Data engineering

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IOblend: Simplifying Feature Stores for Modern MLOps

AI, Data analytics, Data engineering, Data Integration, DataOps, Technology /

IOblend: Simplifying Feature Stores for Modern MLOps Feature stores emerged to solve a real challenge in machine learning: managing features across models, maintaining consistency between training and inference, and ensuring proper governance. To meet this need, many solutions introduced new infrastructure layers—Redis, DynamoDB, Feast-style APIs, and others. While these tools provided powerful capabilities, they also […]

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Rethinking the Feature Store concept for MLOps

AI, Data analytics, Data engineering, Data Integration, DataOps, Technology /

Rethinking the Feature Store concept for MLOps Today we talk about Feature Stores. The recent Databricks acquisition of Tecton raised an interesting question for us: can we make a feature store work with any infra just as easily as a dedicated system using IOblend? Let’s have a look. How a Feature Store Works Today Machine

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CRM + ERP: Powering Predictive Analytics

AI, Airlines, Data analytics, Data engineering, Data Integration, DataOps, Technology /

The Data-Driven Value Chain: Predictive Analytics with CRM and ERP  📊 Did you know? A study on real-time data integration platforms revealed that organisations can reduce their average response time to supply chain disruptions from 5.2 hours to just 37 minutes.  A Unified Data Landscape  The modern value chain is a complex ecosystem where every component is interconnected,

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Enhancing Data Migrations with IOblend Agentic AI ETL

AI, Data analytics, Data engineering, Data Integration, DataOps, Technology, Uncategorized /

LeanData Optimising Cloud Migration: for Telecoms with Agentic AI ETL  📡 Did you know? The global telecommunications industry is projected to create over £120 billion in value from agentic AI by 2026.  The Dawn of Agentic AI ETL  For data experts in the telecoms sector, the term ETL—Extract, Transform, Load—is a familiar, if often laborious, process. It’s

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LeanData: Reduce Data Waste & Boost Efficiency

AI, Data analytics, Data engineering, Data Integration, DataOps, Technology /

LeanData Strategy: Reduce Data Waste & Boost Efficiency | IOblend 📊 Did you know? Globally, we generate around 50 million tonnes of e-waste every year.  What is LeanData? LeanData is more than a passing trend — it’s a disciplined, results-focused approach to data management.At its core, LeanData means shifting from a “collect everything, sort it later” mentality to

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The Data Deluge: Are You Ready?

AI, Data analytics, Data engineering, Data Integration, DataOps, Technology /

The Data Deluge: Are You Ready? 📰 Did you know? Some modern data centres are being designed with modularity in mind, allowing them to expand upwards – effectively “raising the roof” – to accommodate future increases in data demand without significant structural overhauls. — Raising the data roof refers to designing and implementing a data

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The Proactive Shift: Harnessing Data to Transform Healthcare

AI, Data analytics, Data engineering, Data Integration, DataOps, Technology /

The Proactive Shift: Harnessing Data to Transform Healthcare Outcomes  🔔 Did You Know? According to the National Institutes of Health, the implementation of data analytics in healthcare settings can reduce hospital readmissions by over 33%.  The Proactive Healthcare Paradigm The healthcare industry has traditionally operated on a reactive model, where intervention occurs only after symptoms manifest

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PoC to Production: Accelerating AI Deployment with IOblend

AI, Airlines, Data analytics, Data engineering, Data Integration, DataOps, flight safety, Technology /

PoC to Production: Accelerating AI Deployment with IOblend 💭 Did You Know? While a staggering 92% of companies are actively experimenting with Artificial Intelligence, a mere 1% ever achieve full maturity in deploying AI solutions at scale. The AI Production Journey A Proof of Concept (PoC) in AI serves as a small-scale, experimental project designed

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