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DataOps For Business Analytics Teams

DataKitchen

Business analysts must rapidly deliver value and simultaneously manage fragile and error-prone analytics production pipelines. Data tables from IT and other data sources require a large amount of repetitive, manual work to be used in analytics. In business analytics, fire-fighting and stress are common.

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Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

AWS Big Data

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.

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An A-Z Data Adventure on Cloudera’s Data Platform

Cloudera

Company data exists in the data lake. Data Catalog profilers have been run on existing databases in the Data Lake. A Cloudera Data Warehouse virtual warehouse with Cloudera Data Visualisation enabled exists. A Cloudera Data Engineering service exists. The Data Scientist.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Data scientists also rely on data analytics to understand datasets and develop algorithms and machine learning models that benefit research or improve business performance.

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How Macmillan Publishers authored success using IBM Cognos Analytics

IBM Big Data Hub

Data analytics in the publishing industry With such a widespread global operation, Macmillan Publishers has a long history of investing in technology that can source deep analytical information about sales, inventory and transportation of their titles in the market.

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5 Signs You’re Using Bad Data to Make Business Decisions

Jet Global

While we’ve seen a rise in technology investment that manages business processes and gathers data, it has greatly exceeded the time and resources put towards data management and governance. There are multiple versions of reports are located on your servers, local machines, and networks that create silos of information .

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9 Distinct Threats to Your BI Implementation

Jet Global

Risk to the business. It’s risk that comes from making expensive decisions on faulty information. The mechanical solution is to build a data warehouse. The solution is to establish a process or a set of automated controls to identify bad data and block it from entering the data warehouse or BI environment.