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8 Ways Successful Online Business Leverage Big Data

Smart Data Collective

Big data technology is disrupting almost every industry in the modern economy. Global businesses are projected to spend over $103 billion on big data by 2027. While many industries benefit from the growing use of big data, online businesses are among those most affected. You can check them out below!

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3 AI Trends from the Big Data & AI Toronto Conference

DataRobot Blog

Organizations are looking for AI platforms that drive efficiency, scalability, and best practices, trends that were very clear at Big Data & AI Toronto. DataRobot Booth at Big Data & AI Toronto 2022. These accelerators are specifically designed to help organizations accelerate from data to results.

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How AWS helped Altron Group accelerate their vision for optimized customer engagement

AWS Big Data

Altron is a pioneer of providing data-driven solutions for their customers by combining technical expertise with in-depth customer understanding to provide highly differentiated technology solutions. Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

The Orca Platform is powered by a state-of-the-art anomaly detection system that uses cutting-edge ML algorithms and big data capabilities to detect potential security threats and alert customers in real time, ensuring maximum security for their cloud environment. Why did Orca choose Apache Iceberg?

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Extend your data mesh with Amazon Athena and federated views

AWS Big Data

This query is fairly complex: it involves multiple joins and requires special knowledge of the correct way to calculate profit metrics that other end-users may not possess. For more information on using views with federated data sources, see Querying federated views. Big Data Architect on Amazon Athena.

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Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data). What data do we have?

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Improve reliability and reduce costs of your Apache Spark workloads with vertical autoscaling on Amazon EMR on EKS

AWS Big Data

When enabled, it uses VPA to track the resource utilization of your EMR Spark jobs and derive recommendations for resource assignments for Spark executor pods based on this data. The data, fetched from the Kubernetes Metric Server, feeds into statistical models that VPA constructs in order to build recommendations.

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