Intelligence that works
Preparing your next perspective
Build robust, real-time data flows that keep every downstream model fed and healthy.
Trace critical datasets from source changes to the analysts, applications and models that consume them.
Trace critical datasets from source changes to the analysts, applications and models that consume them
Create reliable paths from source systems to the teams, models and decisions that depend on them.
Design ingestion, transformation and quality rules around freshness, scale and schema change
Connect operational databases, files and event streams through interfaces suited to freshness, volume and access constraints.
Define reusable transformations, data contracts and checks that expose missing, late or inconsistent records.
Design retries, backfills, lineage and monitoring so the pipeline can be operated by your team.
Move from dependency mapping through contracts and implementation to backfill and recovery exercises
Map producers, consumers, schemas and access boundaries.
Choose ingestion patterns, contracts and recovery paths.
Implement transformations and checks using representative source data.
Exercise backfills, monitor freshness and hand over the runbook.
Deliver versioned pipelines, explicit data contracts and operational knowledge alongside usable datasets
Versioned ingestion and transformation code with documented dependencies.
Agreed schemas, quality rules and ownership for important datasets.
Lineage, freshness dashboards and recovery instructions.
Test late arrivals, duplicates, missing records and upstream changes rather than only the happy path
Reconcile record counts and business aggregates across pipeline stages. Exercise replay and deduplication, test a schema change, and measure freshness under representative throughput. Define how rejected records are quarantined, corrected and reintroduced.
Make the behaviour of each boundary explicit when producers and consumers move at different speeds
Version data contracts, detect incompatible schema changes and define whether processing stops, quarantines records or continues with an explicit warning. Avoid silently dropping fields that consumers rely on.
Document checkpointing, idempotency and replay boundaries. Define who can trigger a backfill and how downstream consumers distinguish corrected data from duplicate delivery.
Use sample records, schema history and known incidents to expose assumptions early
Address an unreliable flow, establish a new data product or modernise ingestion without an all-at-once migration
Identify failure points in one existing flow and prioritise quality, freshness and recovery fixes.
Build a documented source-to-consumer pipeline with contracts, lineage and operational checks.
Move from fragile exports to managed batch or event flows through staged cutovers and reconciliation.
Give operators the lineage, replay instructions and ownership needed to investigate issues without reverse engineering the pipeline
Have the operating team trace a delayed dataset, inspect rejected records and perform a controlled recovery. Capture the steps that were difficult to discover.
Choose architecture around the consuming workflow rather than the novelty of a tool
We choose based on how quickly the consuming workflow needs data, along with cost and operational complexity.
Yes. We begin with the current architecture and identify which parts to retain, connect or improve.
Design ingestion, transformation and quality rules around freshness, scale and schema change.
Apache Spark
Apache KafkaUse sample records, schema history and known incidents to expose assumptions early
Select a critical dataset and its consumers. Provide schema samples, history and expected volumes.
Test late arrivals, duplicates, missing records and upstream changes rather than only the happy path.
Have the operating team trace a delayed dataset, inspect rejected records and perform a controlled recovery. Capture the steps that were difficult to discover.
Intelligence that works
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