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Join Data365 X(Twitter) with Apify Instagram Comments Scraper
Top companies trust Datastreamer to integrate, enrich, join, and apply their web data needs.
About Data365 X(Twitter)
Collect and extract Twitter (X) posts and profiles using keywords. Data365 offers advanced capabilities for collecting Twitter data, helping you understand and popular themes, monitor emerging trends, and even identify potential threats.
About Apify Instagram Comments Scraper
Retrieve comments from posts by calling Apify's Instagram Comments Scraper within your pipeline!
To set up this Actor, you will need to add Instagram posts or reels to extract the comments from, the desired number of comments, and optionally, the order of comments, and replies.
For each Instagram post, you will extract:
Comment details: comment text, timestamp, and number of likes.
Commenter profile: username, full name, profile picture URL, and account status (private or public).
Engagement data: number of replies and whether the commenter is verified.
Post association: URL of the Instagram post the comment belongs to.
Replies (if any): nested replies under the main comment.
Quickly connect Data365 X(Twitter) and Apify Instagram Comments Scraper with a Datstreamer Pipeline.
Step 1
Start your Pipeline with Data365 X(Twitter)
Effective enterprise integration requires diverse data inputs. Web data—whether from partner ecosystems, proprietary systems, or public sources—offers the scale and flexibility needed to drive data pipelines.
Step 2
Add Apify Instagram Comments Scraper with Unify or another transformer to combine schemas
To accelerate using your web data, you can apply any number of operations to the data. Enrich, augment, join, structure, filter, storage, search, or more! Datastreamer has hundreds of plug-and-play operations that you can apply.
Step 3
That's it! You have just connected Data365 X(Twitter) and Apify Instagram Comments Scraper
With Datastreamer it’s never been easier to use web data. You can dynamically expand your Pipelines with more capabilities, and you’ve now been able to solve your operational bottlenecks in working with web data.