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Pig is a high-level platform for creating MapReduce programs used with Hadoop.

Year/Make/Model fitment search for Shopify. 8 verticals, Smart Parse, and your data in Shopify metaobjects — not a vendor database. Free tier, Pro at $49.

Which is more popular?
Based on our record, Apache Pig seems to be more popular. It has been mentioned 2 times since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | pig.apache.org | normalview.pro |
| Pricing | — | |
| Platforms | — | |
| Company | — | Startup from the United States · 1 - 9 employees · 2026 |
| Listed in |
In their own words, as submitted to SaaSHub.


No description of Apache Pig yet.
ViewForge is a Year Make Model (YMM) parts finder for Shopify. Shoppers pick their vehicle, machine or device from cascading dropdowns and see only the parts that fit. Fitment search works across eight verticals — auto, motorcycle, tractor, marine, power equipment, bicycle, printer and...
What each product offers, as listed by its team.


Possible disadvantages
An editorial look at what each product does well and who it suits.


Overall verdict
Why this product is good
Recommended for
Apache Pig is recommended for data engineers and analysts who are working in Apache Hadoop environments and need to perform ETL (Extract, Transform, Load) operations on large datasets. It is also suitable for teams looking to leverage existing Hadoop infrastructures without delving into complex Java MapReduce programming or when migrating legacy processing scripts based on Pig Latin.
No analysis of ViewForge yet.
Walkthroughs and reviews on video.
Pig Tutorial | Apache Pig Script | Hadoop Pig Tutorial | Edureka
More videos
ViewForge: YMM Search & Filter
How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing Apache Pig and ViewForge.
ViewForge's answer:
Three things. (1) Data ownership: ViewForge writes fitment as Shopify metaobjects native to your store — most competitors store fitment in their own database. (2) 8 verticals out of the box: auto, motorcycle, tractor, marine, power equipment, bicycle, printer, electronics — most competitors are automotive-only. (3) Smart Parse: extract fitment automatically from your existing product titles and descriptions instead of re-typing everything.
ViewForge's answer:
Data ownership. Fitment lives in your Shopify metaobjects, so uninstalling does not take your compatibility data with it. Convermax, EasySearch and PartFinder all keep it in their own databases, and getting it back depends on their export tooling on the day you cancel.
Cost at the low end. The search widget, the compatibility table on the product page and the saved-vehicle garage are all on the free tier, up to 50 products, with no expiry. EasySearch puts the table and the garage behind its $75/month Premium plan. Convermax starts at $250/month.
Automotive and non-automotive coverage. Eight built-in templates, and fully custom templates from $19/month, for catalogs that do not decompose into Year/Make/Model at all.
ViewForge's answer:
Shopify merchants whose customers need to know whether a part fits before they will buy it — and who do not have an engineer on staff to build that themselves.
Concretely: auto and truck parts retailers, powersports and motorcycle dealers, tractor and agricultural parts sellers, marine and outboard suppliers, small-engine and power equipment stores, bicycle and e-bike component shops, printer supply merchants, and electronics accessory sellers.
Catalog sizes run from a few dozen products on the free tier up into the tens of thousands; it is running in production on a catalog of roughly 40,000 SKUs. The common thread is not the industry — it is that "does this fit my thing" is the question deciding the sale.
ViewForge's answer:
ViewForge came out of agency work. Normal View was building for a parts retailer running roughly 12,000 SKUs who needed fitment search, and every app we evaluated stored the merchant's compatibility data in the vendor's own database.
That is a strange trade when you look at it directly. Fitment data is genuinely expensive to produce — it is weeks of work — and the merchant would not own the result. It would belong to whichever app happened to be installed that year.
Shopify metaobjects made a different answer possible: write fitment as native structured data inside the merchant's own store. The theme reads it, the Storefront API queries it, Admin GraphQL exports it, and it is still there after an uninstall. That decision is what the rest of the app is built around.
Everything else came from real catalogs rather than a roadmap. Eight verticals exist because a tractor catalog is not Year/Make/Model. Smart Parse exists because that retailer had already written fitment into 12,000 product titles, and nobody was ever going to retype them.
ViewForge's answer:
Share your experience with using Apache Pig and ViewForge. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / almost 4 years ago
In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark.... - Source: dev.to / almost 5 years ago
Tracking ViewForge since Aug 2026.
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