About Modern DataTools
Who We Are
Modern DataTools is a continuously updated intelligence and decision platform for selecting data and AI technology. It combines structured product evidence, pricing, public adoption signals, market context, and verified relationships to help technical teams move from market research to an explainable shortlist and architecture.
The site is built and maintained by Egor Burlakov — a Tech Leader with 15+ years of hands-on experience building data pipelines, ML systems, and analytics platforms at scale. Based in Luxembourg, operating across the EU.
Why Modern DataTools Exists
Technology decisions are difficult because vendor information is fragmented, capabilities and pricing change quickly, market signals are inconsistent, and integration relationships are rarely clear enough to turn research into an architecture decision.
Modern DataTools exists to connect that evidence. Tool coverage, pricing intelligence, comparisons, market landscapes, rankings, and stack recommendations are built from a shared, refreshed data system rather than disconnected summaries.
No vendor pays for placement. Sources, methodology, and important evidence gaps are documented so teams can assess the strength and limits of the available evidence.
Our Approach
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Continuously collected evidence — Pricing, capabilities, deployment options, public signals, and integrations are collected and refreshed from documented sources.
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Historical market context — Weekly public-signal snapshots show category-relative movement instead of a one-time market view.
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Connected decision outputs — Product and integration data support comparisons, alternatives, rankings, landscapes, and stack recommendations.
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Transparent methodology — We document sources, limitations, missing evidence, and editorial controls; no vendor pays for placement.
How We're Different
The distinction is the connected data asset: evidence is collected, normalized, related, and refreshed so visitors can understand markets, evaluate options, and design a stack.
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Historical signals, not one-time snapshots — Weekly snapshots make changes in public adoption and momentum signals visible in their category context.
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Evidence and relationship validation — Source traceability, pricing verification, missing-data handling, and verified integration relationships are checked before they inform decision outputs.
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Decision outputs, not generic summaries — The same structured evidence supports comparisons, alternatives, rankings, market landscapes, and explainable reference architectures.
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Independent and transparent — No vendor pays for placement. We document sources and methodology, and identify missing evidence rather than guessing.