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275 questions, 991 terms and 600 topics in 30 areas.
21 results for “Warehouse, Lake & Lakehouse”
Data Lakehouse
A pattern that puts warehouse-style transactions, schema and governance on top of cheap open-format object storage.
Workload Fit Assessment
Choosing between warehouse, lake and lakehouse by the workloads that must run, rather than by which one is currently fashionable.
Open Table Format
A metadata layer over files in object storage that supplies ACID transactions, schema evolution and time travel — the thing that turns a data lake into a lakehouse.
Lakehouse Table Format
A metadata layer over object storage that adds atomic commits, snapshots and schema evolution to files that otherwise have none.
Warehouse Concurrency Scaling
Adding compute clusters to absorb concurrent queries rather than queueing them, and the cost behaviour that turns a queue into a bill.
ETL vs ELT
Whether data is transformed before loading into the target or after it, which decides where the compute happens and how much raw history you keep.
Operational Data Push
Sending modelled analytical data back into operational tools, which turns a warehouse table into a production dependency with none of the guarantees.
Report Migration Inventory
The enumerated list of every report, extract and downstream consumer of the legacy warehouse, with usage evidence, which is what makes the migration finite.
Schema Evolution
Changing a table's structure over time while keeping existing data readable and existing consumers working.
Slowly Changing Dimension
A strategy for handling attributes that change over time, deciding whether history is preserved and how facts attach to the correct version.
Star Schema
A dimensional model with one central fact table of measurements surrounded by denormalised dimension tables describing them.
Sub-Second Aggregation Store
A database built to ingest continuously and answer aggregate queries over recent data in milliseconds, occupying the gap between OLTP and the warehouse.
Leadership wants to consolidate a warehouse, a data lake and three departmental marts into a lakehouse. How do you scope and sequence this?
Establish the actual driver "Consolidate onto a lakehouse" is a solution. The driver is usually one of: cost of maintaining several copies, inconsistency betwee
You are asked to choose between a cloud data warehouse and a lakehouse for a new analytics platform. How do you decide?
What actually differs The gap has narrowed considerably, so the decision turns on fewer things than the marketing suggests. Format ownership. In a lakehouse, da
A CDC pipeline feeding your warehouse falls three hours behind during a source system's batch job, and the source's transaction log retention is 24 hours. What is the risk and what do you change?
The immediate risk Lag consumes the retention window. At three hours behind against a 24 hour retention, you have 21 hours of margin. If the consumer stops enti
A team is choosing between Iceberg, Delta and Hudi for a new lakehouse. How would you approach the decision?
The formats are more alike than the debate suggests All three provide the same core: atomic commits over object storage, snapshots and time travel, schema evolu
A warehouse table now feeds the CRM through reverse ETL. It breaks, and the analytics team and the CRM team each say it is the other's problem. How do you resolve it?
The dispute is a design gap, not a personality problem A warehouse table built for analysis — nightly latency, best effort availability, schema changes made by
Your platform is built on an append-only event log with a lakehouse behind it. Legal asks how you will satisfy erasure requests within 30 days. What is your answer?
Establish the actual scope first Erasure applies to personal data held about the subject, and it has exceptions — data retained under a separate legal obligatio
A finance report double-counts revenue after a new fact table is added. What is the likely modelling error?
The likely error: a fan out join between fact tables at different grains The classic mechanism. You have an order lines fact at line grain and a shipments fact