(pronounced import-eye-oh) lets you scrape data from any website into a searchable database. It is perfect for gathering, aggregating and analysing data from websites without the need for coding skills. As Sally Hadadi, from, told the idea is to “democratise” data. “We want journalists to get the best information possible to encourage and enhance unique, powerful pieces of work and generally make their research much easier.” Different uses for journalists, supplemented by case studies, can be found here.

A beginner’s guide

After downloading and opening browser, copy the URL of the page you want to scrape into the browser. I decided to scrape the search results website of orphanages in London:

001 Orphanages in London

After opening the website, press the tiny pink button in top right corner of the browser and follow up with “Let’s get cracking!” in the bottom right menu which has just appeared.

Then, choose the type of scraping you want to perform. In my case, it’s a Crawler (we’ll be getting data from multiple similar pages on the same site):


And confirm the URL of the website you want to scrape by clicking “I’m there”.

As advised, choose “Detect optimal settings” and confirm the following:


In the menu “Rows per page” select the format in which data appears on the website, whether it is “single” or “multiple”. I’m opting for the multiple as my URL is a listing of multiple search results:multiple

Now, the time has come to “train your rows” i.e. mark which part of the website you are interested in scraping. Hover over an entire “entry” or “paragraph”:hover over entry

…and he entry will be highlighted in pink or blue. Press “Train rows”.

train rows

Repeat the operation with the next entry/paragraph so that the scraper gets the hang of the pattern of your selections. Two examples should suffice. Scroll down to the bottom of your website to make sure that all entries until the last one are selected (=highlighted in pink or blue alternately).

If it is, press “I’ve got all 50 rows” (the number depends on how many rows you have selected).

Now it’s time to focus on particular chunks of data you would like to extract. My entries consist of a name of the orphanage, address, phone number and a short description so I will extract all those to separate columns. Let’s start by adding a column “name”:

add column

Next, highlight the name of the first orphanage in the list and press “Train”.


Your table should automatically fill in with names of all orphanages in the list:table name

If it didn’t, try tweaking your selection a bit. Then add another column “address” and extract the address of the orphanage by highlighting the two lines of addresses and “training” the rows.

Repeat the operation for a “phone number” and “description”. Your table should end up looking like this:table final

*Before passing on to the next column it is worth to check that all the rows have filled up. If not, highlighting and training of the individual elements might be necessary.

Once you’ve grabbed all that you need, click “I’ve got what I need”. The menu will now ask you if you want to scrape more pages. In this case, the search yielded two pages of search results so I will add another page. In order to this this, go back to your website in you regular browser, choose page 2 (or any next one) of your search results and copy the URL. Paste it into the browser and confirm by clicking “I’m there”:

i'm there

The scraper should automatically fill in your table for page 2. Click “I’ve got all 45 rows” and “I’ve got what I needed”.

You need to add at least 5 pages, which is a bit frustrating with a smaller data set like this one. The way around it is to add page 2 a couple of times and delete the unnecessary rows in the final table.

Once the cheating is done, click “I’m done training!” and “Upload to”.


Give the name to your Crawler, e.g. “Orphanages in London” and wait for to upload your data. Then, run crawler:run crawler

Make sure that the page depth is 10 and that click “Go”. If you’re scraping a huge dataset with several pages of search results, you can copy your URLs to Excel, highlight them and drag down with a black cross (bottom right of the cell) to obtain a comprehensive list. Paste it into the “Where to start?” window and press “Go”.go

crawlingAfter the crawling is complete, you can download you data in EXCEL, HTML, JSON or CSV.dataset

As a result, we obtain a data set which can be easily turned into a map of orphanages in London, e.g. using Google Fusion Tables.

Do you have any further tips for extraction? Do you know any other good scrapers? Share your thoughts in the comments below.


  1. The resultant data set that is generated after perfectly running a connector in is having only 200 rows, the total number of rows are almost 1896. Can u please help me out in getting the total data. It will be really helpful for me in my seo work. Will be eagerly waiting for your reply. Thanks in advance!!!

    • Hi, connectors are currently limited to 20 pages of pagination. You could try showing more results per page (100) and running the connector like that.

  2. Hey Aleksandra,

    Nice tutorial, there is one more which is completely free for unlimited data scraping and just point and click element selector Chrome app.
    And then use their desktop app for advance feature like batch crawling, scheduling etc.
    I guess you’d love to add this freeware tool in your post, details here

  3. Hello,
    I find your article very useful and I thank you for making clear for me. I used to scrape with Python modules, but my script took a lot of time, too much data, and now I am trying with, I have multiple pages and select tags, I thought that i will work with connector .. Will it take time to learn how to make the crawler i want and to execute it? Should i stuck with my script or try out ?

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