SQL & NoSQL Databases

Modern Data Storage & Management

Understand the differences, strengths, and use-cases of SQL and NoSQL databases. Learn when to use each, and how they power today’s data-driven applications.

What’s the Difference?

If your data is very structured and ACID compliance is a must, SQL is a great choice. If your data requirements aren’t clear or if your data is unstructured, NoSQL may be your best bet. This page documents both database types and their differences.

  • SQL: Structured Query Language (CRUD: Create, Read, Update, Delete). Universal among SQL database engines.
  • NoSQL: Tables, documents, graphs. Built to scale with high performance but queries are less flexible.

References

MongoDB: SQL vs NoSQL
Building a Scalable Data Warehouse with Data Vault 2.0

Getting Started

Job scraping is the process of gathering job posting information online in a programmatic manner. This automated way of extracting data from the web helps build resourceful job databases by integrating various data sources into one.

Pre-Requirements

Libraries: psycopg2, SQLAlchemy, pandas, numpy, BeautifulSoup, Selenium

Scraping & Data Integration

See Python, BeautifulSoup, and Selenium scripts to extract data from websites:

Linkedin Scraper
Data Integration with Psycopg2
Open HTML Links & Get Data

Accessibility

Modern database systems should be accessible and usable for all. Consider accessibility best practices when designing data-driven applications.

More Info

For more details, see the references above or contact me for consulting and project support.