Design and build cloud-native data lakes on GCS, ADLS, or S3 - with Delta Lake, Databricks, and ingestion pipelines that handle structured, semi-structured, and unstructured data at enterprise scale.
Enterprise data lakes centralise raw, semi-structured, and unstructured data from across your organisation - making it available for analytics, machine learning, and reporting without the rigidity of a traditional warehouse. But without proper zone design, governance, and ingestion pipelines, lakes rapidly become unusable.
At DynamicUnit, we architect and build data lakes on GCS, Azure Data Lake Storage, and S3 using Bronze/Silver/Gold medallion patterns, Delta Lake for ACID transactions, and Databricks for large-scale processing. We design the ingestion pipelines, cataloguing, and access controls that keep your lake clean, queryable, and governed - from launch and as it scales.
Most mature organisations use both a lake and a data warehouse - the lake for raw storage and exploration, the warehouse for curated reporting. We build both and design the integration between them so Gold-layer tables in the lake can feed directly into Power BI or Synapse for BI consumption.
Data quality in the lake depends on what enters it. Our data cleansing pipelines run inline during ingestion, and for external data sources, our data scraping team builds the extraction layer that feeds the lake on schedule.
When the lake needs to be loaded with historical data from legacy systems or ERP platforms, our data migration methodology ensures structured, validated loading with full reconciliation.
IoT sensor data, SCADA feeds, and asset telemetry centralised in a lake for predictive maintenance, production optimisation, and integration with EAM systems.
Clinical records, imaging data, and genomic datasets stored in governed lake zones - enabling ML research while meeting HIPAA and regional compliance requirements.
Transaction logs, market data, and alternative data feeds centralised for risk modelling, fraud detection, and regulatory reporting - with full audit trails and access governance.
Production telemetry, quality inspection data, and ERP extracts unified in a lake for yield analysis, supply chain optimisation, and feeding analytical warehouses.
From storage configuration and ingestion pipelines to governance and downstream serving layers - here's what we build.
Zone-based and medallion (Bronze/Silver/Gold) architecture design - ensuring raw data is preserved while clean, curated data is reliably served downstream.
Provision and configure GCS buckets, Azure Data Lake Storage Gen2, or AWS S3 with lifecycle policies, versioning, encryption, and tiered storage classes.
Implement Delta Lake on top of your cloud storage for ACID transactions, schema enforcement, time travel, and reliable upserts on large datasets.
Build and deploy Databricks notebooks, jobs, and Unity Catalog configurations for large-scale Spark processing and centralised governance.
Design and build batch and streaming ingestion pipelines from ERP, IoT, APIs, and databases - with schema validation, error handling, and retry logic.
Implement data catalogues (Purview, Dataplex, Glue) with schema documentation, lineage tracking, and business glossary for discoverability.
Configure IAM roles, attribute-based access control, encryption at rest and in transit, and data classification policies to meet compliance requirements.
Expose Gold-layer tables to BigQuery, Synapse, or Databricks SQL for consumption by BI tools, data scientists, and downstream applications.
Most data lake failures aren't technical - they're architectural. Poor zone design, missing governance, and ungoverned ingestion pipelines turn a lake into an unusable mess within months. Here's how we prevent that.
We design the zone model, data flow, and governance framework before building anything - ensuring the foundation is correct and extensible.
Access controls, data classification, and cataloguing are built into the initial design - not added retrospectively when an audit finds a problem.
We use Delta Lake to provide ACID guarantees, schema enforcement, and time travel - so your lake doesn't corrupt silently when pipelines fail mid-run.
Every ingestion pipeline includes data quality checks, schema validation at ingest, error logging, and monitoring - preventing garbage from entering the lake.
We implement lifecycle policies, storage tiering, and Databricks cluster auto-termination to keep cloud storage and compute costs predictable and justified.
We provide SLA-backed support for lake environments - covering pipeline monitoring, schema evolution, cluster management, and capacity planning.
We audit your data sources, analytical use cases, and compliance requirements. You get a lake architecture document covering zone design, storage platform, governance model, and ingestion strategy.
We provision storage, configure Delta Lake, build ingestion pipelines from your source systems, and implement data quality checks at each zone transition.
We implement the data catalogue, access policies, and classification tags. Historical data from legacy systems is migrated into the Bronze zone with full reconciliation.
Pipelines go live with monitoring, alerting, and cost tracking. We hand over documentation, train your data engineers, and transition to SLA-backed managed support for ongoing operations.
Tell us your data volumes, source systems, and analytical goals - we'll show you what the right architecture looks like for your organisation.
