A Business and Technologies journey

We are going to illustrate how to use Snowflake to process structured data, semi-structured data (json) and  geospatial data (geojson) to produce some analytics. The exemple is quite simple and only aims at illustrating some of the Snowflake capabilities related to geospatial and semi-structured data.  For this illustration we will use the cadastre, an official record of the owners of land used for calculating the amount of tax owed  and property sales datasets. It's a breakdown of the territory up to the individual parcels. Loading the cadastre dataset The french cadastre is available here: https://www.data.gouv.fr/fr/datasets/cadastre/ The cadastre source is a huge geojson file with a root "FeatureCollection". In order to optimise the processing, I first split the geojson into individual feature with a simple loop in JavaScript and node.js: fs = require ( 'fs' ) fs . readFile ( '/Users/USERNAME/Documents/dev/immo/cadastre-35-parcelles.json' , 'utf8...

A while ago I wrote a blog post about the 7 dimensions of the Digital continuity. This blog was about manufacturing and if you look at the common definition of digital continuity aka Digital Thread, it will refer to the ability to seamlessly collect, share and generate accurate insights from data across the product life cycle from its design to its end of life in order to operate the business, and it's not only applicable to manufacturing but also to all other industries where data is usually produced in silos, has to be processed by multiple specialised solutions, can live is different cloud vendors, must be acquired and shared from/to third parties (internal or external). Actually if we generalise these topics about seamlessly collect, share and generate accurate insights, we touch the extended enterprise,  "a loosely coupled, self-organizing network of firms that combine their economic output to provide products and services offerings to the market" (*Wikipedia). From...

There are multiple use cases where you need to combine ERP, PLM and IoT data to produce valuable business insights.  Proactive and predictive maintenance - with IoT coming from the product but also from maintenance tooling Product testing analysis  - with IoT coming from the live testing of the product Fabrication supervision and optimisation - with IoT coming from the shop floor Anomaly lineage  - with IoT from in service product The challenge is still that these data live in silos sometimes in different divisions or even collected by third parties and the sources are structured and semi-structured. Snowflake can act as a single source of truth combining the structured data sources (ERP, PLM) with IoT coming from all stages of the product lifecycle. The Snowflake Cloud Data Platform is able to load and process structured and semi-structured data (including IoT) at a massive scale.   Combined with the unique Snowflake data sharing capability, it's also possible ...

Initially I wanted to make a post about agility and two speed IT.  Two speed IT is a quite old concept and even declared deprecated by its creator the BCG. Indeed in term of process most of the biggest organisations have now converged to the Agile methodology for both legacy and all brand new digital services and business processes. It closes the gap between how the projects are managed on legacy systems and new ones. But does agile mean agility ? Not so sure  ... what we see is still limited capacity to innovate in a given constrained period of time. What about the Data hub in this context ? The data hub allows to create an abstraction level on top of the legacy systems in order to keep the pace of the digital transformation. Data Hub allows to ingest the data as-is whatever the sources are (including lecacy systems, ERP, etc.), harmonise them to a unified business model and then create wide range of services to serve the new requirements. All that with maximum agility....