Data Platform Architecture
General material on designing the analytical data estate end to end.
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advanced
A mobility platform's data team is overwhelmed by requests and every new dataset requires their involvement. What is the structural problem?
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advanced
You are the first data platform engineer at a 200-person company. The CFO's board pack is assembled in a spreadsheet from three CSV exports, the product team queries a read replica directly, and two teams have started separate warehouse trials. Your manager asks what you will do in the first quarter. Walk me through how you would answer.
2 min answer
4 terms in this topic
Analytical Estate Topology
The full picture of where analytical data lands, is transformed and is served, including the paths that bypass the intended one.
metricData Pipeline SLA
A published commitment about when data will be available and how fresh it will be, turning a pipeline into something consumers can design against.
conceptData Platform Architecture
The arrangement of ingestion, storage, transformation, serving and governance that decides whether analytical data arrives on time, means the same th…
practiceDataset Ownership Boundary
The line between a producing team's internal data and the surface it publishes - which is what makes decentralised data ownership safe rather than fr…
Neighbouring topics
Medallion Architecture
Bronze, silver and gold layers, and what each layer is allowed to guarantee.
Open Table Formats
Iceberg, Delta and Hudi — transactions, snapshots and time travel over object storage.
Warehouse, Lake & Lakehouse
Three answers to where analytical data lives, and the workloads that separate them.
Storage Layout & Partitioning
Partition keys, clustering, and the scan the query planner is left able to skip.
File Formats & Compaction
Columnar formats, the small-file problem, and the maintenance nobody schedules.
Ingestion Patterns
Full load, incremental, append-only and merge, and the source system each one suits.
CDC Pipeline Design
Building on a change stream: snapshot plus delta, tombstones, and merge into the target.
Batch Orchestration
DAGs, dependencies, retries, and the difference between a schedule and an orchestration.
Workflow Schedulers
Airflow, Dagster and their kin — where the control plane sits and what it can recover.
Transformation Frameworks
Declarative SQL transformation with tests, lineage and versioned models.
Dimensional Modelling
Facts, dimensions, grain, and the star schema's continued relevance.
Data Vault Modelling
Hubs, links and satellites, and the auditability and load parallelism they buy.
Slowly Changing Dimensions
Overwriting, versioning or timestamping attribute history, and the reporting each enables.
Analytics Cost Control
Scanned bytes, idle warehouses, and the query nobody knew was running hourly.
Workload Isolation
Keeping an analyst's query off the pipeline's compute, and both off the dashboard's.
Data Platform Tenancy
Multiple domains on shared storage and compute, with separable access and cost.
Reverse ETL
Pushing modelled analytical data back into operational systems, and who owns it then.
Data Virtualisation
Querying across sources without moving data, and the performance ceiling that imposes.
Warehouse Migration
Moving off a legacy warehouse with thousands of reports pointed at it.