Search results
35 resultsSecure Coding — OWASP Top 10 for Backend Engineers
Injection, broken auth, XSS, IDOR, and how to prevent each.
Applied AI & ML — Service Overview
Everything included in our Applied AI engagements: RAG, agents, fine-tuning, evals, and guardrails.
Data Warehouse Modelling — Star Schema and Dimensional Design
Facts, dimensions, slowly changing dimensions, and why modelling choices matter for query performance.
REST API Versioning Strategies
URL path, header, and query-param versioning compared with real-world tradeoffs.
Graph Databases — When to Use Neo4j Over Relational
Nodes, edges, Cypher queries, and use cases where graph beats SQL.
What is Retrieval-Augmented Generation (RAG)?
A plain-English explanation of RAG: why it beats pure LLM memory for production knowledge systems.
PostgreSQL Performance Tuning Fundamentals
Indexing strategy, EXPLAIN ANALYZE, vacuum, and configuration settings that matter most.
Apache Iceberg — The Open Table Format Explained
Snapshots, schema evolution, partition evolution, time travel, and compaction.
Multi-Tenancy Patterns — Database-per-Tenant, Schema-per-Tenant, and Row-Level
Tradeoffs for SaaS data isolation, compliance, and operational complexity.
SQL Query Optimisation — Indexes, Execution Plans, and N+1
Practical techniques for making slow queries fast.
Trino (formerly PrestoSQL) — Federated SQL Across Data Sources
Architecture, connectors, query federation, and performance tuning.
Event Sourcing and CQRS — Practical Implementation
Event store design, projection rebuilding, and operational realities.
CDN and Edge Caching Strategy
Origin offload, cache key design, purging, and choosing a CDN.
Elasticsearch Indexing Strategy and Performance
Mapping, sharding, bulk indexing, and query optimization for Elasticsearch.
Real-Time Analytics Architecture Patterns
Lambda, Kappa, HTAP, and choosing the right pattern for sub-second analytics.
Designing a Data Lake on AWS S3
Folder structure, naming conventions, lifecycle policies, and access patterns.
Snowflake Best Practices for Cost and Performance
Virtual warehouses, clustering, query optimization, and controlling spend.
Data Lake vs Data Warehouse vs Lakehouse
Practical comparison of the three architectures and how to choose.
DuckDB — Blazing Fast Local Analytics
When to reach for DuckDB instead of Spark, and how to use it effectively.
Fine-tuning LLMs: when, why, and how
A practical guide to LoRA, QLoRA, and full fine-tuning for production use cases.
MongoDB Schema Design Patterns
Embedding vs referencing, the subset pattern, and indexing strategy.
Time-Series Databases — InfluxDB vs TimescaleDB vs ClickHouse
Comparing purpose-built and general-purpose solutions for time-series data.
Event-Driven Data Architecture Patterns
Event sourcing, CQRS, outbox pattern, and when event-driven beats request/response.
GraphQL vs REST — When to Use Each
Comparing query flexibility, over-fetching, tooling, and operational complexity.
Amazon Redshift — Architecture and Query Optimization
Distribution styles, sort keys, VACUUM, ANALYZE, and WLM tuning.
Distributed Tracing — Propagating Context Across Services
Trace context propagation, sampling strategies, and analysing traces.
Redis Caching Patterns for Production Applications
Cache-aside, write-through, TTL strategy, and cache invalidation approaches.
Apache Spark — Core Concepts and When to Use It
RDDs, DataFrames, Spark SQL, and the use cases where Spark is the right tool.
Data & Platform — Service Overview
Pipelines, vector stores, governance, and privacy-first data design.
Data Platform Cost Optimization Strategies
Reducing Snowflake, S3, Spark, and Kafka spend without sacrificing performance.
Implementing Search — From Basic SQL to Elasticsearch
Full-text search progression from LIKE queries to dedicated search engines.
Materialised Views — When and How to Use Them
Incremental refresh, use cases, and implementation across Postgres, Snowflake, and dbt.
Parquet vs CSV — Why Columnar Storage Matters
How Parquet's columnar format reduces storage costs and speeds up analytical queries.
Vector Embeddings — How They Work and Where They Live
From text to vectors, similarity search, and choosing the right embedding model.
BigQuery Cost and Performance Optimization
Partitioned tables, clustered tables, slot usage, and avoiding full scans.