The article discusses various types of databases, emphasizing the strengths and use cases of relational, wide-column, time-series, and ledger databases. Relational databases are preferred for structured data and maintaining relationships via Foreign Keys. Wide-column databases provide flexibility with column formats, making them ideal for high-write scenarios. Time-series databases focus on collecting and analyzing data over time, accommodating continuous updates efficiently. Lastly, ledger databases specifically cater to financial data management, accommodating the needs of large organizations with high transaction volumes. Each type serves distinct purposes based on workload and data requirements.
Relational databases are structured, managing large data volumes, maintaining integrity, and establishing relationships through Foreign Keys, perfect for well-defined data.
Wide-Column databases allow flexible column structures across rows, enabling scalability and low-latency queries, best for use cases with high write loads.
Time-Series databases specialize in storing aggregated time-based data, managing lifecycle and enabling time-aware queries for monitoring performance across various metrics.
Ledger databases provide a structure for recording financial transactions, crucial for larger companies with high-frequency financial activity requiring specialized databases.
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