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Transforming Task Automation: The Future of Intelligent Orchestration

David Menninger's Analyst Perspectives

Integrating with various data sources is crucial for enhancing the capabilities of automation platforms , allowing enterprises to derive actionable insights from all available datasets. This ability facilitates breaking down silos between departments and fosters a collaborative approach to data use.

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Companies to shift AI goals in 2025 — with setbacks inevitable, Forrester predicts

CIO Business Intelligence

Forrester’s top automation predictions for 2025 include: Gen AI will orchestrate less than 1% of core business processes. Forrester said gen AI will affect process design, development, and data integration, thereby reducing design and development time and the need for desktop and mobile interfaces.

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Elevating Data Integration: A Four-Tier Approach to Effective Data Preparation

Data Virtualization

Reading Time: 2 minutes In today’s data-driven landscape, the integration of raw source data into usable business objects is a pivotal step in ensuring that organizations can make informed decisions and maximize the value of their data assets. To achieve these goals, a well-structured.

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Data Integration Patterns in Knowledge Graph Building with GraphDB

Ontotext

The update is to drop and re-import the same graph data into a single atomic transaction. In use cases when the named graph has other meanings or the granularity of the updates is smaller like on the business object level, the user can design an explicit DELETE/INSERT template. Ontotext’s GraphDB Give it a try today!

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CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.

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Introducing a new unified data connection experience with Amazon SageMaker Lakehouse unified data connectivity

AWS Big Data

Third, some services require you to set up and manage compute resources used for federated connectivity, and capabilities like connection testing and data preview arent available in all services. To solve for these challenges, we launched Amazon SageMaker Lakehouse unified data connectivity.

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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

Let’s briefly describe the capabilities of the AWS services we referred above: AWS Glue is a fully managed, serverless, and scalable extract, transform, and load (ETL) service that simplifies the process of discovering, preparing, and loading data for analytics. The following diagram illustrates this architecture.

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