Learn how to build a Python web crawler to collect U.S. stock data, store it in InfluxDB, and analyze trends. Covers installation, data retrieval, database writing, querying, and visualization. Ideal for real-time financial analytics, time-series data management, and performance monitoring with Grafana.

MongoDB is a flexible, document-oriented NoSQL database for large-scale, semi-structured, and dynamic data. It supports replica sets, sharding, and powerful aggregation pipelines for scalable, high-performance analytics. Ideal for IoT, log querying, and content management, MongoDB bridges operational and analytical data processing.

Boost Web Scraping Efficiency with Kafka & Python

Kafka acts as a scalable message queue for Python web scrapers, enabling:

🚀 Decoupled architecture (producers/consumers work independently)
📈 Traffic buffering during high-volume crawling
🔧 Real-time data cleaning (noise removal, format standardization)
🔄 Fault-tolerant processing with ZooKeeper coordination
Ideal for distributed scraping systems requiring high throughput and data quality.

Python MITM Proxy: Intercept & Modify Web Traffic

Use mitmproxy (Python 3.7+) to:

🛠️ Intercept HTTP/HTTPS traffic
✏️ Modify requests/responses dynamically
📊 Scrape data directly to MongoDB
🔄 Redirect/rewrite content via Python scripts
Ideal for web scraping, API testing, and security audits.

Doris delivers sub-second queries on billion-row datasets with MySQL compatibility. Key features:

Minimalist MPP architecture (FE/BE nodes)
Vectorized engine & columnar storage
Real-time ingestion (Kafka/Flink/HTTP)
Single-system simplicity (No Hadoop/ZooKeeper)
Ideal for BI dashboards, user analytics, and live data warehousing.

Bypass JavaScript protections effortlessly using Playwright – the fastest headless browser for scraping. Learn:

Auto-login & pagination handling
Proxy rotation & fingerprint spoofing
30-50% faster than Selenium
Anti-detection techniques
Perfect for scraping modern JS-heavy sites!

Master efficient data cleaning for web scraping with these pro techniques:

PostgreSQL/MySQL for structured data
MongoDB for JSON/nested documents
Elasticsearch for fuzzy text searches
Optimize with database partitioning
Handle billions of records seamlessly
Boost your scraping workflow with these essential storage solutions!

ClickHouse is a high-performance OLAP database using columnar storage, data compression, and vectorized execution for lightning-fast analytics. Ideal for log analysis, user behavior tracking, and real-time BI, it delivers petabyte-scale queries in seconds. Open-source, scalable, and SQL-compatible, ClickHouse revolutionizes big data processing. Learn more about its architecture and use cases!

Master pandas for efficient data cleaning: handle missing values (dropna(), fillna()), remove duplicates (drop_duplicates()), convert data types (astype(), to_datetime()), and clean strings (str.replace()). Learn advanced merging (merge(), concat()) and regex extraction for polished datasets.