SQL, NoSQL and modern data practices

#todayilearned #sql #database

TL;DR style notes from articles I read today.

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

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

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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