Why SQL is beating NoSQL, and what this means for the future of data

9/26/2017

This blog post discusses the advantages of using SQL (Structured Query Language) over NoSQL (Not only SQL) for managing data, particularly in the context of time-series databases. The author highlights the scalability, flexibility, and robustness of SQL databases compared to NoSQL alternatives. They argue that SQL's declarative nature allows for easier data manipulation and querying, making it more suitable for complex data analysis tasks. The post also emphasizes the importance of standardization and compatibility, suggesting that SQL's widespread adoption and ecosystem contribute to its superiority. Overall, the author predicts a promising future for SQL databases in handling time-series data.

sql ꞏ nosql ꞏ time series databases ꞏ data management ꞏ data storage ꞏ data analysis ꞏ database technology ꞏ data scalability ꞏ data integrity ꞏ data querying ꞏ data modeling ꞏ data performance ꞏ data reliability ꞏ data consistency ꞏ data security ꞏ data trends ꞏ database design ꞏ database architecture ꞏ data organization ꞏ data optimization ꞏ data indexing ꞏ data replication ꞏ data backup ꞏ data serialization ꞏ data retrieval ꞏ data processing ꞏ data visualization ꞏ data analytics ꞏ data-driven decision making ꞏ data warehousing ꞏ data governance ꞏ sql vs nosql ꞏ data manipulation ꞏ data querying language ꞏ data structures ꞏ data schemas ꞏ data mining ꞏ data migration ꞏ data evolution ꞏ data durability ꞏ data volatility ꞏ data recovery ꞏ data exploration ꞏ data insights ꞏ data science ꞏ data engineering