Solving the Data Overload Problem with Smart Architecture

Cloudesign delivers Big Data Architecture and Data Lake solutions that transform fragmented data into unified intelligence. Our data science consulting, AI-driven analytics, and predictive data analytics services help enterprises harness structured and unstructured data, enabling real-time insights, automation, and scalable business growth across industries.

Transform Data Chaos into Clarity with Modern Data Architecture

Build a scalable data lake and architecture that turns fragmented information into actionable business intelligence. Cloudesign empowers enterprises to harness intelligence from every byte through a modern data platform built for growth, innovation, and agility.

Our data modernization services help you unify disconnected data, enabling seamless data analytics, AI-driven insights, and predictive intelligence across your organization. With a robust data architecture and cloud-native foundation, we ensure your business data is secure, scalable, and ready for the future of data science and advanced analytics.

The Intelligent Framework for Scalable Data Lakes and Big Data Solutions

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Integrate structured, semi-structured, and unstructured data from databases, APIs, and IoT devices.

Leverage big data processing tools like Apache Kafka, Spark, and Databricks.

Automate ETL/ELT workflows for real-time and batch data ingestion.

Ensure data consistency and lineage tracking for compliance and traceability.

Enable scalable, low-latency pipelines for analytics and AI workloads.

Case Study: Big Data Analytics Dashboard for Jindal Aluminium

Client: Jindal AluminiumClient: Jindal Aluminium
Industry: Manufacturing • Metals & Materials • B2B Enterprise SalesIndustry: Manufacturing • Metals & Materials • B2B Enterprise Sales

Challenge

Jindal Aluminium managed its nationwide sales operations using massive Excel files (120,000 rows per year). Manual analysis slowed decision-making, limited trend detection, and made leadership planning difficult. With growing data volumes, the workflow became unsustainable.

The Problem

Manual Excel-based analysis (240k+ rows) was slow, error-prone, and difficult for non-technical teams.

Executives lacked a unified dashboard for year-over-year, zone-wise, customer-wise, and material-wise performance.

Revenue dips and low-performing zones often went unnoticed due to fragmented data.

Forecasting and decision-making depended on gut feel instead of accurate, data-driven insights.

Our Solution: Cloudesign Big Data & Analytics Visualisation Framework

Built a high-performance, browser-based Sales Insights Dashboard processing large datasets without any database, ETL pipeline, or big data setup.

Automatically transformed raw Excel spreadsheets into clean, analytics-ready data for instant insights.

Provided multi-dimensional analysis across sales, revenue, materials, customers, executives, and zones.

Enabled clear year-over-year comparisons, trend detection, and visibility into hidden performance patterns.

Delivered smart forecasting capabilities helping leaders make accurate, data-driven decisions.

Results in 6 Weeks

MetricBeforeAfterImpact
Sales Data Processing Time4–6 hours manually< 15 seconds99% faster
Insights VisibilityFragmentedUnified dashboardFull KPI clarity
Yearly ComparisonManual Excel workAutomated real-timeInstant decision-making
Trend DetectionDifficult, error-proneVisual & automated10× faster insights
ForecastingNone18% predicted revenue growthStrategy-aligned planning

Forecast-driven strategic planning

Instant year-over-year analytics

99% faster processing

Why It Worked

Efficient big data processing without heavy infrastructure

Efficient big data processing without heavy infrastructure

Smart visualisation architecture using React.js

Smart visualisation architecture using React.js

KPI-first structure aligned with leadership needs

KPI-first structure aligned with leadership needs

Forecast models and risk indicators built into the UI

Forecast models and risk indicators built into the UI

No backend complexity, easy to maintain and scale

No backend complexity, easy to maintain and scale

Want dashboards that drive decisions?

Service Capabilities

From custom LLM development and advanced RAG systems to multi-agent deployments and industry-specific regulations, we deliver enterprise-grade AI solutions that are reliable, cost-efficient, and aligned with stringent security standards.

Architecture Strategy & Planning - visual content 1

Architecture Strategy & Planning

We partner with you to define the blueprint for your data ecosystem, mapping business objectives, data workflows, technology stack and governance. This ensures your architecture supports data science consulting, predictive data analytics and AI data science efforts effectively.

Data Lake Design & Implementation

Our team builds a scalable data lake that accommodates all data types and supports AI and analytics workloads. We focus on ingestion, storage, indexing, cataloguing and serving up data for analytics, machine learning and dashboards.

Data Lake Design & Implementation
Big Data Platform Engineering - visual content 1

Big Data Platform Engineering

We engineer platforms on cloud-native services (or hybrid) with compute, storage and orchestration optimised for high volumes, high velocity and low latency. This supports data analytics, artificial intelligence scenarios and predictive data analytics services.

Data Ingestion, Integration & Pipelines

We build robust ingestion pipelines, batch and streaming, to bring data from multiple sources into your lake. Clean, enrich and prepare that data for predictive models, dashboards and AI-driven insights.

Data Ingestion, Integration & Pipelines
Analytics, BI & AI Enablement - visual content 1

Analytics, BI & AI Enablement

Once your lake is live, we enable analytics, embedding AI into your data environment. Using predictive data analytics tools, machine learning models and AI for data analytics, we help you move from raw data to actionable insight.

Governance, Security & Compliance - visual content 1

Governance, Security & Compliance

We design governance frameworks, data catalogues, access controls and lineage tracking so your lake is secure, compliant (including for India and global regulations) and trusted for mission-critical use.

Ongoing Optimisation & Support

Data lakes are dynamic. We offer managed services to monitor performance, optimise pipelines, incorporate new data sources and ensure your architecture continues to deliver value and supports your data science development services long-term.

