TL;DR style notes from articles I read today.

SQL or NoSQL, that is the question!

• The answer depends on your context of usage.
• Use a SQL database when you need ACID compliance (Atomicity, Consistency, Isolation, Durability).
• Use SQL for logic-related discrete data requirements you can identify upfront and when data integrity is essential - when your data is structured and unchanging.
• Use NoSQL for data requirements that are indeterminate, unrelated and evolving; when you need to store large volumes of data without structure.
• Use NoSQL when you need to get started faster, for projects with simpler or looser objectives and where speed and/or scalability are paramount.


Full post here, 8 mins read

SQL is 43 years old — here’s 8 reasons we still use it today

• It excels at accessing and organizing relational databases.
• RDBMS and SQL are battle-tested for many different scenarios, including those where the loss of data, corruption and failure are catastrophic.
• SQL is easy to learn and with half of all developers using SQL and RDBMS, skill sets transfer easily between companies and industries.
• Though not completely interoperable, SQL syntax varies only slightly between vendors.
• It helps to bring computation to the data than bringing data to the computation
• SQL/RDBMS is the best option for most systems, especially where data integrity is essential.


Full post here, 8 mins read

Modern data practice and the SQL tradition

• Most RDBMS offer some schema-less support today allowing you to have a single database for both structured & unstructured data without sacrificing ACID compliance.
• ETL is a necessity for most modern data-driven endeavours but data cleaning and transformation are often decentralized and this distorts data.
• Push data cleaning to the DB level for smoother & cleaner data pipeline. Focus on good data type definitions.
• Postgres and even SQLite and other RDBMS offer some text manipulation and free-text search functions good enough for most applications. You can deploy NLTK or ElasticSearch for more complexity rather than start there.
• Relational databases are cost-effective compared to distributed systems. Performance & stability are easier to achieve too when complexity starts building up.

Full post here, 13 mins read

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