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The Future of the Data Lakehouse – Open

CIO Business Intelligence

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

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The Future of the Data Lakehouse – Open

Cloudera

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

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Three Trends for Modernizing Analytics and Data Warehousing in 2019

Cloudera

Business intelligence (BI), an umbrella term coined in 1989 by Howard Dresner, Chief Research Officer at Dresner Advisory Services, refers to the ability of end-users to access and analyze enterprise data. The most common big data use case is data warehouse optimization. Natural Language Analysis and Streaming Data Analytics.

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The New Normal for FP&A: Data Analytics

Jedox

Too often, Finance & FP&A has been constrained by the data architecture our organizations have built (or maybe not built) over the past twenty or so years. Typically, we take our multiple data sources and perform some level of ETL on the data.

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Breaking State and Local Data Silos with Modern Data Architectures

Cloudera

Modern data architectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern data architectures (MDAs). Deploying modern data architectures. Lack of sharing hinders the elimination of fraud, waste, and abuse. Forrester ).

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Supporting Transformation with an Integrated Data Platform. Three Common Questions Answered.

Cloudera

CDOs are under increasing pressure to reduce costs by moving data and workloads to the cloud, similar to what has happened with business applications during the last decade. Our upcoming webinar is centered on how an integrated data platform supports the data strategy and goals of becoming a data-driven company.

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A Day in the Life of a DataOps Engineer

DataKitchen

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Figure 2: Example data pipeline with DataOps automation. In this project, I automated data extraction from SFTP, the public websites, and the email attachments. About the Author.

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