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Data Engineering

Fuel Smarter Decisions with Enterprise-Ready Data

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Designing Data Architectures That Drive Business Value

At DPS, we design data architectures that connect, organize, and activate your most valuable asset—your data. Whether you’re building from the ground up or modernizing legacy systems, our solutions enable scalable infrastructure, seamless integration, and real-time insight delivery. From advanced analytics and BI to cloud-based data lakes and enterprise data warehouses, we engineer systems that help you move faster, think clearer, and lead with confidence.

Offerings

Our Approach

  • Collaborate with stakeholders to understand goals, data challenges, and success metrics
  • Define project scope, technical requirements, and a roadmap for delivery

Discovery & Planning

  • Collaborate with stakeholders to understand goals, data challenges, and success metrics

  • Define project scope, technical requirements, and a roadmap for delivery

Data Integration & Architecture

  • Connect, ingest, and unify data from diverse sources (legacy systems, APIs, cloud, IoT)

  • Design a scalable architecture that supports both structured and unstructured data

Solution Design & Modeling

  • Build data models, analytics pipelines, and storage structures based on your use cases

  • Align governance, security, and performance standards with your business needs

Deployment & Enablement

  • Deploy solutions with minimal disruption to ongoing operations

  • Integrate seamlessly with BI, ML, reporting, and cloud platforms

Optimization & Support

  • Monitor, scale, and optimize data performance and cost

  • Provide ongoing support, training, and feature expansion as your data needs evolve

 

Technologies We Use

Amazon Web Services

Amazon Web Services

Used to build scalable, secure, and cost-efficient cloud-based data environments.
Microsoft Azure

Microsoft Azure

Supports hybrid cloud deployments, advanced analytics, and enterprise data warehousing.
Google Cloud Platform

Google Cloud Platform

Powers real-time data processing and large-scale storage with high availability.
Power BI

Power BI

Used for building interactive dashboards and reports for real-time business intelligence.
Tableau

Tableau

Transforms raw data into visual analytics and customizable dashboards for decision-makers.
Azure Machine Learning

Azure Machine Learning

Enables predictive modeling, ML experimentation, and operationalization of AI at scale.
Python

Python

Core scripting language used in machine learning, data pipelines, and advanced analytics.

What’s Trending?

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Data Mesh vs. Data Lakehouse: What’s Hype and What’s Real?

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Why Every AI Strategy Starts with Data Engineering
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In the rush to “go AI,” many organizations jump straight to model-building and analytics. But without structured, accessible, and reliable data AI becomes a stalled promise. The real groundwork starts earlier.

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FAQs

Data engineering focuses on building data pipelines and infrastructure, while data science analyzes that data to extract insights and predictions.

A data warehouse stores and organizes historical data for analytics, while an ERP system manages daily business operations in real time.

Popular data engineering tools include Apache Spark, Airflow, Kafka, Snowflake, and AWS Glue.

Implementing a data warehouse typically takes 3 to 6 months depending on complexity, data sources, and business goals.

You can import Excel data directly into Power BI, clean and model it, then create interactive dashboards for deeper insights.

Real-time analytics processes and visualizes data instantly as it’s generated to support immediate decision-making.

Cloud deployments offer scalability and lower maintenance, while on-prem setups provide full control and in-house data storage.

A modern data stack combines cloud-based tools for data collection, storage, transformation, and visualization such as Snowflake, dbt, and Power BI.

The key stages are data collection, ingestion, storage, transformation, modeling, and delivery for analytics use.

Top choices include AWS, Google Cloud, and Microsoft Azure for their robust data engineering and analytics services.

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