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What is a data engineer? An analytics role in high demand

CIO

Data engineers must also know how to optimize data retrieval and how to develop dashboards, reports, and other visualizations for stakeholders. Depending on the organization, they may also be responsible for communicating data trends.

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Why You Want To Use Looker Studio For Data Visualization on BigQuery

Perficient

If you have built or are building a Data Lake on the Google Cloud Platform (GCP) and BigQuery you already know that BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence.

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Three Trends for Modernizing Analytics and Data Warehousing in 2019

Cloudera

Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments. 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.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

Altexsoft

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Who needs a data lake?

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Top Data science blogs to follow

Apiumhub

This article will list some of the most useful blogs for everyone – from data science experts to complete newbies. In addition to offering advice and learning resources, this list of data science blogs is also full of current news, trends, and opinions from professionals. Top Data Science Blogs to follow.

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Fundamentals of Data Engineering

Xebia

. – AltexSoft All the data processing is done in Big Data frameworks like MapReduce, Spark and Flink. – Jesse Anderson The data engineering field could be thought of as a superset of business intelligence and data warehousing that brings more elements from software engineering.

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Cloud Data Warehouses vs Cloud Data Lakes – Where are the Lines Drawn?

Apps Associates

Performing analytics on the data was possible but took a long time and was mostly done in batch (using map reduce routines written in Java). Although on-premises Hadoop clusters ushered in the era of Big Data and supported data use cases that previously were not possible, many of these systems proved to be costly and cumbersome to maintain.

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