--- title: Big Data Engineering | Data & AI Solutions | Claritas One description: Big Data Engineering — production ML, analytics and AI capabilities delivered by the Claritas One data practice. url: https://claritasone.com/solutions/data-ai/big-data-engineering canonical: https://claritasone.com/solutions/data-ai/big-data-engineering kind: solution source: https://claritasone.com/solutions/data-ai/big-data-engineering author: Claritas One datePublished: 2016-01-01 dateModified: 2026-04-18 updated: 2026-04-18 publisher: Claritas One --- # Big Data Engineering *Solutions / Data & AI* > We architect and deliver petabyte-scale data platforms that process, govern, and serve data across your enterprise with the latency, lineage, and compliance controls that regulated industries require. From real-time streaming pipelines to enterprise data lakehouses, our engineering practice builds the data infrastructure that AI and analytics strategies depend on. [Home](https://claritasone.com/) › [Solutions](https://claritasone.com/solutions) › [Data & AI Solutions](https://claritasone.com/solutions/data-ai) › **Big Data Engineering** ## Overview Data platform failures are the silent killer of enterprise AI strategies. Organisations invest in data science teams, ML tooling, and analytics platforms only to find that the underlying data infrastructure cannot provide the data quality, availability, and lineage required for production workloads. Data arrives late. Schemas drift unexpectedly. Lineage is untracked. Regulatory audits cannot be answered. Our big data engineering practice is built around the principle that data infrastructure is a product with SLAs, not a utility that operates on a best-effort basis. Every pipeline we build ships with quality contracts, lineage tracking, and operational monitoring that gives your data consumers the reliability they need to build critical systems on top. ## Our Approach ### 1. Data Architecture Assessment & Lakehouse Design We assess your current data landscape — sources, volumes, latency requirements, and consumer patterns — and design a modern data lakehouse architecture on Delta Lake, Apache Iceberg, or Apache Hudi that unifies batch and streaming workloads while maintaining ACID transaction guarantees at petabyte scale. ### 2. Ingestion Pipeline Engineering We build high-throughput ingestion pipelines that capture data from operational databases, SaaS APIs, event streams, and third-party feeds — with schema evolution handling, exactly-once delivery guarantees, and ingestion SLA monitoring that alerts before downstream consumers are impacted. ### 3. Real-Time Streaming Architecture Apache Kafka, Apache Flink, and Spark Structured Streaming are implemented to deliver sub-minute data freshness for operational analytics, fraud detection, and customer-facing personalisation use cases — with topic partitioning strategies and consumer group management designed for long-term operational stability. ### 4. Data Quality, Governance & Lineage We implement data quality frameworks using Great Expectations or Soda Core, column-level lineage tracking with OpenLineage, and a data catalogue integration with Apache Atlas or DataHub — giving your data governance team the controls required to satisfy GDPR, CCPA, and sector-specific data regulations. ### 5. Performance Optimisation & Cost Engineering We apply file format optimisation (Parquet, ORC), partition pruning, Z-ordering, and cluster auto-scaling strategies to reduce query costs and improve performance by 3-10× versus unoptimised architectures — with ongoing cost anomaly monitoring and automated rightsizing recommendations. ## Capabilities - Delta Lake, Apache Iceberg, and Apache Hudi lakehouse architecture - Apache Spark ETL engineering with performance tuning and cost optimisation - Real-time streaming with Kafka, Flink, and Spark Structured Streaming - Schema evolution management and exactly-once delivery guarantees - Column-level data lineage tracking with OpenLineage and DataHub - Data quality framework implementation with automated SLA alerting - GDPR and CCPA-compliant data lifecycle management and deletion workflows - Cloud cost engineering with automated rightsizing and anomaly detection ## Outcomes | Metric | Value | | --- | --- | | Data volume managed across client platforms | **10PB+** | | End-to-end streaming latency in production deployments | **< 60s** | | Average cloud infrastructure cost reduction post-optimisation | **40%** | | Data pipeline SLA uptime in managed engagements | **99.9%** | ## Next Step **Build the Data Foundation Your AI Strategy Depends On** Our data architects will assess your current infrastructure and design a scalable, governed data platform aligned to your analytics and AI roadmap. → [Get a proposal](https://claritasone.com/get-a-proposal) · [Contact us](https://claritasone.com/contact) --- View the live page: About Claritas One: · Contact: · All pages: