ETL & ELT
Where transformation happens, and how much raw history you keep.
3 to work through
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intermediate
A grocery platform ingests catalogue and inventory feeds from hundreds of retailers, each with different formats, quality and update frequency. Should transformation happen before loading or after, and where should data quality be enforced?
2 min answer -
intermediate
A nightly pipeline failed halfway and the retry produced duplicate rows. Walk me through fixing this properly.
2 min answer -
advanced
A daily pipeline has been producing wrong numbers for three weeks due to a logic bug. What must be true of the architecture for the fix to be a one-day job?
3 min answer
3 terms in this topic
ETL and ELT
Whether to transform before loading or after — a choice that follows from where compute is cheap and who owns the transformation.
practiceIdempotent Pipeline
A pipeline whose task can be re-run for the same input window any number of times and produce the same result.
practicePipeline Orchestration
Coordinating the execution of data tasks by dependency rather than by clock, with retries, backfill and observability built in.
Neighbouring topics
Data Architecture
General material on structuring, storing and governing data.
Relational Modelling
Normalisation, keys, constraints and the invariants a schema enforces.
NoSQL Stores
Key-value, document, wide-column and graph — what each buys and forbids.
Indexing
Designing indexes per query shape, and paying for them on every write.
Query Optimisation
Reading a plan, fixing statistics, and finding the real bottleneck.
Transactions & Isolation
ACID, isolation levels, and the anomalies each level permits.
Replication
Primaries, replicas, lag, and synchronous versus asynchronous durability.
Partitioning & Sharding
Splitting data across machines, and the one-way door of a partition key.
Caching Strategies
Cache-aside, read-through, write-through and where each belongs.
Cache Invalidation
Stampedes, penetration, staleness windows and versioned keys.
CQRS
Separating the write model from the read models that serve queries.
Event Sourcing
Storing the change log as the system of record, and what that costs forever.
Change Data Capture
Turning a database's replication log into a stream, and its coupling risk.
Data Warehousing
Dimensional modelling, star schemas and analytical workloads.
Data Lakes & Lakehouses
Open formats on object storage with transactional metadata on top.
Streaming Data
Windowing, watermarks, late arrivals and exactly-once semantics.
Data Governance
Ownership, lineage, quality, catalogues and who may see what.
Data Lifecycle & Retention
How long data is kept, where it ages to, and how it is actually deleted.
Polyglot Persistence
Choosing a store per workload, and the operational cost of variety.