business
Hosted by Fexingo · business · EN
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
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Contact path
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Booking probability
36%
Guest openness
Selective
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Signup to Generate a PitchLucas 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.
Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations is hosted by Fexingo. The show has published 223 episodes to date.
Recent episodes include "How Data Compression Saves Cloud Spend" (1 November 2026, 13 minutes), "How Data Gravity Locks You Into Cloud Vendors" (31 October 2026, 10 minutes), and "The Hidden Cost of Data Redundancy in Modern Systems" (29 October 2026, 11 minutes).
Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations publishes roughly daily, with episodes averaging 11 minutes across 223 episodes.
Booking probability for Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations is 36%, and the host is rated Selective on guests. Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations requires a Pod Score of 80 or higher. Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations's contact route on record: verified email.
The strongest fits for Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations are IT Services & Software Development. Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations has a business audience.
Fexingo hosts Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations, a business podcast with 223 episodes published.
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Episode #229
Most engineering teams treat data compression as a technical afterthought, but it is actually one of the most direct levers for controlling cloud storage costs. In this episode, we look at how shifting from uncompressed...
Episode #228
We are looking at the hidden economic trap of data gravity, where massive datasets physically anchor businesses to their current cloud provider. Using a hypothetical logistics firm that moved two petabytes of shipping...
Episode #227
Most engineering teams optimize for read speed, but they ignore the silent tax of duplicate data. We look at how redundant storage inflates cloud bills and complicates consistency. Lucas and Luna break down why...
Episode #226
We look at how distributed caching layers transformed data access patterns in the mid-2020s, using a specific case study of a major fintech platform that reduced latency by ninety percent. The conversation explores the...
Episode #225
Database lock contention is the silent thief of application performance, turning milliseconds into minutes and costing companies millions in wasted compute. In this episode, we dive into a specific case study of a...
Episode #224
We look at the hidden cost of data gravity on modern analytics engines. When petabytes of historical logs sit in cold storage, even simple queries stall. We examine a specific case where a mid-market logistics firm cut...
Episode #223
Lucas and Luna explore how silent schema changes degrade database performance over time, using a real-world case where a minor column type shift caused a ten percent latency spike across an entire microservices...
Episode #222
We explore data gravity — the economic force that makes moving massive datasets prohibitively expensive. Using a concrete example of an analytics firm facing five thousand dollar transfer fees, Lucas and Luna break down...
Episode #221
On October twenty third twenty twenty six, we look at why connection pooling is the most misunderstood part of database architecture. Most engineering teams treat connections like free resources, leading to massive...
Episode #220
We dive into the mechanics of horizontal scaling through database sharding, using the specific architectural decisions made by companies like Stripe and Discord to handle millions of concurrent transactions. Lucas and...
Episode #219
Lucas and Luna explore the strategic cost of schema rigidity in modern data architecture. Using a hypothetical fintech pivot from single-currency to multi-asset trading, they examine how database design choices dictate...
Episode #218
We dissect how index bloat silently degrades database performance in modern tech stacks. Lucas and Luna explore the specific mechanics of write amplification, using a concrete case study from a mid-sized e-commerce...
Episode #217
Most engineering teams optimize for throughput, but latency is the silent killer of user retention and operational cost. In this episode, Lucas and Luna break down how a one-hundred-millisecond delay in database...
Episode #216
We talk about how modern distributed databases handle partial failures and why most engineering teams ignore the cost of silent data loss. Using a specific example from a major fintech platform that lost transactions...
Episode #215
In this episode, Lucas and Luna examine the silent killer of modern software teams: schema drift. They trace how unmanaged changes in SQL and NoSQL structures create hidden technical debt that slows deployment cycles...
Episode #214
When a major fintech firm faced a regulatory audit that hinged on tracing customer data from source to report, the difference between chaos and clarity came down to one thing: data lineage. In this episode of Database...
Episode #213
We look at how database indexes drift over time, becoming fragmented and useless. Using a specific example from a mid-sized e-commerce platform, we show how unused or redundant indexes can bloat storage costs by thirty...
Episode #212
Most engineers treat database indexes as magic bullets, but they often create more problems than they solve. In this episode, we look at how the B-tree index structure works under the hood, using a specific case of a...
Episode #211
Most engineering teams over-index their primary databases, paying a heavy performance tax for queries they never run. In this episode, we look at the hidden cost of index bloat and how to identify which indexes are...
Episode #210
We break down the specific mechanics of query optimization and why index fragmentation is silently killing application performance. Using a real-world example from a major fintech platform that saw latency spikes after...
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