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

KDnuggets

Streaming: Use tools like Kafka or event-driven APIs to ingest data continuously. Its key goals are to store data in a format that supports fast querying and scalability and to enable real-time or near-real-time access for decision-making. Key questions: Should you use a data warehouse, a data lake, or a hybrid (lakehouse) approach?

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. You get the picture.

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Unbundling the Graph in GraphRAG

O'Reilly on Data

Entity resolution merges the entities which appear consistently across two or more structured data sources, while preserving evidence decisions. A generalized, unbundled workflow A more accountable approach to GraphRAG is to unbundle the process of knowledge graph construction, paying special attention to data quality.

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Zoho unveils Zia Hubs, its answer to Copilot and Duet AI for unstructured content intelligence

CIO Business Intelligence

Most unstructured data is text-based, meaning pertinent information lives within email conversations, social media posts, word processor documents, or audio and video transcripts. With the rapid adoption of AI tools across enterprise functions, the need to make unstructured data useful is growing exponentially.

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Battle bots: RPA and agentic AI

CIO Business Intelligence

It operates through predefined workflows, handling structured data in tasks such as data entry, invoice processing, and report generation. Agentic AI, on the other hand, represents more capable autonomous decision-making, learning, and interaction. It operates within well-defined boundaries and workflows.

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

AWS Big Data

Data architecture has evolved significantly to handle growing data volumes and diverse workloads. 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.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO Business Intelligence

Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. In many cases, this eliminates the need for specialized teams, extensive data labeling, and complex machine-learning pipelines.