AutomationData Engineering
LinkedIn & Twitter Scraping Bot
Distributed scraping bot for LinkedIn and X with Kafka/SQS-based pipelines.
Overview
Client Overview
A distributed scraping bot for LinkedIn and X (Twitter) built in Python with Selenium. The bot uses SQS and Kafka pooling to coordinate jobs and forwards extracted data (posts, profiles, connections, engagements) to downstream APIs, with operational alerts routed to Slack.
Industries
AutomationData Engineering
Technologies
PythonSeleniumKAFKASQSSlack
Status
Live & Active
Challenges
The Challenges
1
Scraping LinkedIn and X reliably under aggressive anti-bot defenses.
2
Coordinating thousands of scraping jobs across distributed workers.
3
Handling rate limits and rotating sessions without losing job state.
4
Surfacing failures fast through Slack alerts so ops can react in minutes.
Solutions
Solutions & Strategies
01
Distributed Job Pooling
- Used SQS and Kafka to pool jobs across worker fleets for elastic throughput.
- Designed idempotent jobs so retries are safe under transient failures.
02
Scraping Logic
- Built Selenium-based scrapers covering posts, tweets, profiles, and engagements.
- Normalized scraped data into a unified schema before forwarding to APIs.
03
Observability
- Integrated Slack for real-time alerts on failures, throttling, and job backlogs.
- Added structured logging for downstream debugging.
Results
The Results
✓Key Achievements
- Reliable scraping pipeline across LinkedIn and X.
- Distributed worker architecture with Kafka and SQS pooling.
- Real-time Slack ops alerts.
- Clean, normalized data forwarded to downstream APIs.
★Project Highlights
- LinkedIn + X coverage in one bot.
- Kafka and SQS pooling.
- Slack-integrated ops alerts.
- Idempotent, retry-safe job design.
Tech Stack
Technologies Used
Scraping
PythonSelenium
Messaging
KafkaAWS SQS
Observability
Slack