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Innovative data integration in 2024: Pioneering the future of data integration

CIO Business Intelligence

In the age of big data, where information is generated at an unprecedented rate, the ability to integrate and manage diverse data sources has become a critical business imperative. Traditional data integration methods are often cumbersome, time-consuming, and unable to keep up with the rapidly evolving data landscape.

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What is data architecture? A framework to manage data

CIO Business Intelligence

A scalable data architecture should be able to scale up (adding more resources or processing power to individual machines) and to scale out (adding more machines to distribute the load of the database). Flexible data architectures can integrate new data sources, incorporate new technologies, and evolve with business needs.

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Is Your Finance Team Stuck in the Past? Real-Time Answers

Jet Global

Real-time data integration can help break down data silos and better decision-making. Data governance is crucial when handling financial data. Real-time data enables more accurate forecasting and budgeting. Organisations should define their data strategy before implementation.

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Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy.

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Collibra Provides a Platform for Data Intelligence

David Menninger's Analyst Perspectives

In addition to providing the core functionality for standardizing data governance and enabling self-service data access across a distributed enterprise, Collibra was early to identify the need to provide customers with information about how, when and where data is being produced and consumed across an enterprise.

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Introducing generative AI upgrades for Apache Spark in AWS Glue (preview)

AWS Big Data

To achieve this, we recommend specifying a run configuration when starting an upgrade analysis as follows: Using non-production developer accounts and selecting sample mock datasets that represent your production data but are smaller in size for validation with Spark Upgrades. 2X workers and auto scaling enabled for validation.

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Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.