Ongoing Optimisation & Support

How We Bridge Your Challenges

Need Additional Expertise? IT Staff Augmentation for Big Data Analytics Services

Beyond our comprehensive Big Data Analytics services, Cloudesign provides IT staff augmentation solutions connecting you with specialized Big Data developers. Ready to augment your team with experienced Big Data Analytics professionals?

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Why Modern Big Data Architecture & Data Lakes are Essential for Business

In today’s data-driven world, companies must handle vast volumes of diverse information from multiple sources. A modern architecture with a well-designed data lake gives you:

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A single source of truth where structured, semi-structured and unstructured data coexist.

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The ability to power data science development services, AI for data analytics and predictive data analytics tools with high-quality data.

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Real-time and batch insights that help teams act faster, reduce costs and drive growth.

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A scalable, governed environment for growth, whether you are in India or operating globally.

Why Choose Us for Big Data Architecture & Data Lakes in India?

Deep domain experience

Deep domain experience

We support enterprises in India and globally with architecture, data science consulting services and analytics-driven transformation.

End-to-end delivery

End-to-end delivery

From strategy to build to AI enablement, one partner handles your full data lake journey.

Security & compliance first

Security & compliance first

Data governance, encryption, access controls and lineage built in for regulatory peace of mind.

Real business results

Real business results

We help reduce analytical time-to-insight, lower costs and enable smarter decisions using AI data science and predictive data analytics services.

Local delivery, global standards

Local delivery, global standards

Based in India, we understand regional markets and regulations while delivering world-class technology solutions.

Continuous innovation

Continuous innovation

We leverage the latest in AI for data analytics, big data architecture patterns and predictive data analytics tools so you’re ready for tomorrow.

What Our Customers Say

Praveen Sharma
Praveen Sharma
Shannon Kenny
Shannon Kenny
Harsh Jha
Harsh Jha
Rikard Windh
Rikard Windh
Praveen Sharma
Praveen Sharma
VP, Engineering, Cactus Communications

As a growing company, finding top-notch" engineering talent at affordable rates is one of the biggest challenges. Cloudesign, a strategic partner to us, has provided us Angular, ReactJs, Laravel & UI Developers at short notice for a wide range of projects at Cactus Communications. They are quick to provide candidates, always very quick in communications, and overall keeping the process low friction.Cloudesign Engineers took control of our projects in a way that exceeded my expectations. we have been happy with the quality of the software engineers as well as thier management team.

500+
Successful Transformations

ROI-generating digital solutions delivered to Industry leaders across 10+ sectors

150+
Expert Engineers

Cross-functional experts across AI, ML, IoT & more collaborating for excellent Client experience

12+
Years Of Excellence

Unparalleled IT execution that’s forged lasting customer relationships

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Frequently Asked Questions

Big Data Lake Architecture is a scalable framework designed to collect, store, process, and analyse large volumes of raw data, structured, semi-structured, and unstructured, from multiple sources. It provides a flexible foundation for advanced analytics, machine learning, and AI-driven insights, ensuring organisations can make faster and more informed decisions.

No, a Data Lake is not an ETL (Extract, Transform, Load) tool. It is a centralised data repository that stores data in its raw format. ETL or ELT processes are used to move, clean, and prepare data before or after it enters the Data Lake for analysis.

The main types include:

  • Lambda Architecture: Combines batch and real-time data processing.
  • Kappa Architecture: Focuses entirely on real-time streaming data.
  • Data Lakehouse Architecture: Merges the flexibility of Data Lakes with the structure of Data Warehouses for unified analytics.

A Data Warehouse stores processed, structured data optimised for business intelligence, while a Data Lake stores raw, unprocessed data suitable for advanced analytics, data science, and AI applications.

Enterprises generate massive volumes of data daily. Big Data Architecture helps them organise, store, and analyse this data efficiently, unlocking predictive insights, optimising operations, and enhancing customer experiences through AI and analytics.

AI automates data classification, detects anomalies, and accelerates predictive modelling. When integrated with Data Lakes, it enables real-time analytics and data-driven automation, helping organisations make smarter, faster decisions.

A robust Big Data Architecture includes:

  • Data Ingestion Layer (collecting data from various sources)
  • Storage Layer (Data Lake or Data Warehouse)
  • Processing Layer (batch and real-time analytics)
  • Analytics & Visualisation Layer (BI dashboards, AI insights)
  • Governance & Security Layer (compliance, access control, monitoring)

Data Lakes eliminate data silos, reduce duplication, and enable seamless access to high-quality data across teams. They make it easier for organisations to perform advanced analytics, predictive modelling, and AI-driven decision-making.

We implement enterprise-grade security measures, including encryption, access control, identity management, and compliance with GDPR and regional data protection laws. Continuous monitoring and auditing ensure your data stays secure at every stage.

The timeline depends on your data volume, complexity, and existing infrastructure. Typically, a proof of concept (PoC) can be delivered in a few weeks, while full enterprise-scale implementations may take a few months, including integration, optimisation, and governance setup.

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Let's Shape Your Vision Together!


Ready to discuss your next digital transformation project? Our experts are here to help you plan, design, and engineer solutions built for scale and performance.

What Happens Next?

1

Consultation

Share your idea, and our team will schedule a discovery call to understand your goals and challenges.

2

Solution Blueprint

Receive a tailored technology roadmap outlining architecture, tools, and timelines to bring your vision to life.

3

Onboarding

Once aligned, our engineers integrate seamlessly with your team to execute and accelerate delivery.

Send us an email at

sales@cloudesign.com

Let’s Discuss Your Project


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