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By Data Type guides
How to choose a geospatial analytics tool Pick a location-intelligence tool by the question you're asking — desktop analysis, a shared map, an embedded feature, or warehouse SQL — not the demo map. How to choose a text analytics or NLP tool Pick an NLP library or platform by whether your team writes code, what task you're actually doing, and whether you need a pretrained model or a custom one. How to analyze text data From word counts and topic models to LLM classification — matching the method to the question instead of reaching for the newest tool by default. How to choose a digital humanities tool Digital humanities tools split by what they analyze — text, networks, images or structured historical data — pick by your source material, not the category. How to start with geospatial analysis Coordinate systems, geocoding, spatial joins, and the map design choices that mislead — the fundamentals before you pick a GIS tool. Where to find earth observation and satellite data NASA, ESA, NOAA and USGS all publish free satellite archives — the real choice is resolution, revisit frequency and whether you need raw scenes or an API. Graph and network analytics, explained How relationships between things become data — the algorithms that find structure in a network and where graph databases fit. Image and video analytics, explained How pictures and video become structured data — computer vision, the labeled training data it depends on, and how streaming teams measure watch quality. Speech and audio analytics, explained How spoken words and audio streams become structured data — transcription, diarization and what contact-centre and podcast analytics actually measure. 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. How to choose a change data capture tool Pick CDC by how it reads the source log, who has to operate it, and whether you actually need sub-second delivery or just a fresh warehouse table. 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 an event streaming platform Kafka's protocol won the ecosystem; the real decision is who operates the cluster and whether you need Kafka compatibility at all. 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 an air and water quality monitoring tool Air and water tools split into free open data, consumer hardware and managed sensor networks — pick by who needs the number and how certified it must be. How to choose an industrial IoT analytics tool Historians, contextualization platforms, machine-health monitors and shop-floor apps solve different plant problems — map your job before comparing vendors. Behavioral event data, explained How product and marketing teams turn raw clicks and actions into events, and what separates a usable event stream from a noisy one. 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 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.