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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

Its key goals are to ensure data quality, consistency, and usability and align data with analytical models or reporting needs. It may also be sent directly to dashboards, APIs, or ML models. How fresh or real-time does the data need to be? What tools and data models best fit our requirements?

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United Airlines sets its flight plan for gen AI success

CIO Business Intelligence

With the core architectural backbone of the airlines gen AI roadmap in place, including United Data Hub and an AI and ML platform dubbed Mars, Birnbaum has released a handful of models into production use for employees and customers alike.

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6 data risks CIOs should be paranoid about

CIO Business Intelligence

To address this gap and ensure the data supply chain receives enough top-level attention, CIOs have hired or partnered with chief data officers, entrusting them to address the data debt , automate data pipelines , and transform to a proactive data governance model focusing on health metrics, data quality , and data model interoperability. [

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Key takeaways for CIOs from AWS re:Invent 2024

CIO Business Intelligence

On the model and model development front, AWS CEO Matt Garman unveiled new Nova foundational models, which he said would offer better value, especially in terms of cost, than most rival models. On the storage front, AWS unveiled S3 Table Buckets and the S3 Metadata features.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data. The mechanism periodically scans a data catalog like the AWS Glue Data Catalog for tables to convert with XTable.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

These strategies, such as investing in AI-powered cleansing tools and adopting federated governance models, not only address the current data quality challenges but also pave the way for improved decision-making, operational efficiency and customer satisfaction. When financial data is inconsistent, reporting becomes unreliable.

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Outdated business apps can cloud your AI vision

CIO Business Intelligence

Stone called outdated apps a multi-trillion-dollar problem, even after organizations have spent the past decade focused on modernizing their infrastructure to deal with big data. This allows for the extraction and integration of data into AI models without overhauling entire platforms, Erolin says.