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 to the data source, earlier in the data lifecycle. Instead of centralising everything in a data warehouse after data has been moved and transformed, more processing and control happen at the point of ingestion or even within transactional systems.
The Challenges of Traditional Data Management
Many organisations grapple with latency and complexity in their data pipelines. Extracting, transforming, and loading (ETL) vast datasets into a central data warehouse can be time-consuming and resource-intensive. This delay hinders real-time insights and agile decision-making. Furthermore, governing data that has passed through multiple stages and systems can become a tangled web, making it difficult to ensure quality, compliance, and security. Imagine a retailer trying to react to rapidly changing customer behaviour based on yesterday’s sales figures – they’re already behind the curve.
The Shift-Left Approach
The shift-left approach advocates for processing and governing data near its source. This means cleaning, transforming, and applying governance rules as early as possible in the data lifecycle. This allows for:
Reduced Latency: In-memory processing significantly reduces the time it takes to access and process data, enabling real-time analytics and decision-making.
Improved Data Quality: Cleaning and validating data at the source minimizes errors and ensures higher data quality.
Cost Savings: Processing data in-memory reduces the need for expensive data movement and storage.
Enhanced Governance: Applying governance policies early in the process ensures consistent and compliant data across the organisation.
Increased Agility: Faster data processing and improved data quality enable businesses to respond more quickly to changing market conditions.
IOblend: Empowering Shift-Left Data Processing
IOblend is a comprehensive DataOps solution designed to enable businesses to adopt a shift-left strategy in data processing. By facilitating early-stage data integration, transformation, and validation, IOblend ensures that data issues are addressed promptly, reducing downstream complexities and costs.
Key Capabilities:
In-Memory Compute: Leveraging a custom engine built on Apache Spark™, IOblend executes ETL pipelines in-memory, allowing for real-time data transformations without relying on expensive data warehouses. This approach supports processing data as it moves, enhancing efficiency and reducing latency.
Real-Time Data Integration: IOblend seamlessly integrates both real-time streaming and batch data from diverse sources, including JDBC, APIs, ESBs, dataframes, and flat files. Its architecture supports Change Data Capture (CDC), ensuring that the most recent data is always available for analysis.
Automated Data Quality Management: Built-in data quality features, such as schema validation, deduplication, and error handling, ensure the reliability and validity of data throughout the pipeline. This automation reduces manual intervention and accelerates data readiness.
Low-Code/No-Code Pipeline Development: Full data modelling capabilities like the warehouse, but without a need for one. Users can apply business logic using SQL or Python, streamlining the development process and enabling rapid deployment. No limitations on functionality.
Flexible Deployment Options: Whether on-premises, in the cloud, or hybrid environments, IOblend’s decoupled storage and compute architecture allows for adaptable deployment strategies, ensuring optimal performance and cost-effectiveness. Run computes on the most cost-effective infra (e.g. on-prem data centres, EC2, etc) and save a fortune on data processing,
By shifting data processing to earlier stages, IOblend empowers organizations to detect and resolve data issues promptly, streamline operations, and accelerate time-to-insight.
Ready to shift left and unlock the power of your data? Contact us today!
IOblend: See more. Do more. Deliver better.
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
Real-Time Risk Modelling with Legacy & Modern Data
Risk Modelling in Real-time: Integrating Legacy Oracle/HP Underwriting Data with Modern External Datasets 💼 Did you know that in the time it takes to brew a cup of tea, a real-time risk model could have processed enough data to flag over 60 million potential fraudulent insurance claims? The Real-Time Risk Modelling Imperative Real-time risk modelling is
Unify Clinical & Financial Data to Cut Readmissions
Clinical-Financial Synergy: The Seamless Integration of Clinical and Financial Data to Minimise Readmissions 🚑 Did You Know? Unnecessary hospital readmissions within 30 days represent a colossal financial burden, often reflecting suboptimal transitional care. Clinical-Financial Synergy: The Seamless Integration of Clinical and Financial Data to Minimise Readmissions The Convergence of Clinical and Financial Data The convergence of clinical and financial
Agentic Pipelines and Real-Time Data with Guardrails
The New Era of ETL: Agentic Pipelines and Real-Time Data with Guardrails For years, ETL meant one thing — moving and transforming data in predictable, scheduled batches, often using a multitude of complementary tools. It was practical, reliable, and familiar. But in 2025, well, that’s no longer enough. Let’s have a look at the shift
Real-Time Insurance Claims with CDC and Spark
From Batch to Real-Time: Accelerating Insurance Claims Processing with CDC and Spark 💼 Did you know? In the insurance sector, the move from overnight batch processing to real-time stream processing has been shown to reduce the average claims settlement time from several days to under an hour in highly automated systems. Real-Time Data and Insurance
Agentic AI: The New Standard for ETL Governance
Autonomous Finance: Agentic AI as the New Standard for ETL Governance and Resilience 📌 Did You Know? Autonomous data quality agents deployed by leading financial institutions have been shown to proactively detect and correct up to 95% of critical data quality issues. The Agentic AI Concept Agentic Artificial Intelligence (AI) represents the progression beyond simple prompt-and-response
IOblend: Simplifying Feature Stores for Modern MLOps
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
