Crag Mesh Blend is a Tokyo-based software platform that unifies fragmented enterprise data into a single, queryable mesh. Built for teams that need answers in minutes, not days.
Ingests data from relational databases, REST APIs, and CSV/Excel sources. Builds a virtual schema that your analysts query with…
Listens to the binary log of your source databases and pushes row-level changes to downstream consumers within three seconds.…
Traces every field back to its origin table and flags columns that contain personal information as defined under Japan's Act on…
A browser-based, no-code tool for mapping a new data source. You drag in a source, preview its schema, map fields to your…
Kenji Murakami spent eight years as a data infrastructure engineer at a mid-size logistics firm in Yokohama, watching the same problem repeat itself: every department had its own database, its own export schedule, its…
Book a thirty-minute demo with one of our Tokyo-based engineers. We will map your current data sources and show you what a unified query layer looks like for your specific setup.
The term 'data mesh' has been circulating in Japanese IT departments since around 2022, but the explanations tend to be either too abstract or too tied to the specific tooling of a particular vendor. This article explains the concept plainly, describes where it fits in a typical Japanese enterprise data environment, and points out where it does not fit.
Read more →Japan's Act on the Protection of Personal Information was significantly revised in 2022, and the amendments that came into force that year added new obligations around data lineage, breach notification, and third-party data transfers. For companies that handle personal data across multiple systems, demonstrating compliance requires more than a privacy policy. It requires documented evidence of where personal data lives, who can access it, and what happens to it.
Read more →Cloud-managed data platforms are the default assumption in most vendor documentation, but a significant number of Japanese enterprises. Particularly in financial services and manufacturing. Operate under security policies that require data to remain in their own facilities. Deploying a data mesh on-premise introduces a set of constraints that are worth understanding before you start.
Read more →Connect databases, SaaS APIs, and flat files into one queryable layer. No ETL pipeline to maintain, no warehouse to rebuild every quarter.
Queries are routed to the nearest data node automatically. Latency stays under 200ms even across cross-regional sources.
Every team member sees exactly the data their role permits. Permissions are set once at the schema level and propagate everywhere.
Every read and write is logged with a timestamp, user ID, and query fingerprint. Compliance teams stop asking for reports. They pull them directly.
Drag a source, map its fields, set a refresh cadence. A new connector takes about twelve minutes for a non-technical user.
Deploy inside your own data centre in Osaka or Tokyo, or run on a managed cloud instance. The binary is the same either way.
Answer three questions about your current data setup and we will point you to the module that solves your most immediate problem.
"Kenji Murakami is the founder and CEO of Crag Mesh Blend. He trained as a systems engineer at Waseda University and spent eight years building data infrastructure at a logistics company in Yokohama before founding Crag Mesh Blend in 2019. The specific frustration that pushed him to start the company was a three-day spreadsheet reconciliation exercise that should have taken thirty minutes. He keeps a small whiteboard above his desk in the Kofu office where he sketches query plans by hand before writing any code. His preferred tool for debugging is still a terminal and a cup of Kyoto-roasted drip coffee."
No. The Core module builds a virtual schema and routes queries live to your source systems. Results are cached at a TTL you configure, but no data is duplicated into a Meshblend-managed store. Your data stays where it is.
Yes. We ship a Docker image and a Helm chart that runs on standard Kubernetes. We have tested this on bare-metal servers in Osaka and Tokyo co-location facilities. The on-premise binary is identical to the cloud-managed version.
The Govern module scans your virtual schema for columns that match common PII patterns. Names, addresses, phone numbers, My Number fields. And flags them. It then generates a lineage report showing where each flagged field originates and who has queried it. This does not replace legal advice, but it gives your compliance officer a documented starting point.
Currently PostgreSQL 14 and 15, MySQL 8, and Oracle 19c. These are the versions most commonly running in Japanese enterprise environments. Support for SQL Server 2022 is in the roadmap for Q1 2027.
Our standard onboarding is four weeks: one week for discovery and connector mapping, two weeks for integration and testing, one week for team training and go-live. For simpler setups with fewer than five data sources, teams are often live in two weeks.