Articles

Machine Learning (ML) and Artificial Intelligence (AI) are having an increasingly transformative impact on all industries and are already used in many mission-critical use cases in production, bringing considerable value. Data engineering, which combines ETL pipelines with other workflows managing data and machine learning operations, is also significantly impacted. The Intelligent Data Engineering and Automation framework offers the groundwork for intelligent automation processes. However, ML/AI are not the only disruptive forces; new Big Data technologies inspired by Web2.0 companies are also reshaping the Internet. Companies having the largest Big Data footprints not only provide applications with a Big Data operational model but also source their competitive advantage from data in the form of AI services and, consequently, impact the cost/performance equilibrium of ETL pipelines. All these technologies and reasons help explain why the traditional ETL pipeline design should adapt to current and emerging technologies and may be enhanced through artificial intelligence.

Scalable architecture principles for data warehousing are introduced to support population health management and predictive analytics. These principles are validated through the design of an accompanying Data Pipeline that allows the integration of non-traditional data sources, the use of real-time data for descriptive analytics dashboards, and support for the generation of supervised Machine Learning models. Several analytical capabilities have been implemented to exemplify the practical application of the principles, including predictive models for Risk Stratification in health care. Optimal cost-effectiveness and performance considerations ensure the practical relevance of the architectural principles and associated Data Pipeline. In recent years, the availability of Low-Cost Data Storage services and the increasing popularity of Streaming technologies opened new possibilities for the storage and processing of Streaming data on a near-real-time basis. These technologies can help Developing Countries in tackling many relevant issues such as Urban Planning, Environmental Management, Migration Policies, etc. A multi-tier approach combining Cloud-based Storage with Data Warehousing and Data Mining technologies can offer an interesting architecture to exploit Big Data related to populations.

Healthcare is increasingly recognized as a data-intensive industry. Multi-hospital networks, among other organizations, face mounting operational and governance challenges because of rigid data-integration pipelines that support all data sources and destinations in the network. These pipelines have become difficult to modify, causing them to lag behind the changing needs of the clinical operation. Scalable data-pipeline architectures better support clinical decision making, optimize hospital operations, ease data quality and compliance concerns, and contribute to improved patient outcomes. Meeting scalability goals requires breaking up monolithic data-integration pipelines into smaller decoupled components and aligning service-level agreements of pipeline components and source systems. Parallelization and adoption of distributed data-warehouse technology mitigate the burden of ingesting data into a multi-hospital network. However, latency requirements still warrant the construction of separate pipelines for data ingress from clinical devices, electronic health records, and external laboratory-information systems. Healthcare associations recommend near real-time data availability for a growing list of clinical and operational applications. Mishandling the real-time ingestion of data from clinical devices, in particular, compromises availability and performance. Scalable architectural patterns for real-time streaming Ingestion from heterogeneous data sources, transport processes, and back-end processing structures are detailed.

Data and artificial intelligence (AI) models lack the governance and protective mechanisms that traditional applications enjoy through software engineering (DevOps) practices in industry. The complexities of the enterprise CI/CD pipelines, which span across source control, continuous integration, continuous delivery deployments, testing, monitoring for observability, blocking and telemetry for production, enterprise dashboards with pipelines and project health, for AI models and Data in Enterprise environments is presented. Data at Rest, Metadata and Smart data through automation that provide lineage and tracking automation for ML models are discussed. Cloud DevOps ecosystem built with Azure DevOps repository and Pipelines with extensions, which enables orchestration creation and data pipeline YAML templates that embrace best CI/CD practices guide is discussed with detailed indices for enterprise reference. The enterprise ecosystem provides seamless package management integrated with other Azure DevOps extensions and third-party services availability. The pipelines provide the ability to deploy to any environments read from the pipeline YAML config. The development of pipeline YAML is driven as per the extensions installed and project structure for easy consumption by developers with integrated documentation and additional metadata to help in easy maintainability. These pipelines templates can be used to build, test, and publish data artifacts in enterprise data pipeline, enterprise database automation.

A Changing World Demands Optimized Health Surveillance Systems – and How Data Engineering Can Help There is a growing urgency to manage the public health and emergency response practices effectively today, in light of complex and emerging health threats. Fortunately, a host of new tools, including big and streaming data sources, methods such as machine learning, new types of hardware like blockchain or secure enclaves, and means of data storage and retrieval, have emerged. But, with these innovations comes a grand challenge: how to blend with, and adapt them to, the traditional public health practices. The long-in-place infrastructures and protocols to protect and ensure the welfare of communities are in need of change, or at least update, to enhance their marked longevity of impact directly on the health outcomes and community wellbeing they were designed to fortify. It is in this vein that the essay is written and composed. The investigation in this essay is to query what, particularly, might be the aspects and influences of the emerging veritable cornucopia of new data engineering frameworks that are either being developed specifically for health surveillance and wellness, or are available to be co opted from devices and services already thriving in the current market and research milieu. Knowing what these ways may be could well aid in molding their uptake and spread, ensuring their beneficial impacts on those communities who stand to gain the most. The essay is divided into several key segments. After this introduction, section two details the research methods. In the section that follows, the maximum health outcome potentials of these novel frameworks are reviewed. Part four of the essay takes a more critical approach, addressing how the success of these methods may be hindered and future research avenues. Lastly, the concluding information suggests some actions to take to aid best suit the implementation of these ways, and suggests some thoughts for further research after the completion of these inquiriestrand [1].