Newark (element14) Product Catalog Data

It is the Americas shelf of the electronics trade, turned into rows. Newark is the North American brand of the Farnell/element14 global distribution group - now part of Avnet - serving electronics design, maintenance and repair buyers across the United States, Canada and Mexico. Storefront categories embedded in the index span roughly 430 categories: connectors, passive components, semiconductors, switches and relays, circuit protection, cable and wire assemblies, power supplies, transformers, automation and process control, test and measurement, and an Electrical category covering power distribution and industrial electrical product.

Scale is measured, not asserted. Category counts read off the storefront in a single research pass put more than 1.5 million connector SKUs, 352,695 fixed-value resistors, 130,180 cable assemblies and 72,404 switch and relay lines on the shelf, while the group channel exposes over 1.3 million products across the Newark, Farnell and element14 storefronts combined. Every listing carries brand, part number, pack and availability detail, so breadth becomes something you can slice rather than scroll.

Datadory packages that depth as a deliverable: one row per SKU normalized into the field dictionary below, delivered daily, weekly, or hourly. Get a sample of this dataset scoped to the categories you actually buy.

One component row, shaped as delivered:

# one catalogue row - the verified sample line, captured exactly
sku                    : 3263767
displayName            : ABB - E93/125S - DIN RAIL FUSE HOLDER, 3P, 125A, 600V
manufacturerPartNumber : E93/125S
brand                  : ABB
productStatus          : STOCKED            packSize            : 1
unitOfMeasure          : EA                 minimumOrderQty     : 1
stock.level            : 4                  # units on the shelf at capture

# price breaks travel as a ladder - from quantity, to quantity, unit cost
prices[0]              : {"from": 1, "to": 999999999, "cost": 1.44}

# multi-site parts split stock by warehouse; the row keys never move
stock.breakdown        : [{"inv": 4, "region": "<warehouse region>"}]
stock.leastLeadTime    : 98                 # shortest lead time across warehouses, days

Read the anatomy rather than the illustrative values. displayName compresses three joins into one string - brand, manufacturer part number and plain-language description - which is why the parsed brandName and translatedManufacturerPartNumber columns ride beside it on every row. The prices field arrives as a ladder, not a number: from-quantity, to-quantity and unit cost per step, so order-size arithmetic needs no second source. stock.level answers can I build this week, while stock.breakdown splits the same part across warehouses and stock.leastLeadTime states the shortest wait in days. packSize and translatedMinimumOrderQuality convert any of those unit figures into what an order actually costs. Live records ship with your sample; these show the shape.

Sixteen documented columns anchor every row, defined in the table below with worked examples. Two do most of the joining - sku inside this catalog's own numbering, translatedManufacturerPartNumber everywhere else. Three do most of the warning: stock.level, stock.leastLeadTime and stock.breakdown. prices carries the commercial reality as a break ladder, and packSize with translatedMinimumOrderQuality converts it into order truth.

Additional fields on request: fuller parametric attribute sets per category, datasheet references per part, the full storefront category tree with item counts, and cross-market slices of the same SKU across the United States, Canada and Mexico storefronts. Their exact shapes are pinned against live records when your sample is cut - which is also where column naming locks down for your pipeline.

• Geography - Americas first. The Newark brand serves the United States, Canada and Mexico through regional storefronts whose pricing and stock are kept separate, sitting inside a group footprint that spans Europe, Asia Pacific and Japan under the Farnell and element14 brands - over 1.3 million products group-wide. Regional separation is precisely why multi-market teams pull one normalized feed instead of maintaining three browser sessions.
• Temporal window - a living catalog. Stock counts and prices reflect query-moment state; nothing here is a nightly export wearing yesterday's numbers. This record sits in the realtime tier of Datadory's wider catalog - home to only 197 of 1,744 datasets - and there is no historical archive behind the storefront, so longitudinal views come from scheduled captures, which is exactly what the cadence you choose builds.
• Granularity - one record per individual SKU, nested in a deep category tree and carrying per-warehouse stock and price-break detail. That atomic grain is why the same file serves BOM pricing, shortage scanning and assortment benchmarking without restructuring between jobs.

API, files, or your warehouse. Daily, weekly, or hourly.

Pick the channel your team already works in and set the cadence to match the decision you feed. Hourly suits shortage watch, where allocation can thin a part's stock between Monday standup and Wednesday quoting. Daily suits BOM repricing and competitor price tracking across whole categories. Weekly suits quarterly benchmarking where the trend matters more than any single sticker.

Because every delivery reuses the same sixteen-column dictionary, snapshots concatenate into clean stock-and-price time series without remapping - your join keys stay stable whichever cadence you pick. Name the categories, manufacturers or part numbers you care about and the sample comes back shaped exactly like the table above.

Ranked by how directly one SKU row settles their day job:

1. Procurement and component engineers price bills of materials at observed shelf reality - price monitoring with part numbers attached, not survey averages.
2. Supply chain analysts read thinning availability across categories as an early allocation signal - mapped in supply chain mapping.
3. Competitive intelligence and product teams track rival stocking depth and price moves part by part - competitor tracking at SKU resolution.
4. Data scientists and ML engineers build demand and shortage models from stock-and-price panels keyed on manufacturer part number - ml model training on verified rows.
5. Market researchers and consultants benchmark Americas assortment depth by category - how far distribution actually runs, counted rather than estimated.
6. E-commerce operators and resellers quote against observed trade prices instead of stale list sheets, with pack size and minimum order quantity already parsed.

Component engineers and procurement teams lead the tagging here: shelf reality per part number before the purchase order goes out. Developers and builders get a stable part-number spine for BOM tools and sourcing products without maintaining a parser per distributor (developers builders use cases). Data scientists and ML engineers load stock-and-price panels as training data without an extraction project in front of them (data scientists use cases). Competitive intelligence and product teams track rival stocking week over week (competitive intel product teams use cases). Persona-by-persona detail lives on the industry pages linked throughout this record.

Inside this industry slice, the distributor catalogs answer different questions, and mixing them up is how bad component analysis gets written. The Digi-Key Electronics Product Catalog carries the deepest single-subcategory structure observed anywhere in the slice - 543,532 items in one RF coaxial branch - and the Digi-Key API (Product Information V4) is its machine interface, the only score-9 record among the industry's 22 datasets. The Farnell electronics catalog is this record's EMEA/Japan twin - same group, different storefronts - while the Avnet electronics catalog writes the parent group's two channels as one table. RS Components runs roughly 750,000-800,000 products group-wide, narrower than this channel's reach. Against the ECIA electronic component sales data indices, the division of labour holds: those series measure the room's bookings and lead times, this one measures the shelf itself.

Four things, stated up front.

First, this is a current-state catalog, not an archive. Each delivery reflects live stock, present prices and current lifecycle flags; longitudinal series exist only where someone captured them, which is why teams start a cadence before they need the curve.

Second, scope figures deserve reconciliation. Storefront category counts sum to several million line items while the group channel states over 1.3 million products - the gap is long-tail variation versus exposed coverage, and we would rather hand you both anchors than invent a total.

Third, the verified dictionary covers sixteen columns; anything deeper - parametric expansions per category, datasheet references - is pinned down against live records when we cut your sample, which is also where column naming locks for your pipeline.

Fourth, scoping is regional. Tell us which of the United States, Canada or Mexico you buy in, and the sample comes back priced and stocked the way your team actually purchases. Name the categories and the sample request does the rest.