Enterprise Data Platforms

Enterprise Big Data &
Data Engineering Services

Turn fragmented enterprise data into a trusted, scalable and AI-ready data foundation.

Mobiloitte designs and engineers modern data platforms that connect structured, semi-structured and unstructured information across applications, cloud environments, databases, events and enterprise systems. From batch and real-time pipelines to lakehouse architecture, analytics, governance and AI-ready data products, we help organizations make data easier to access, trust, analyze and operationalize.

Request a Data Architecture Assessment

What Are Enterprise Big Data & Data Engineering Services?

Enterprise big data and data engineering services help organizations collect, integrate, transform, store, govern and deliver large volumes of data for analytics, applications and artificial intelligence.

Modern data engineering goes beyond storing large datasets. It creates reliable pipelines and platforms that connect batch and streaming data, cloud and on-premise systems, structured and unstructured information, analytics workloads and AI applications.

The objective is to make enterprise data accessible, trusted, governed and useful at the point where decisions or digital workflows require it.

Enterprise Data Engineering in Practice

Mobiloitte supports organizations building and modernizing software, data, cloud and intelligent digital platforms across diverse business environments.

Confidential Financial Services Client

Business ChallengeFragmented customer data across legacy on-premise databases slowing down risk analysis and customer insights.
Data SolutionCloud-native Lakehouse Architecture.
ArchitectureAWS, Snowflake, Apache Spark, Airflow.
Mobiloitte's WorkEngineered scalable ELT pipelines, implemented strict data governance controls, and centralized analytics.
OutcomeReduced reporting times from days to hours and enabled self-service analytics.

Confidential Logistics Provider

Business ChallengeInability to track shipments in real-time leading to supply chain inefficiencies.
Data SolutionReal-time Streaming Data Pipelines.
ArchitectureApache Kafka, Flink, DataBricks, IoT Sensors.
Mobiloitte's WorkBuilt a robust streaming architecture processing millions of IoT events per second for live fleet tracking.
OutcomeImproved delivery predictability and optimized route planning.

Confidential Retail Enterprise

Business ChallengePoor data quality and siloed inventory information hindering AI-driven personalization.
Data SolutionAI-Ready Data Products & Governance.
ArchitectureGoogle Cloud, BigQuery, dbt, Vector Databases.
Mobiloitte's WorkModernized legacy warehouses into structured data domains with clear lineage, metadata, and quality SLAs.
OutcomeAccelerated deployment of personalized recommendation engines and AI assistants.

Enterprise Data Engineering
& Analytics Capabilities

01.

Data Strategy & Architecture

Start with the business outcomes, data landscape and operating requirements before selecting platforms. We assess sources, use cases, existing lakes, AI requirements, security, and governance.

  • Current-state assessment
  • Target data architecture
  • Platform recommendations
  • Data-domain map
  • Governance requirements
  • Implementation roadmap
Deliverables provide a clear path forward before heavy engineering begins.
02.

Data Engineering & Pipeline Development

Build reliable pipelines that move and transform data across enterprise systems. The objective is not simply moving data, but making it reliable and discoverable.

  • ETL & ELT
  • Batch processing
  • Streaming pipelines
  • CDC
  • Data transformation
  • API & File ingestion
  • Pipeline orchestration
  • Error handling
Pipelines should make data usable by downstream applications, analytics and AI.
03.

Modern Data Platforms & Lakehouse

Build scalable data foundations across cloud, hybrid or suitable on-premise environments based on workload, latency, governance, and cost.

  • Data lakes
  • Cloud data warehouses
  • Lakehouse platforms
  • Domain-oriented data platforms
  • Data marts
  • Analytics platforms
A lakehouse is one architecture option—not a mandatory replacement for every warehouse or data lake.
04.

Real-Time Data & Streaming

Process events and operational information as they happen. Useful for fraud detection, operational monitoring, and IoT.

  • Event streaming
  • Kafka-based architectures
  • Stream processing
  • CDC pipelines
  • Real-time ingestion
  • Streaming analytics
Real-time architecture should be used when the business genuinely needs low-latency information.
05.

Data Modernization & Cloud Migration

Modernize aging warehouses, Hadoop estates and fragmented data platforms. Migrate what continues to create value.

  • Legacy warehouse migration
  • Hadoop modernization
  • Cloud migration
  • ETL-to-ELT transformation
  • Schema migration
  • Metadata migration
Do not lift and shift technical debt. Identify redundant datasets and obsolete pipelines first.
06.

Data Quality, Governance & Metadata

AI and analytics are only useful when users can understand and trust the underlying data. Governance should operate throughout the data lifecycle.

  • Data classification
  • Ownership & Stewardship
  • Metadata & Cataloging
  • Lineage
  • Quality rules
  • Access policies & Masking
Governance answers: Who owns this data? Where did it come from? Who should access it?
07.

