Summary:Digitec Solution builds enterprise-grade Big Data platforms — modern lakehouses, real-time streaming pipelines, and governed analytics layers that make every record queryable and AI-ready. We work with data and engineering leaders modernising legacy warehouses, fragmented sources or stalled analytics programmes that need to compound, not collapse, under scale.
    Big Data Engineering

    Big Data Engineering

    We turn fragmented legacy data into modern, AI-ready platforms — lakehouses, real-time pipelines and governance, engineered to preserve every record while unlocking new value.

    10TB+ daily throughput delivered · 100% data integrity across migrations

    Modernise without losing a single record

    Decades of business logic, regulatory history and operational knowledge live inside legacy data systems. Throwing them out and starting over isn't an option — but neither is staying stuck on infrastructure that can't power modern analytics, AI or real-time decisioning. Digitec's Big Data practice specialises in the hardest part of the modernisation journey: moving you forward without breaking what works.

    We design and execute end-to-end data platform transformations — from on-premise warehouses and brittle ETL pipelines to cloud-native lakehouses on Azure Synapse, Databricks and Snowflake. Every engagement starts with a forensic audit: source profiling, lineage mapping, data quality scoring, and a reconciliation strategy that guarantees row-level integrity through the cut-over.

    Beyond migration, we build the foundations that compound value: real-time streaming pipelines with Kafka and Event Hubs, governance and cataloguing with Purview, self-serve BI in Power BI and Tableau, and AI-ready feature stores and vector layers that make every downstream ML and GenAI product faster to ship. The platform becomes a force multiplier — not a maintenance burden.

    100%
    Data integrity preserved
    70%
    Faster analytics queries
    10TB+
    Daily throughput delivered
    Capabilities

    What we do

    A full-stack practice built to solve the hardest digital problems with craft and precision.

    Data Modernisation

    Migrate from legacy ETL and on-prem warehouses to cloud-native lakehouses. Parallel pipelines and row-level reconciliation guarantee zero data loss during cut-over.

    Lakehouse & Warehousing

    Azure Synapse, Databricks, Snowflake and BigQuery — modelled, partitioned and optimised for analytics at scale. We build the dimensional models analysts actually want to query.

    Real-Time Pipelines

    Streaming ingestion with Kafka, Event Hubs and Stream Analytics that powers operational dashboards and ML features. Sub-second latency where the business needs it.

    Analytics & BI

    Power BI, Tableau and Looker dashboards built around real decision moments — not vanity metrics. Self-serve analytics that executives and operators actually use.

    Data Governance

    Cataloguing, lineage, quality and access control with Microsoft Purview, Unity Catalog or Atlan. Audit-ready governance without slowing the team down.

    AI-Ready Data Layer

    Feature stores, vector databases and serving layers tuned for ML and generative AI. Every downstream model ships faster because the data layer is already built right.

    Process

    How we work

    Every engagement follows a battle-tested rhythm — clear, collaborative, and outcome-driven.

    STEP / 01

    Discovery

    We profile your sources, map lineage and audit data quality across the estate. The output is a forensic view of where value is trapped and what it will cost to release.

    STEP / 02

    Strategy

    We design the target architecture, governance model and phased migration roadmap. Every wave has clear KPIs, reconciliation guarantees and a measurable business outcome.

    STEP / 03

    Build

    We stand up the lakehouse, build the pipelines and migrate workloads in parallel runs. Reconciliation reports prove integrity before any legacy system is decommissioned.

    STEP / 04

    Launch & Optimise

    We hand over the platform with governance, dashboards and runbooks — then stay on to tune performance, control costs and activate ML and analytics use cases on top.

    Why Digitec

    Why teams choose Digitec

    Three reasons clients pick us over consultancies, freelancers and in-house builds.

    Zero-record-loss migrations

    We've moved 10TB+ daily workloads off legacy systems with row-level checksums and parallel runs that prove integrity before cut-over. Audit teams have signed off the first time, every time.

    AI-ready from day one

    Every platform we build includes a feature store and vector layer alongside the warehouse. When you're ready for ML or GenAI, the data foundation isn't a six-month detour — it's already there.

    Engineered for cost, not just scale

    We tune partitioning, caching, file formats and compute auto-scaling so your cloud bill doesn't balloon as data grows. Most clients see 30–60% lower spend than their original quote.

    Stack

    Technologies we master

    Azure SynapseDatabricksSnowflakeApache SparkKafkaEvent HubsStream AnalyticsPower BITableauPurviewdbtAirflow

    "Digitec turned a 15-year-old data stack into a real-time AI engine — on time, on budget, with zero downtime."

    Sarah Mitchell
    CTO, FinServ Co.
    Pakistan Software Export Board
    Government-verified
    Registered with Pakistan Software Export Board
    150+ projects delivered
    Enterprise-grade & NDA-protected
    AI-native by default
    FAQ

    Common questions

    How do you guarantee data integrity during migration?+

    We run the legacy and new platforms in parallel, with automated row-level reconciliation and checksum validation between them. No legacy system is decommissioned until the reconciliation report shows zero variance over multiple cycles.

    Can you handle on-prem to cloud migrations?+

    Yes — most of our engagements move from on-prem or hybrid estates to Azure, AWS or GCP. We've migrated regulated workloads in finance, healthcare and logistics with full audit trails and zero downtime cut-overs.

    How long does a typical platform build take?+

    First production workload usually goes live in 8–14 weeks. Full multi-source migrations run 6–18 months, delivered in measurable waves so the business sees value early rather than waiting for a big-bang launch.

    Will we be locked into one cloud vendor?+

    No. We design for portability where it matters — open file formats like Delta and Iceberg, dbt for transformations, and orchestrators like Airflow. Vendor choice is based on your existing footprint and economics, not our preferences.

    Do you also build the AI and analytics on top?+

    Yes — our AI/ML practice partners on activation, from predictive models and GenAI products to executive dashboards. The data platform isn't the destination; it's the foundation for everything that compounds after it.

    Start a project

    Ready to start?

    Bring us your goal, your stack, or your roadblock. We'll spend 30 minutes mapping the fastest path to outcome — no slide deck, no pitch.

    Or email us at info@digitecsolution.com