Data Co-construction Round-up (June 2026) RootData is building a trustworthy tokenized-asset data platform that provides users with accurate and timely industry information. We publish a monthly data review, aiming to help users gain a clearer understanding of industry trends by organizing, verifying, and disclosing key data. Read more:

🛡️ CoinMarketCap's Features: 🔹 CMC is a market data platform, not an exchange, trades run via on-chain DEX routing. 🔹 Your assets stay in YOUR wallet; CMC never holds your funds, no withdrawals needed. 🔹 The Portfolio feature tracks assets only, it's not a crypto wallet.

# Learning Palantir Foundry 🚀 Bring complex logic that no-code can't reach into your data platform, along with full software-engineering quality control. That's what Code Repositories delivers. 📌 Title and Feature URL Title: Code Repositories (Python Transforms) URL: 📝 Overview Code Repositories is a web-based integrated development environment (IDE) for creating and collaborating on production-ready code within Foundry. It provides a friendly UI over the underlying Git repositories, so teams can work without command-line access. With platform-specific features, you can apply software development practices directly to data engineering. 🔧 How It Works Version control and collaboration are at its core. - Common Git tasks (branching, committing, release tagging) execute through the web UI - Pull requests drive code review, with "highly configurable" permissions that support quality assurance such as mandatory reviews - IntelliSense, linting, error checking, and contextual help dialogs are available across all repository types - Transforms repositories let you author data transformation logic in Python, Java, or SQL with preview and debugging - Functions repositories natively integrate the Ontology and run low-latency business logic in TypeScript or Python 🛠 Practical Usage - Use PySpark to implement billion-row entity resolution and complex business rules in code - Require PR reviews so a second reviewer and CI checks must pass before merge - Add unit tests to guard transform logic against regressions - In Functions repositories, leverage Ontology-data-type autocomplete to write logic safely - Bring machine learning workflows into the platform via model development repositories 🎯 Use Cases - Implementing complex reconciliation and business rules in PySpark that Pipeline Builder can't express - Structurally eliminating "regressions from editing production directly" through mandatory reviews and branch-based workflows - Implementing derived KPIs and validation logic as Functions reused across apps - Managing ML model training and inference code under governance ⚠️ Caveats - The docs note that Japanese translations are machine-generated and unverified, so localized content may have accuracy limitations - Each repository type (Transforms/Functions/Model) supports different languages and purposes, so pick the one that fits your goal - Being a pro-code environment, the quality benefits only materialize if your organization establishes review, CI, and test practices #PalantirFoundry# #DataEngineering#

NHS England has granted external staff from companies including Palantir ‘unlimited access’ to identifiable patient data while working on a part of its flagship data platform. Laura Hughes explains why the change has raised concerns.

At Flink Forward Asia Shenzhen 2026, Feng Wang, Researcher and Head of Open Data Platform at Alibaba Cloud & Vice Chair of the Alibaba Open Source Committee, highlighted the evolving data foundation for AI: "In the AI era, models and data together determine the quality and efficiency of Agents. Apache Flink evolves into Agentic Streaming for AI, working alongside Agentic Lake to build AI-native data platform." The next generation of intelligent agents is built on a unified, AI-native data infrastructure designed for real-time agentic workflows. #AlibabaCloud# #ApacheFlink# #ApachePaimon# #ApacheFluss# #DataAI# #AI# #Agent# #RealTimeData#

“Governance” usually sounds like a separate layer. In practice, it shows up when you try to access data, share it across teams, or ship something to production. Check out this guide to see how Snowflake handles governance inside the data platform itself, including: ✨ Access control built into queries and pipelines ✨ Policies applied directly to data ✨ Consistent governance across apps and workflows

From serving people to serving Agents. At WAIC 2026, ApsaraDB, such as: PolarDB, AnalyticDB, Lindorm etc, unveiled its vision of evolving toward Agentic Database—built on LakeBase, multi-modal memory, and full-chain observability to create a next-gen data platform for the agentic era, powering AI applications at scale.👍 #WAIC# #ApsaraDB# #PolarDB# #AlibabaCloud#

# Snowflake Features and Practical Usage 🚀 People often say Snowflake "separates storage from compute," but once you truly grasp what that means, every discussion about cost, performance, and concurrency suddenly clicks. Let's dig into Snowflake's foundational three-layer architecture. 📌 Title and Feature URL Title: Key Concepts and Architecture URL: 📝 Overview Snowflake is a cloud-native SQL data platform delivered as a fully managed service, with no hardware to manage or software to install. Its architecture is a hybrid of shared-disk and shared-nothing designs, organized into three layers: database storage, query processing (compute), and cloud services. The defining trait is that these three layers scale independently of one another. 🔧 How It Works Each layer has a distinct role: ・Database storage layer: Ingested data is reorganized into an internally optimized, compressed, columnar format and divided into micro-partitions (contiguous units of storage). Snowflake fully manages the physical layout; you only ever interact via SQL. ・Compute layer (virtual warehouses): A virtual warehouse is a cluster of compute resources that runs queries using massively parallel processing (MPP). Each warehouse runs independently, so load on one has no effect on the performance of others. ・Cloud services layer: The "brain" that coordinates everything — authentication and access control, metadata management, query parsing and optimization, and infrastructure management. The core idea: storage is centrally shared (a shared-disk benefit) while processing happens on distributed nodes (a shared-nothing benefit). 🛠 Practical Usage Given this separation model, the first design step is to split compute by workload: ・Provision separate virtual warehouses for ETL, BI dashboards, and data science. Since storage is shared, all warehouses read the same tables without duplicating data. ・A heavy nightly batch won't slow BI queries running on a different warehouse — you eliminate interference structurally, not by tuning. ・For table types, you can use standard Snowflake tables, Apache Iceberg tables backed by your own external cloud storage, or Hybrid Tables for transactional workloads. 🎯 Use Cases ・Permanently fix the "dashboards get slow during the nightly batch" problem by isolating workloads onto separate warehouses. ・Quarantine data scientists' exploratory queries on a dedicated warehouse to protect production analytics. ・Split warehouses per department to make cost visible and chargeable. ⚠️ Caveats ・Compute consumes credits only while a warehouse is running, billed separately from storage. Estimate the two independently. ・Shared storage does not mean shared access — permissions are enforced separately by RBAC in the cloud services layer. ・"Separation" is a logical design principle. Spinning up warehouses indiscriminately increases what you must manage, so split by meaningful workload boundaries. #Snowflake# #DataWarehouse#