Software Alternatives & Startups

Dataiku VS ViewForge

Compare Dataiku VS ViewForge and see what are their differences

Dataiku

Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.

Rating
0 reviews
ViewForge

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.

Rating
0 reviews
Pricing
Freemium Free trial $19 / Monthly ($19/month Starter 1,000 products, custom templates, CSV import)
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Base details

Website, pricing, platforms and company facts side by side.

Dataiku
ViewForge
Website dataiku.com normalview.pro
Pricing
Freemium Free trial $19 / Monthly ($19/month Starter 1,000 products, custom templates, CSV import) Official pricing
Platforms —
Shopify
Company Startup from the United States · 500 - 999 employees · 2013 Startup from the United States · 1 - 9 employees · 2026
Listed in

About Dataiku and ViewForge

In their own words, as submitted to SaaSHub.

Dataiku
ViewForge

No description of Dataiku 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...

Read more about ViewForge

Features and specs

What each product offers, as listed by its team.

Dataiku 6 features
ViewForge 9 features
  • User-Friendly Interface
    Dataiku offers an intuitive and easy-to-navigate visual interface that allows users of all technical backgrounds to create, manage, and deploy data projects without needing extensive coding knowledge.
  • Collaborative Environment
    The platform supports collaborative work, enabling data scientists, engineers, and analysts to work together on the same projects seamlessly, sharing insights and models easily.
  • End-to-End Workflow
    Dataiku provides tools that cover the entire data pipeline, from data preparation and cleaning to model building, deployment, and monitoring, making it a comprehensive solution for data teams.
  • Integrations and Extensibility
    The platform integrates with many data storage systems, machine learning libraries, and cloud services, allowing users to leverage existing tools and infrastructure.
  • Automation Capabilities
    Dataiku offers automation features such as scheduling, automation scenarios, and machine learning model monitoring, which can significantly enhance productivity and efficiency.
  • Rich Documentation and Support
    Dataiku provides extensive documentation, tutorials, and a strong support community to help users navigate the platform and troubleshoot issues.

Possible disadvantages

  • Pricing
    Dataiku can be expensive, particularly for small businesses and startups. The cost may be a barrier to entry for organizations with limited budgets.
  • Resource Intensive
    The platform can be resource-hungry, requiring significant computing power, which may necessitate additional investments in hardware or cloud services.
  • Learning Curve for Advanced Features
    Although the basic interface is user-friendly, mastering advanced features and customizations can require a steep learning curve and significant training.
  • Limited Offline Capabilities
    Dataiku relies heavily on cloud services for many of its functionalities. This dependence might be restrictive in environments with limited or no internet access.
  • Custom Model Flexibility
    While Dataiku supports many machine learning frameworks, the process of integrating custom or niche models can be cumbersome compared to using those frameworks directly.
  • Dependency on Ecosystem
    The seamless experience of Dataiku often relies on the broader cloud and data ecosystem. Changes or issues in integrated services can impact its performance and reliability.
  • Fitment Information
    Cascading Year/Make/Model fitment search, up to four levels
  • Vertical integration nobody else has
    8 built-in vertical templates, plus custom templates on paid tiers
  • Shopify Metaobjects
    Fitment stored as native Shopify metaobjects — your data survives uninstall
  • Smart Data Processing
    Smart Parse: extract fitment from existing product titles and descriptions, with confidence scoring
  • CSV Import/Export
    CSV import with fuzzy matching and a coverage dashboard
  • ACES / PIES
    ACES / PIES import and NHTSA VIN decoding
  • My Garage
    Saved-vehicle garage, compatibility table, and product-page fit notice
  • Context Aware Fitment Search
    Collection-level fitment assignment
  • No Obligations
    Free tier with no expiry, up to 50 products

Videos

Walkthroughs and reviews on video.

Dataiku 3 videos + Add
ViewForge 1 video + Add

AutoML with Dataiku: And End-to-End Demo

More videos

  • - Dataiku: For Everyone in the Data-Powered Organization
  • - Dataiku DSS Tutorial 101: Your very first steps

ViewForge: YMM Search & Filter

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Dataiku
ViewForge
0% 0%
100% 100%
100% 100%
0% 0%
0% 0%
100% 100%

Questions & Answers

As answered by people managing Dataiku and ViewForge.

What makes your product unique?

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.

Why should a person choose your product over its competitors?

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.

How would you describe the primary audience of your product?

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.

What's the story behind your product?

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.

Which are the primary technologies used for building your product?

ViewForge's answer:

  • TypeScript
  • Shopify metaobjects, as the fitment data store
  • Shopify theme app extensions for the storefront components: fitment search, saved-vehicle garage, compatibility table, product-page fit notice
  • Shopify Storefront API, for querying fitment from the theme
  • Shopify Admin GraphQL API, for writing and exporting fitment records
  • NHTSA vehicle database, for VIN decoding
  • ACES and PIES XML parsing, for automotive catalog import

User comments

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Dataiku no reviews yet
ViewForge no reviews yet

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