Data Products & Data Mesh

Treat high-value datasets as reusable products with clear ownership, quality expectations and consumers.

  • Defined business purpose
  • Documented schema
  • Quality expectations
  • APIs or query interfaces
  • Lineage & Monitoring
Data mesh should only be introduced when the organization's scale and domains justify the complexity.
08.

Business Intelligence & Analytics

Turn trusted enterprise data into useful business intelligence that connects clearly to the decisions it is intended to improve.

  • Executive dashboards
  • Operational reporting
  • KPI frameworks
  • Self-service analytics
  • Predictive analytics
  • Financial analytics
Analytics should empower users without creating unmanaged data copies.
09.

AI-Ready Data Engineering

Prepare enterprise information for machine learning, generative AI, RAG and AI agents.

  • Structured-data preparation
  • Unstructured-data pipelines
  • Document processing
  • Embeddings & Vector databases
  • Hybrid retrieval
  • Feature engineering
AI-ready data requires business context—not simply larger volumes of data.
10.

DataOps & Platform Operations

Operate data platforms as production systems. Reliable data delivery requires engineering discipline similar to production software.

  • Pipeline CI/CD
  • Automated testing
  • Data-quality monitoring
  • Schema-change monitoring
  • Pipeline observability
  • Incident alerts
  • Infrastructure automation
Ensures uptime, reliability, and cost-visibility for data workloads.
Discuss Your Data Requirements

Architecture of a
Modern Enterprise Data Platform

Data Sources

Applications • Databases • SaaS platforms • Files • Documents • APIs • IoT devices • Events • Logs • External data

Ingestion & Integration

Batch ingestion • Streaming • CDC • APIs • Connectors • File ingestion

Processing & Transformation

ETL / ELT • Data cleansing • Validation • Enrichment • Business transformations • Streaming processing

Storage & Platform

Data warehouse • Data lake • Lakehouse • Operational stores • Object storage

Serving & Data Products

SQL • APIs • Semantic layers • Data products • Feature stores • Vector stores

Consumption

BI & dashboards • Operational applications • Analytics • Machine learning • Generative AI • AI agents

Across Every Layer

Data Quality • Metadata • Lineage • Security • Access Control • Governance • Observability • Cost Management

Enterprise Data Engineering Process

From Fragmented Data to Production-Ready Data Products.

01

Discover

Identify: Business objectives, Users, Data sources, Existing platforms, Analytics requirements, AI requirements, Pain points, Success metrics

02

Assess

Evaluate: Data quality, Volumes, Velocity, Formats, Dependencies, Existing warehouses/lakes, Pipelines, Security, Governance, Platform cost

03

Architect

Define: Target architecture, Storage strategy, Processing model, Batch vs streaming, Integration, Data contracts, Governance, Security, Cloud/platform design

04

Engineer

Build: Pipelines, Transformations, Data models, APIs, Streaming systems, Storage layers, Data products

05

Validate

Test: Data completeness, Accuracy, Schema consistency, Pipeline behaviour, Performance, Access controls, Recovery workflows

06

Activate

Connect trusted data to: Analytics, Dashboards, Applications, Machine learning, RAG, AI agents

07

Operate

Monitor: Pipeline reliability, Data quality, Latency, Schema changes, Cost, Access, Infrastructure health

08

Optimize

Continuously improve: Performance, Data quality, Architecture, Cost, Governance, User adoption

Modern Data Engineering
Technology Ecosystem

Technology selection should follow workload, architecture, governance, performance and operating requirements.

We modernize around the enterprise's actual data estate rather than forcing one platform onto every workload.

Data Processing

Apache Spark, Apache Flink, Appropriate distributed processing frameworks

Streaming & Messaging

Apache Kafka, Event-streaming technologies, Cloud-native event services

Data Platforms

Databricks, Snowflake, Cloud-native warehouses, Lakehouse architectures, Existing Hadoop ecosystems where required

Cloud Data Services

AWS, Microsoft Azure, Google Cloud

Storage

Object storage, Data lakes, Data warehouses, Lakehouses, Operational databases

Transformation

SQL, Python, Appropriate ETL/ELT tools, Workflow orchestration platforms

Analytics

BI platforms, SQL analytics, Python analytics, Machine-learning platforms

AI & Retrieval

Vector databases, Embedding pipelines, Enterprise search, Hybrid retrieval, AI-ready data services

Governance & Metadata

Cataloging, Lineage, Data-quality tooling, Access management, Data observability

Legacy Ecosystems

Where existing environments require them: Hadoop, HDFS, Hive, Impala, Spark

Make Enterprise Data
Ready for AI

AI cannot reliably use enterprise information simply because the data exists. An AI-ready data foundation makes enterprise information easier to discover, interpret, secure and use within controlled AI workflows.

