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Huawei unveils four strategic directions for the future of finance

CIO Business Intelligence

“We use the peer-to-peer computing of CPUs, GPUs, and NPUs to enable the multi-active data center to run efficiently, just like a computer,” Mr. Cao told the conference. The distributed architecture now outperforms the original core host platform, notably with lower latency. Huawei’s new Data Intelligence Solution 3.0

Finance 98
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Building resilient infrastructure: the key to cloud-native, real-time decision-making

CIO Business Intelligence

“We use the peer-to-peer computing of CPUs, GPUs, and NPUs to enable the multi-active data center to run efficiently, just like a computer,” Mr. Cao told the conference. The distributed architecture now outperforms the original core host platform, notably with lower latency. Huawei’s new Data Intelligence Solution 3.0

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5 misconceptions about cloud data warehouses

IBM Big Data Hub

This approach has several benefits, such as streamlined migration of data from on-premises to the cloud, reduced query tuning requirements and continuity in SRE tooling, automations, and personnel. This enabled data-driven analytics at scale across the organization 4.

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 111
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KGF 2023: Bikes To The Moon, Datastrophies, Abstract Art And A Knowledge Graph Forum To Embrace Them All

Ontotext

Most organisations are missing this ability to connect all the data together. from Q&A with Tim Berners-Lee ) Finally, Sumit highlighted the importance of knowledge graphs to advance semantic data architecture models that allow unified data access and empower flexible data integration.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Open AWS Glue Studio. Choose ETL Jobs.

Data Lake 100
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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.

Data Lake 100