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Storage & Compute guides
Graph and network analytics, explained How relationships between things become data — the algorithms that find structure in a network and where graph databases fit. How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a cloud data warehouse Start from your cloud, your pricing tolerance and your workload shape — general BI, high-concurrency apps, or enterprise mixed workloads — not the feature list. How to choose a distributed query engine This category bundles two different jobs — federating SQL across existing systems, and distributed compute for pipelines and ML — start by telling them apart. How to choose a graph database Decide property graph vs RDF first, then managed vs self-hosted and how the engine scales — the query language follows from those, not the other way round. How to choose a lakehouse platform Choosing a lakehouse means two decisions, not one — which open table format your data sits in, and which platform manages and queries it. How to choose a real-time OLAP database Real-time OLAP databases trade operational complexity for speed on fresh data — the decisions are architecture, query concurrency, and who runs it. How to choose a time-series database Pick a time-series database by whether you need relational joins, what protocol your data already speaks, and how much cardinality you actually have. How to choose a vector database Pick a vector database by deployment model, scale and search type, not by benchmark charts — most teams' real constraint is who runs the cluster. How to choose an in-process analytics engine Pick an embedded analytics engine by workload shape, language runtime and what happens the day you outgrow a single machine. How to decide build vs buy for analytics A framework for the build-vs-buy decision in analytics infrastructure — total cost, maintenance burden and lock-in, layer by layer. How to reduce data warehouse costs Where usage-based warehouse spend actually goes, and the levers — query, storage, scheduling — that reliably bring it down. Time series analysis, explained How analysts model data ordered in time — the patterns forecasting methods look for, the pitfalls, and where the data actually lives.