Structured Data

Prepare transactional and operational data for analytics, prediction and AI tools.

Unstructured Data

Process documents, PDFs, text and other knowledge sources for enterprise search and RAG.

Business Context

Add metadata, definitions and semantic context so AI systems understand what information represents.

Retrieval

Create search, vector and hybrid-retrieval layers where AI applications require enterprise knowledge.

Permissions

Preserve data-access requirements when information is exposed through AI interfaces.

Quality

Evaluate completeness, freshness, relevance and reliability before data is used in production AI systems.

Assess Your AI Data Readiness

Data Governance,
Security & Trust

Make Data Usable Without Losing Control

Data Ownership

Define accountable owners and stewards for important data domains.

Data Classification

Identify sensitive, personal, financial, confidential and business-critical information.

Access Control

Apply role- and policy-based access according to business requirements.

Metadata & Catalog

Help users understand what data exists and what it represents.

Lineage

Trace important information from source through transformations to downstream consumption.

Data Quality

Define measurable quality requirements and monitor deviations.

Data Protection

Apply appropriate masking, encryption, retention and security controls.

Governance for AI

Extend governance into the AI lifecycle where data feeds models, RAG systems or agents.

Compliance Support

Data architecture and controls can be mapped to applicable privacy, security and industry requirements according to the client's environment.

Data Quality & Observability

Know When Your Data Stops Being Trustworthy. Modern data platforms should detect failures before unreliable information reaches reports, applications or AI systems.

Pipeline Reliability

  • Success
  • Failures
  • Retries
  • Dependencies

Data Quality

  • Completeness
  • Validity
  • Accuracy
  • Uniqueness
  • Freshness

Schema Changes

  • Detect changes that may break downstream consumers.

Data Freshness

  • Identify late or stale datasets.

Volume Anomalies

  • Detect unexpected changes in record counts or event volumes.

Lineage

  • Understand which reports, applications or models may be affected.

Cost

  • Track expensive jobs, storage growth and inefficient workloads.

Move From Data Projects to
Reusable Data Products

A data product packages trusted information for repeatable use by applications, analysts and AI systems. Data-product thinking helps enterprises reduce repeated pipelines and duplicate interpretations of the same information.

Purpose

What business problem does the data solve?

Ownership

Who is responsible for quality and evolution?

Contract

What schema and semantics can consumers depend on?

Quality

What standards must the data satisfy?

Access

Who or what systems are authorized to use it?

Delivery

How can consumers access it—SQL, APIs, events or files?

Observability

How are freshness and reliability monitored?

Lifecycle

How are changes communicated and managed?

Turn Trusted Data Into
Decision Intelligence

Mobiloitte can connect enterprise data platforms with analytics experiences designed around actual business decisions.

Executive Analytics

Monitor strategic KPIs and organizational performance.

Operational Analytics

Give teams visibility into ongoing processes, assets and workflows.

Customer Analytics

Understand customer behaviour, engagement and lifecycle patterns.

Predictive Analytics

Use historical and contextual data to support forecasting and risk analysis where suitable data exists.

Self-Service Analytics

Allow authorized business users to explore trusted information without creating unmanaged data copies.

Embedded Analytics

Bring insights directly into business applications and workflows.

AI-Assisted Analytics

Use natural-language or AI-assisted interfaces where governance and data quality support reliable use.

Why Modern Enterprise
Data Platforms Fail

—and How We Engineer Around It

Data Silos

ProblemCritical information remains fragmented across business systems.
Engineering responseUnified integration architecture, APIs, pipelines and domain-oriented data products.

Low Data Trust

ProblemDifferent teams report different numbers for the same metric.
Engineering responseQuality rules, lineage, metadata, ownership and governed semantic definitions.

Slow Data Delivery

ProblemNew datasets require long engineering cycles.
Engineering responseReusable pipeline patterns, DataOps automation, self-service platforms and data contracts.

Uncontrolled Cloud Cost

ProblemData workloads scale without visibility into consumption.
Engineering responseWorkload monitoring, platform optimization and cost-aware architecture.

Legacy Data Technology

ProblemHadoop, aging warehouses or custom ETL platforms create technical constraints.
Engineering responseProgressive modernization rather than blind lift-and-shift migration.

AI Data Readiness

ProblemAI teams cannot reliably discover or use enterprise information.
Engineering responseGoverned, contextualized and AI-ready data architecture.

Measure the Data Outcomes
That Matter

Do not measure success only by terabytes processed or pipelines created.

