『Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations』のカバーアート

Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations

Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations

著者： Fexingo
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Lucas and Luna explore the landscape of database technology, from relational SQL systems to document-based NoSQL and emerging storage paradigms. Each episode examines a specific database model—columnar stores, graph databases, time-series engines, or serverless SQL—and dissects its architecture, performance characteristics, and real-world tradeoffs. Lucas brings a journalist's rigor, questioning vendor claims and surfacing benchmark data; Luna pushes back with practitioner experience, asking how these systems behave under production loads. Together they compare when PostgreSQL's mature indexing wins over MongoDB's flexible schema, or why Snowflake's cloud-native approach may not suit every analytical workload. The show also probes deeper: the economics of data storage, the rise of NewSQL, and the implications of data gravity. Listeners will walk away understanding not just which database to choose, but why one design philosophy beats another for a given problem. What happens when your application's read pattern shifts from row-based to columnar? How do you evaluate consistency models without oversimplifying? This is the conversation for data engineers, architects, and technical leaders who need to make informed storage decisions. #SQL #NoSQL #DatabaseTech #DataStorage #PostgreSQL #MongoDB #Snowflake #TimeSeriesDB #GraphDatabase #ColumnarStorage #NewSQL #DataEngineering #Technology #FexingoBusiness #BusinessPodcast #DatabaseArchitecture #DataGravity #Benchmarking Keep every episode free: buymeacoffee.com/fexingo© 2026 Fexingo. All rights reserved. 経済学
エピソード
  • Why Database Stored Procedures Still Matter in 2026

    Why Database Stored Procedures Still Matter in 2026
    2026/06/07
    In this episode of Database Tech with Fexingo, Lucas and Luna explore why stored procedures—once considered legacy—are seeing a resurgence in modern cloud databases. They break down a real-world case from a mid-sized e-commerce company that cut query latency by 40 percent by moving critical order-processing logic into stored procedures. The discussion covers when to use them, the trade-offs with application-layer logic, and why the rise of serverless and edge computing is changing the calculus. No fluff, just a concrete look at a forgotten tool that still delivers. Perfect for engineers and architects evaluating database design patterns in 2026. #StoredProcedures #Database #SQL #Tech #Technology #DatabaseTech #FexingoBusiness #BusinessPodcast #CloudDatabases #Serverless #EdgeComputing #QueryPerformance #EcommerceTech #DatabaseOptimization #LegacyTech #SoftwareEngineering #DataArchitecture #DatabaseDesign Keep every episode free: buymeacoffee.com/fexingo
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    11 分
  • Why Database Indexes Become a Performance Problem

    Why Database Indexes Become a Performance Problem
    2026/06/07
    Lucas and Luna explore a counterintuitive database performance issue: indexes that actually slow queries down. They examine how B-tree index depth grows with data volume, turning fast lookups into multi-level tree traversals. The discussion uses a concrete example of a 10-million-row table on PostgreSQL and shows how a poorly chosen composite index on an e-commerce orders table caused query times to jump from 2 milliseconds to over 200 milliseconds after a schema migration. They explain index fragmentation, the impact of high-cardinality columns, and why write-heavy workloads suffer from index maintenance overhead. Practical tuning advice includes using pg_stat_user_indexes to spot unused indexes and considering partial indexes for skewed data distributions. The episode closes with a reminder that indexes are not set-and-forget—they need periodic review. #DatabaseIndexes #PostgreSQL #BTree #QueryPerformance #IndexTuning #DatabaseOptimization #CompositeIndex #IndexFragmentation #Cardinality #PartialIndex #pg_stat_user_indexes #WriteHeavyWorkloads #Technology #DatabaseTech #FexingoBusiness #BusinessPodcast #SQL #DataStorage Keep every episode free: buymeacoffee.com/fexingo
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    8 分
  • Why Database Connection Latency Spikes Under Load

    Why Database Connection Latency Spikes Under Load
    2026/06/06
    Lucas and Luna explore why database connection latency can suddenly spike from 2 milliseconds to over 200 milliseconds under heavy load, even when the database itself isn't saturated. They examine a real-world case from a mid-sized e-commerce platform where connection establishment overhead — TCP handshakes, TLS negotiation, authentication — became the bottleneck after a routine deployment. Lucas explains how connection pooling mitigates this but why pool size tuning and keepalive settings matter more than most engineers realize. Luna challenges the common assumption that adding more connections helps, revealing how connection storms can cascade into latency disasters. The episode covers specific tools like pgBouncer for PostgreSQL, connection multiplexing strategies, and why monitoring connection establishment time is more important than query execution time for many applications. A practical deep dive for anyone who has ever seen application latency degrade without obvious database performance issues. #Database #Technology #ConnectionLatency #PostgreSQL #pgBouncer #ConnectionPooling #TCPHandshake #TLSNegotiation #DatabasePerformance #LatencySpikes #ConnectionStorm #BackendEngineering #SQL #NoSQL #DataEngineering #FexingoBusiness #BusinessPodcast #TechPodcast Keep every episode free: buymeacoffee.com/fexingo
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    8 分
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