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Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

AI News

Ahead of AI & Big Data Expo Europe , Han Heloir, EMEA gen AI senior solutions architect at MongoDB , discusses the future of AI-powered applications and the role of scalable databases in supporting generative AI and enhancing business processes. Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Big Data 273
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Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

AI News

“If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui.

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Boosting Resiliency with an ML-based Telemetry Analytics Architecture | Amazon Web Services

Flipboard

Data proliferation has become a norm and as organizations become more data driven, automating data pipelines that enable data ingestion, curation, …

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A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

Objective of Data Engineering: The main goal is to transform raw data into structured data suitable for downstream tasks such as machine learning. This involves a series of semi-automated or automated operations implemented through data engineering pipeline frameworks.

ETL 130
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Big Data as a Service (BDaaS): A Comprehensive Overview

Pickl AI

Summary: Big Data as a Service (BDaaS) offers organisations scalable, cost-effective solutions for managing and analysing vast data volumes. By outsourcing Big Data functionalities, businesses can focus on deriving insights, improving decision-making, and driving innovation while overcoming infrastructure complexities.

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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

This allows you to create rules that invoke specific actions when certain events occur, enhancing the automation and responsiveness of your observability setup (for more details, see Monitor Amazon Bedrock ). The job could be automated based on a ground truth, or you could use humans to bring in expertise on the matter.