Common pitfalls in Data Analytics: Patterns over Tools | One2N Bits Ep.3 скачать в хорошем качестве

Common pitfalls in Data Analytics: Patterns over Tools | One2N Bits Ep.3

How does analytics break in real-world startups and scaleups? In this episode, Kshitish from One2N breaks down the most common traps teams fall into not just technical, but mental models dragged from transactional to analytical systems. Inside: Why switching databases (from Postgres to ClickHouse, MongoDB, or BigQuery) often isn’t the solution, and patterns always matter more than tools The 3 Vs: Volume, Velocity, Variety, and how your data growth multiplies analytics pain Read replicas vs true warehouses: why most analytics queries get slower over time Over-normalization and how it leads to join hell, and why smart denormalization and aggregated views speed up reports Partitioning and indexing for analytics: avoiding full scans, wasted indexes, and monthly queries hitting weekly partitions Why real-time data sync (CDC/ETL) is overkill for most companies, and how to balance data freshness with actual business needs The mindset shift from transactional (OLTP) to analytical (OLAP) thinking: snapshots in time, eventual consistency, and practical cost control Lessons learnt from One2N client discoveries and what you can do to fix analytics before the next bill surprises you Chapters: Will be added soon so you can jump directly to every key pattern and pitfall. Why it matters: Just like in our DORA Metrics episode, this talk is about visibility and substance not measurement theater, not chasing shiny tools. The real value comes from understanding the roots of your slowdown and acting on them. Subscribe for more One2N Bits episodes covering everything tech, data, cloud, and platform reliability. Check out our work and learn more at https://one2n.io. Comment below: What’s the slowest analytics query you run, and how have you tried fixing it? #DataAnalytics #DataWarehousing #OLAP #OLTP #PatternsOverTools #CDC #ETL #Postgres #ClickHouse #BigQuery #Partitioning #Indexes #AggregatedViews #One2N #One2NBits