The definitive Google Trends automation engine with over 10,600+ successful runs. Extract breakout queries, historical interest curves, regional heatmaps, and real-time trending searches without IP bans.
{
"itemType": "post",
"id": "t3_1x02q4h",
"title": "Man discovers his parents’ coffee machine used 1TB of data in 10 days",
"subreddit": "technology",
"author": "Fleshy_Tendrils",
"score": 831,
"upvoteRatio": 0.94,
"numComments": 147,
"postType": "link",
"url": "https://arstechnica.com/gadgets/2026/10/...",
"permalink": "https://www.reddit.com/r/technology/comments/1x02q4h/man_discovers_his_parents_coffee_machine_used_1tb/",
"createdAt": "2026-10-07T17:15:39.000000+0000",
"mediaUrls": [
"https://i.redd.it/e2q780u08mtd1.jpeg"
],
"scrapedAt": "2026-10-07T20:10:46.000Z"
}
Built specifically for high-throughput enterprise pipelines with zero credential leakage.
Real-time trends, interest over time (historical), interest by region (sub-regions & cities), related queries, and related topics.
Captures surging search queries marked as Breakout (+5000% growth) before they reach peak mainstream saturation.
Output is automatically normalized into clean rectangular rows, ready for immediate import into Excel, BigQuery, or Power BI.
Built-in residential proxy rotation with zero compute time billing. Only pay for successful events extracted.
Trigger extractions via the official Apify Client or standard REST endpoints.
from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run_input = {
"startUrls": [],
"searchQuery": "artificial intelligence",
"subreddit": ""
}
run = client.actor("unitbytes/reddit-scraper").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item.get("title"), item.get("url"), item.get("itemType"))
Libraries like pytrends are severely rate-limited by Google (HTTP 429) within minutes. UnitBytes manages residential proxy rotation and request orchestration to guarantee 99.9% uptime at scale.
Yes. Pass up to 5 comparison terms simultaneously to receive normalized relative search volume curves across all queries.
All official Google timeframes: past hour, past 4 hours, past day, past 7 days, past 30 days, past 90 days, past 12 months, past 5 years, or full history from 2004 to present.
Queries experiencing exponential growth (+5000% increase) are explicitly tagged with Breakout for instant alert triggers.
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Our data engineering team provides private cluster deployments, custom webhook integrations, continuous schema monitoring, and high-frequency delivery directly to BigQuery, Snowflake, or AWS S3.
Start running immediately on UnitBytes Cloud with zero infrastructure setup, or contact our engineering team for dedicated S3/BigQuery delivery pipelines.