Data Panda brings your ImageKit assets, folders, custom metadata, transformations and bandwidth events together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your marketing, ecommerce and operations teams use every day.
ImageKit was launched in January 2017 by three former Ixigo colleagues, Rahul Nanwani, Manu Chaudhary and Somesh Khatkar. The product started as a real-time image optimization and transformation API on top of a global CDN, expanded to video in 2022, and added a digital asset management module in early 2023. Today the platform sits in front of object storage like S3, Google Cloud Storage and Azure Blob, serves over 2,500 businesses across 80 countries and processes more than a billion media requests a day.
The interesting part of ImageKit for reporting is how much logic lives in the URL itself. A single source asset can be served as hundreds of variants through transformation strings: width, format, smart-crop, overlay, AI background removal. The DAM module sits next to that with folders, custom metadata, AI tagging and signed URLs. The bandwidth and storage bill is the visible part. The harder questions, like which transformation patterns dominate traffic, which uploaded assets nobody on the site references, which custom-metadata fields the team filled in once and forgot, sit between ImageKit and the systems that consume the URLs. Pulling ImageKit into a warehouse is how you connect the media library to the product catalog, the campaigns and the pages that use it.
Library footprint, transformation patterns and delivery cost across the whole tenant.
Let ImageKit events drive the rest of your stack instead of someone forwarding upload notifications by hand.
Put your real media library behind AI that knows your folder structure and metadata, instead of a generic image search.
Small tools that sit on ImageKit data for people who do not live in the ImageKit dashboard.
A list of concrete reports, automations and AI features we have built on ImageKit data. Pick the one that matches your situation.
How much of what we pay ImageKit for is serving the site?
Per folder and per transformation pattern, the bandwidth-versus-stored-asset ratio. Surfaces the campaign folder that gets four percent of the storage and forty percent of the bandwidth, and the brand library that costs storage every month and serves a handful of requests. That is the split finance and marketing want before the next plan-tier conversation.
Which assets are uploaded but nobody on the site references?
Assets ranked by storage, upload date and the count of references in the catalog, the CMS and the email tool. Splits truly orphan uploads from archive folders that exist on purpose, so the cleanup conversation lands with the right team owner instead of a blanket message to everyone.
Which transformation patterns are quietly driving most of our bandwidth?
Bandwidth grouped by the transformation string, joined to the page or product that requests it. Marketing sees that one mobile-cropped variant of one banner is generating most of the cost, and the team can either accept it, change the source or move the page to a smaller default.
A real bandwidth-and-storage view across folders, brands and transformation patterns instead of a monthly aggregate. Finance walks into the next plan-tier conversation with the variants and folders that drive the cost, and the ones that sit on the bill in silence.
Catalog-asset coverage across Shopify, Akeneo or the PIM joined to ImageKit, with campaign-asset usage on top. Ecommerce and revenue teams see which products are missing the right visuals, which creative variants earned the engagement, and which 404s are quietly degrading the buyer experience.
A company-wide view on storage growth, orphan uploads and signed-URL sprawl. Ops runs a quarterly cleanup with a real list per team owner instead of asking every brand to do their own audit.
New or updated product images uploaded to the matching ImageKit folder propagate to the Shopify product, with the PDP, PLP and thumbnail transformation URLs already filled in. Merchandisers stop pasting CDN links by hand, and the storefront stays consistent with whatever the brand team last approved in the DAM.
Product images and videos in ImageKit are linked to the matching Akeneo product, with custom metadata like brand, season and locale flowing both ways. Catalog managers see one source of truth for what each SKU should look like, and the channel exports pick up the right asset URL without a separate mapping spreadsheet.
Email blocks in Klaviyo pick up the campaign-tagged ImageKit assets automatically, with the right responsive transformation URLs filled in per device. Lifecycle marketers stop searching the DAM during send-day chaos, and the open-rate and click data lands back next to the asset that carried the campaign.
Visuals tagged for a HubSpot campaign in ImageKit are linked to the matching campaign and asset record, with bandwidth and view counts kept in sync. Marketing sees which creative variant earned the engagement on landing pages and emails, instead of guessing from a generic asset library export.
Uploads, replacements and approval-state changes on flagged ImageKit folders post a compact update in the Slack channel that owns the brand or campaign, with a link back to the asset. Brand teams stop relying on someone watching the DAM, and a light activity trail sits next to the conversation where decisions get made.
Assets in ImageKit attach themselves to the matching task on the production board, with current revision, approval state and folder owner shown next to the task. Producers stop pasting outdated links into descriptions, and reviewers always land on the current draft instead of last week's copy.
These are the 3 tables we currently pull from ImageKit into your warehouse. Query them directly in SQL, join them to the rest of your stack, or build reports on top.
Missing a table you need? We can extend the sync. Tell us what is missing and we will build it for you.
You keep the reporting tool you already have. We connect it to the warehouse where your ImageKit data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
ImageKit exposes assets, folders, custom metadata fields, AI tags and signed URLs through its Media and Management APIs. We land all of those alongside upload, transformation and bandwidth events, so reporting on orphan assets, metadata coverage and signed-URL sprawl works on the same join keys. The custom-metadata schema you defined in ImageKit becomes columns in the warehouse, not a JSON blob you still have to parse.
Yes. Each delivered URL carries the transformation string, and the usage data exposes per-asset and per-pattern bandwidth. We model that so you can group bandwidth by the variant (a mobile crop, a watermarked overlay, an AI-removed background) and join it to the page, product or campaign that requested it. That is what makes the variant-level cost conversation possible without screenshotting the dashboard every month.
Yes. ImageKit can sit in front of S3, Google Cloud Storage or Azure Blob without forcing a migration, and the metadata you care about for reporting (which assets exist, how they are tagged, how they are transformed and how often each variant is requested) lives on the ImageKit side. The connector pulls from ImageKit, so the same warehouse model works whether your origin is your own bucket or the ImageKit Media Library.
We review your ImageKit setup and the systems around it. Together we pick the first thing worth building.