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Warehouse, Lake or a Lakehouse – What’s Right for you?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Most of you would know the different approaches for building a data and analytics platform. You would have already worked on systems that used traditional warehouses or Hadoop-based data lakes. Selecting one among […].

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Augmented data management: Data fabric versus data mesh

IBM Big Data Hub

Data fabric and data mesh are emerging data management concepts that are meant to address the organizational change and complexities of understanding, governing and working with enterprise data in a hybrid multicloud ecosystem. The good news is that both data architecture concepts are complimentary.

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Achieving Trusted AI in Manufacturing

Cloudera

Without a thorough grounding with trusted data and a robust data platform, AI/ML approaches will be biased and untrusted, and more likely to fail. Simply put, many organizations fail to realize the value of AI because they rely on AI tools and data science that is being applied to data which is faulty to begin with.

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Announcing the 2020 Data Impact Award Winners

Cloudera

We also celebrated the first-ever winner of the Data Impact Achievement Award — a new award category that recognizes one customer who has consistently achieved transformation across their business, pursuing a diverse set of use cases and creating a culture of data-driven innovation. . Data Impact Achievement Award.

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How Cargotec uses metadata replication to enable cross-account data sharing

AWS Big Data

Cargotec captures terabytes of IoT telemetry data from their machinery operated by numerous customers across the globe. This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization. The export process on the source account is a scheduled job.

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A Retrospective of 2018’s Articles

Peter James Thomas

This increase was driven in part by the launch of my new Maths & Science section , articles from which claimed no fewer than 6 slots in the 2018 top 10 articles, when measured by hits [1]. Given the advent of the Maths & Science section, there are now seven categories into which I have split articles. Data Visualisation.