DataWeave Deep Dive (Course #dataweave)
For all developers.
By the end, you can
By the end of this course, you will be able to architect complex DataWeave 2.x transformations — streaming, group/join, custom modules — so that 20 k-event bursts process on a flat heap and clear review first time.
Running case study
Nordwind Logistics streams telematics from 40,000 vehicles: legacy EDIFACT and CSV feeds land in 20 k-events/s bursts and must exit as versioned JSON webhooks at a target p95 under 100 ms per transform. Carriers reshuffle field layouts weekly, so mappings evolve without downtime. Each module rebuilds one leg of this pipeline in DataWeave 2.x.
Programme every line is something you do
5 modules · 15 topics · in course-flow order
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Module 1
Inside the DataWeave Engine
Objective. Predict evaluation outcomes of laziness, coercion and selectors, then debug a failing webhook mapper within minutes.
- Trace evaluation order: lazy evaluation, map/mapObject/mapArray semantics, recomputation costs
- Master types, coercion and defaulting: Number vs String traps, DateTime formatting with periods and locales
- Debug scripts fast: breakpoints, previews and variable inspection in Studio's DataWeave editor
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Module 2
Nested Maps, Joins & Reductions
Objective. Compose nested mappings, groupBy joins and reductions merging vehicle positions with route plans on 100 k-record sets.
- Build nested mappings and accumulator-style reduce over shipment trees
- Join datasets by key: groupBy + filter composition standing in for SQL joins across 100 k records
- Absorb big payloads: streaming readers and chunked processing to hold heap during 20 k-events/s bursts
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Module 3
XML, CSV & Flat Files
Objective. Map losslessly between namespaced XML, hostile CSV, fixed-width EDIFACT segments and stable JSON webhooks.
- Tame XML: namespaces, attributes, mixed content, CDATA-safe output for customs messages
- Pick JSON strategies: key ordering, null suppression, schema-stable output for webhook consumers
- Crush flat-file edges: quoted commas, embedded newlines, header/footer trailers in fixed-width feeds
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Module 4
Reusable DataWeave Libraries
Objective. Package Nordwind's canonical mapping functions as a versioned library consumed by three projects.
- Author importable modules: exports, function overloading, src/main/resources/libs layout
- Distribute libraries via Maven dependencies or Exchange assets pinned with semantic versions
- Regression-test transforms: golden-sample assertions wired into the Maven build
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Module 5
Review-Proof Transformations
Objective. Institute a review checklist covering naming, readability and performance that blocks regressions at merge time.
- Standardize naming and readability: pure functions, explicit types, zero magic strings
- Benchmark performance traps: repeated scans, deep recursion, eager reads — before/after on 10 MB samples
- Gate reviews with a transform checklist: metadata declared, edge cases listed, streaming verified
References & Official Documentation every claim above has a source
Module 1
- DataWeave VariablesMuleSoft Docs
- Value Constructs for TypesMuleSoft Docs
Module 2
- groupBy (dw::Core)MuleSoft Docs
- reduce (dw::Core)MuleSoft Docs
Module 3
- Flat File FormatMuleSoft Docs
- XML FormatMuleSoft Docs
Module 4
- Create Custom Modules and MappingsMuleSoft Docs
Module 5
- Streaming in DataWeaveMuleSoft Docs