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 the bedrock of any data-driven strategy, enabling the consolidation of vast, disparate datasets. Unlike traditional, rule-based automation, agentic AI employs multiple autonomous agents that work together to set goals, plan actions, and execute complex workflows with minimal human oversight. In an ETL context, these AI agents can dynamically discover, validate, and govern unstructured data, adapting to new challenges in real time and revolutionising how telcos manage their information ecosystems.
The Problem: Migrating a Mountain of Data
The ambition to migrate to the cloud is universal for telcos, promising greater scalability, flexibility, and cost efficiency. However, the path is fraught with significant hurdles, particularly when it comes to data:
- Data Volume and Velocity: Telecoms generate unimaginable quantities of data from network logs, billing systems, and customer interactions. Migrating this “data lake” is not a one-off event but a continuous, high-speed operation that legacy ETL tools simply cannot handle without significant downtime.
- Compliance and Integrity: Data integrity and security are non-negotiable. During migration, any corruption or non-compliance with regulations like GDPR can have severe financial and reputational consequences.
- Cost and Resource Strain: The sheer time and resource commitment of a manual migration can lead to budget overruns. The need for a highly skilled, specialised team to manage multiple, inflexible tools further exacerbates this issue.
Enhancing Data Migrations with IOblend Agentic AI ETL
IOblend offers a paradigm shift, transforming the daunting task of cloud migration and ETL into a seamless, automated process.
- Autonomous Migration: IOblend’s solution uses a real-time Change Data Capture (CDC) approach to synchronise systems. This allows for the swift migration of massive data volumes and complex business logic without any downtime. Once the cloud environment is ready, the legacy systems can be securely decommissioned, a process that might have taken years in the past.
- Low-Code, High-Power ETL: The platform’s intuitive drag-and-drop interface empowers data teams to build sophisticated data pipelines rapidly. This isn’t just simple ingestion; it autogenerates optimised Apache Spark jobs, capable of handling both large-scale batch processing and real-time data streams.
- Built-in DataOps and Compliance: IOblend ensures data trust from the start. Its solution embeds automated data quality checks, robust error handling, and comprehensive audit trails and data lineage.
- Add AI agents into your migration pipelines: IOblend allows embedding of AI agents straight into the data pipelines, so you can process unstructured data, validate quality and combine with the structured datasets on-the-fly.
Revolutionise your cloud migration and ETL processes with IOblend.
IOblend presents a ground-breaking approach to IoT and data integration, revolutionizing the way businesses handle their data. It’s an all-in-one data integration accelerator, boasting real-time, production-grade, managed Apache Spark™ data pipelines that can be set up in mere minutes. This facilitates a massive acceleration in data migration projects, whether from on-prem to cloud or between clouds, thanks to its low code/no code development and automated data management and governance.
IOblend also simplifies the integration of streaming and batch data through Kappa architecture, significantly boosting the efficiency of operational analytics and MLOps. Its system enables the robust and cost-effective delivery of both centralized and federated data architectures, with low latency and massively parallelized data processing, capable of handling over 10 million transactions per second. Additionally, IOblend integrates seamlessly with leading cloud services like Snowflake and Microsoft Azure, underscoring its versatility and broad applicability in various data environments.
At its core, IOblend is an end-to-end enterprise data integration solution built with DataOps capability. It stands out as a versatile ETL product for building and managing data estates with high-grade data flows. The platform powers operational analytics and AI initiatives, drastically reducing the costs and development efforts associated with data projects and data science ventures. It’s engineered to connect to any source, perform in-memory transformations of streaming and batch data, and direct the results to any destination with minimal effort.
IOblend’s use cases are diverse and impactful. It streams live data from factories to automated forecasting models and channels data from IoT sensors to real-time monitoring applications, enabling automated decision-making based on live inputs and historical statistics. Additionally, it handles the movement of production-grade streaming and batch data to and from cloud data warehouses and lakes, powers data exchanges, and feeds applications with data that adheres to complex business rules and governance policies.
The platform comprises two core components: the IOblend Designer and the IOblend Engine. The IOblend Designer is a desktop GUI used for designing, building, and testing data pipeline DAGs, producing metadata that describes the data pipelines. The IOblend Engine, the heart of the system, converts this metadata into Spark streaming jobs executed on any Spark cluster. Available in Developer and Enterprise suites, IOblend supports both local and remote engine operations, catering to a wide range of development and operational needs. It also facilitates collaborative development and pipeline versioning, making it a robust tool for modern data management and analytics
PoC to Production: Accelerating AI Deployment with IOblend
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
AI in Healthcare with Smart Data Pipelines
AI in Healthcare: Powering Progress with Smart Data Pipelines 💉 Did you know? Hospitals in the UK alone produce an astonishing 50 petabytes of data per year, more than double the data managed by the US Library of Congress in 2022! What are Data Pipelines for AI Model Training? In the context of healthcare, this means
The Urgency of Now: Real-Time Data in Analytics
The Urgency of Now: Real-Time Data in Analytics ✈️ Did you know? Every minute of delay in airline operations can cost as much as £100 per minute for a single aircraft. With thousands of flights daily, those minutes add up fast. Just like in aviation, in data analytics, even small delays can lead to big
Still Confused in 2025? AI, ML & Data Science Explained
Still Confused in 2025? AI, ML & Data Science Explained…finally It seems everyone in business circles talks about these days. AI will solve all our business challenges and make/save us a ton of money. AI will replace manual labour with clever agents. It will change the world and our business will be at the forefront
Beyond Spreadsheets: The CFO’s Path to Data-Driven Decisions
Beyond Spreadsheets: The CFO’s Path to Data-Driven Decisions 📊 Did you know? Companies leveraging data-driven insights consistently report a significant uplift in profitability – often exceeding 20%. That’s not just a marginal gain; it’s a game-changer. The Data-Driven CFO The modern Chief Financial Officer operates in a world awash with data. No longer solely focused
Shift Left: Unleashing Data Power with In-Memory Processing
Mind the Gap: Bridging Data Shift Left: Unleashing Data Power with In-Memory Processing 💻 Did you know? Organisations that implement shift-left strategies can experience up to a 30% reduction in compute costs by cleaning data at the source. The Essence of Shifting Left Shifting data compute and governance “left” essentially means moving these processes closer
