SQL, NoSQL or Vector DB? Choosing the Right Store
Non-Relational Databases & Specialized Storage: leave with a workload analysis, database-selection matrix and a prototype of the chosen store.
Not scheduled yet — no date, time or fee fixed. The most-voted topic is hosted next.
1:1 or squad batch · join the community or channel
Learn this topic with a mentor
Don't want to wait for the webinar? Pick how you'd like help. Nothing is paid now — you see the fee before anything is booked.
- Bring your own work — code, campaign, report or plan
- Mentor matched to this topic
- Time, format and fee confirmed by email first
What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why non-relational databases & specialized storage matters — the common problem: Databases are chosen by hype, then fight the actual access patterns.
- 2Core concepts in plain language: Document databases, key-value stores, wide-column stores, graph databases, time-series databases, vector databases
- 3Going further: search indexes, object storage, embedded databases, workload-based database selection
- 4Framework walkthrough: Normalization, Dimensional Modeling, ELT Pipelines, Data Quality Dimensions
- 5Practical workflow, built live: A workload analysis, database-selection matrix and a prototype of the chosen store.
- 6How to measure it: Query latency, Pipeline freshness, Data-quality check pass rate, Failed job rate
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A workload analysis, database-selection matrix and a prototype of the chosen store.
- A working understanding of Normalization and Dimensional Modeling
- A short list of measures to track: Query latency, Pipeline freshness, Data-quality check pass rate
More topics in IT: Data Engineering & Analytics
SQL Beyond SELECT: Joins, Windows and Indexes
Relational Databases & SQL: leave with a normalized schema with constraints, window-function reports and an EXPLAIN-driven index fix.
0 votes · View topic →
Inside the Database: Indexes, Locks and Replication
Database Internals & Administration: leave with a lock-contention diagnosis, replication setup notes and a verified backup-restore runbook.
0 votes · View topic →
Data Pipelines Explained: Batch, Streaming and CDC
Data Engineering & Processing Pipelines: leave with an orchestrated ELT pipeline with validation checks, schema-change handling and a backfill run.
0 votes · View topic →
Analytics Engineering: Trusted Metrics from Raw Data
Analytics, Data Science & Data Governance: leave with a star-schema model, semantic-layer metric definitions, lineage and ownership notes.
0 votes · View topic →