Data Reliability

  • Pipeline success
  • Freshness
  • Data-quality pass rate
  • Incident rate

Data Delivery

  • Time to onboard a source
  • Pipeline development time
  • Data-product delivery cycle

Platform Performance

  • Query latency
  • Processing duration
  • Streaming latency
  • Concurrency

Cost Efficiency

  • Compute cost
  • Storage cost
  • Cost per workload
  • Legacy platform retirement

Data Adoption

  • Active users
  • Self-service usage
  • Data-product consumption
  • Dashboard adoption

Governance

  • Catalog coverage
  • Lineage coverage
  • Policy compliance
  • Sensitive-data classification

AI Readiness

  • AI-accessible datasets
  • Retrieval quality
  • Knowledge freshness
  • Context coverage
  • Permission accuracy

Business Outcomes

  • Decision cycle time
  • Operational efficiency
  • Customer outcomes
  • Revenue or cost impact
Define Your Data Transformation KPIs

Choose the Right Starting Point

Data Architecture Assessment

For organizations with fragmented data platforms or unclear modernization priorities.

Outcome: current-state assessment, target architecture and roadmap.

Data Modernization Assessment

For organizations running aging warehouses, Hadoop or legacy ETL estates.

Outcome: modernization strategy, migration priorities and phased roadmap.

Data Engineering Pilot

For organizations that want to validate one high-value pipeline, streaming or analytics use case.

Outcome: production-oriented pilot and scale recommendation.

Enterprise Data Platform Engineering

For organizations building a modern warehouse, lake, lakehouse or domain data platform.

Real-Time Data Engineering

For organizations that require event-driven and streaming architectures.

AI Data Readiness Assessment

For organizations preparing data for ML, GenAI, RAG or agentic applications.

Outcome: data-quality, governance, retrieval and architecture recommendations.

Dedicated Data Engineering Team

For continuing pipeline, platform, analytics and modernization work.

Managed Data Platform Operations

For organizations requiring ongoing reliability, observability, optimization and governance support.

Frequently Asked
Questions

Mobiloitte can provide data strategy, data engineering, ETL/ELT, streaming, data platforms, data warehouses, data lakes, lakehouse architecture, data modernization, analytics, governance and AI-ready data engineering based on project requirements.
Big data generally refers to datasets and workloads whose volume, velocity or complexity require scalable processing approaches. Data engineering is the broader discipline of designing the pipelines, platforms, transformations and controls that make data usable by applications, analytics and AI.
An enterprise data platform connects ingestion, processing, storage, governance and consumption capabilities so trusted data can be used across analytics, applications and AI workloads. The architecture may include warehouses, data lakes, lakehouses, operational stores or combinations of these.
A lakehouse combines characteristics of data lakes and data warehouses to support large-scale data storage with analytics, governance and other structured platform capabilities. It is one architecture option and should be selected according to workload requirements.
No. Hadoop remains relevant in some existing enterprise data estates, but modern architectures can use cloud data platforms, lakehouses, managed warehouses and streaming services depending on the business and technical requirements.
Yes. A modernization assessment can identify existing datasets, pipelines, dependencies and workloads and determine what should be retained, migrated, redesigned, consolidated or retired.
Real-time or streaming processing handles events as they arrive rather than waiting for a scheduled batch. It is useful where business decisions or systems need information with low latency.
Yes. Depending on the use case, pipelines can process structured, semi-structured and unstructured sources across databases, APIs, files, events, logs and enterprise documents.
Data-quality controls can validate characteristics such as completeness, freshness, validity, consistency and uniqueness while monitoring pipeline and schema changes.
Data observability monitors the health and reliability of data pipelines and datasets by tracking signals such as failures, freshness, schema changes, volume anomalies and lineage.
Yes. AI-ready data engineering can include data integration, quality improvement, metadata, unstructured-data processing, retrieval pipelines, vector databases, permissions and governance depending on the AI use case.
A data product is a reusable, managed data asset designed for defined consumers and use cases with clear ownership, access, quality and delivery expectations.
Data architecture can incorporate appropriate modern cloud data platforms according to the client's existing environment, licensing, workload, governance and technical requirements. Technology selection should follow the use case rather than assuming one platform is appropriate for every organization.
Security can include encryption, identity and access controls, masking, classification, audit logging and appropriate data-governance controls based on the data and application requirements.
There is no universal timeline. Duration depends on the number of sources, data volumes, pipelines, legacy technologies, migration scope, data quality, integrations, governance and validation requirements. A data architecture or modernization assessment should establish the implementation roadmap.
Success should be measured using agreed reliability, delivery, performance, cost, governance, adoption, AI-readiness and business KPIs rather than only the amount of data processed.
Data Engineering • Lakehouse • Streaming • Analytics • Governance • Data Products • AI-Ready Data

Turn Enterprise Data Into a Foundation for Analytics & AI

Move beyond fragmented pipelines, aging data infrastructure and disconnected reporting. Mobiloitte can help you understand your current data estate, design the right target architecture and build a trusted foundation for analytics, intelligent applications and enterprise AI.

Request a Data Architecture AssessmentDiscuss Your Data Challenge