Instagram Scraping

What's included

We collect public data from Instagram for you and turn it into a ready-made table or database for analysis. The work includes gathering profiles and their statistics, posts and Reels with metrics, comments and likes, follower and following lists, as well as posts by hashtag and brand mentions. We set up automated collection around your goals — competitor analysis, audience research, niche monitoring — and export the result in a convenient format: a table, a database, or straight into your system. We work only with publicly available data and respect the platform's restrictions, rather than breaking through protections. What you get is a structured body of data instead of manually scrolling through hundreds of accounts, and that dataset can be refreshed on a schedule.

How it really works

Data collection is built on ready-made actors — cloud-based collector programs that visit public pages, take the fields you need, and store them in a structured dataset. We give the actor its input: a list of profiles, hashtags, or links — and parameters for how many posts and comments to collect. The actor works through the pages with paginated loading, imitating ordinary access, and returns the result as a table where each row is a post, profile, or comment with its metrics. Next the data goes through cleaning and normalization: we remove duplicates, bring dates and numbers to a single format, and strip out junk. The finished dataset is exported in the required format or fed into your CRM and analytics, and the whole collection can be put on a schedule so the data refreshes itself.

Where collecting data from social networks came from

Instagram appeared as an iOS app on October 6, 2010 — it was launched by Kevin Systrom and Mike Krieger, and just a year and a half later Facebook bought the service. The approach of automatically collecting data from the web is older than social networks themselves: the first web robot is generally considered to be the World Wide Web Wanderer, which engineer Matthew Gray deployed at the Massachusetts Institute of Technology in June 1993 to measure the size of the web. From this idea of automatically crawling pages grew an entire industry — from search crawlers to modern data collectors for analytics. The Apify cloud collection platform we work on appeared in 2015 and moved scraping from improvised scripts into a managed, industrial process. We use exactly this mature toolkit, rather than homegrown parsers that break at the first change in markup.

Why accuracy and legality are critical

Data collection is valuable only when the data is correct and obtained cleanly. A sloppy parser mixes up fields, loses some posts, or duplicates records — and decisions get made on a distorted picture. That's why the main engineering work is not in visiting the page itself, but in normalization: reconciling fields, deduplication, correct handling of dates and metrics. The legal framework matters just as much: we collect only publicly available data, respect rate limits, and don't bypass authorization and protections — this is what separates analytics from a violation. We're honest about what's publicly available and what isn't, and we don't promise data from private profiles. This approach yields a reliable body of data you can use for business decisions, rather than a set of random rows with legal risk.

What stack we work with

The foundation is the Apify platform and its ready-made actors for Instagram: they cover the collection of profiles, posts, Reels, comments, and hashtags and run in the cloud, independent of your computer. We build orchestration and scheduling on n8n, tying data collection, cleaning, and export into a single flow: collected, normalized, stored in a database, notified. We store the result in a table, a database, or object storage, and where needed we link it to your CRM and analytics. For recurring tasks, we set up an automated run on a schedule with notifications about new data. The stack is manageable and portable: the collection logic is described explicitly, it can be changed for new tasks, and you don't depend on a single homegrown script that only its author understands.

When the key tools appeared

Data collection tools took shape in three waves. The first web robot, the World Wide Web Wanderer, appeared in June 1993 and set the very idea of automatically crawling pages. Instagram as a data source launched on October 6, 2010. The Apify cloud scraping platform we work on was founded in 2015 by Jan Čurň and Jakub Balada and turned scattered parsers into a managed platform with ready-made actors and storage. The n8n orchestrator was released as an open-source project in 2019 and made it possible to link collection, processing, and export without manual code. We use the current generation of these tools, so collection is resilient to changes and scales, rather than falling apart at the first platform update.

Why you can trust us with this

Our team's combined IT experience exceeds 45 years, and we treat collecting data from social networks as an engineering task, analyzing social platforms on an ongoing basis. We approach it systematically: we fix the goal of collection, configure the actors, always clean and normalize the data, and check it for completeness and duplicates before handover. We keep the legal framework honest — we work with public data, respect platform restrictions, and don't promise access to what's private. The data and the pipeline stay yours: you get both the result and a configured process you can repeat without us. We'll tell you plainly where collection is useful for analytics and where it's an unnecessary burden — so in the end you get a reliable body of data for a specific decision, rather than a pile of raw rows.