integrity issues across all systems.
Impact

15% enhanced business operations, 45% improved data management, 35% reduction in processing time.

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