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Achieving Trusted AI in Manufacturing

Cloudera

With the ability of manufacturers to store a huge volume of historical data, AI can be applied in general business areas of any industry, like developing recommendations for marketing, supply chain optimization, and new product development. Eliminate data silos. That is a very low number.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

In the subsequent post in our series, we will explore the architectural patterns in building streaming pipelines for real-time BI dashboards, contact center agent, ledger data, personalized real-time recommendation, log analytics, IoT data, Change Data Capture, and real-time marketing data.

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Three Trends for Modernizing Analytics and Data Warehousing in 2019

Cloudera

Natural language analytics and streaming data analytics are emerging technologies that will impact the market. Cloud computing has passed the tipping point, with most organizations comfortable moving critical data and applications to the public cloud. The most common big data use case is data warehouse optimization.

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Modern Data Architecture for Telecommunications

Cloudera

But digital transformation programs are accelerating, services innovation around 5G is continuing apace, and results to the stock market have been robust. . Previously, there were three types of data structures in telco: . Entity data sets — i.e. marketing data lakes . The challenges.

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SoftBank Selects Cloudera Data Platform to Leverage Customer Intelligence While Ensuring Data Security

Cloudera

The company also provides a variety of solutions for enterprises, including data centers, cloud, security, global, artificial intelligence (AI), IoT, and digital marketing services. Supporting Data Access to Achieve Data-Driven Innovation Due to the spread of COVID-19, demand for digital services has increased at SoftBank.

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Data platform trinity: Competitive or complementary?

IBM Big Data Hub

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Metadata plays a key role here in discovering the data assets.

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An Introduction to Disaster Recovery with the Cloudera Data Platform

Cloudera

The goal is to minimize the impact to a customer’s data-driven decision making in the time of an operational crisis. To do that, we need to build standards for CDP implementation that account for failure, mitigate it, and are validated by market adoption. . Conclusion.