Remove 2020 Remove Data Engineering Remove Data Pipeline
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Five Interesting Data Engineering Projects

KDnuggets

As the role of the data engineer continues to grow in the field of data science, so are the many tools being developed to support wrangling all that data. Five of these tools are reviewed here (along with a few bonus tools) that you should pay attention to for your data pipeline work.

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How The Explosive Growth Of Data Access Affects Your Engineer’s Team Efficiency

Smart Data Collective

In fact, you may have even heard about IDC’s new Global DataSphere Forecast, 2021-2025 , which projects that global data production and replication will expand at a compound annual growth rate of 23% during the projection period, reaching 181 zettabytes in 2025. zettabytes of data in 2020, a tenfold increase from 6.5

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The 2021 Executive Guide To Data Science and AI

Applied Data Science

Automation Automating data pipelines and models ➡️ 6. Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team? Big Ideas What to look out for in 2022 1.

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Feature Platforms?—?A New Paradigm in Machine Learning Operations (MLOps)

IBM Data Science in Practice

VC Investment in AI firms rose from USD 3 billion in 2012 to close to USD 75 billion in 2020 This trend led to the proliferation of companies developing tools to address different pain points in the machine learning lifecycle. A feature platform should automatically process the data pipelines to calculate that feature.

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Understanding and predicting urban heat islands at Gramener using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

Solution workflow In this section, we discuss how the different components work together, from data acquisition to spatial modeling and forecasting, serving as the core of the UHI solution. Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020.

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Wearable devices (such as fitness trackers, smart watches and smart rings) alone generated roughly 28 petabytes (28 billion megabytes) of data daily in 2020. And in 2024, global daily data generation surpassed 402 million terabytes (or 402 quintillion bytes). Massive, in fact.

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Santa Reins in his Data to Deliver the Holidays

Alation

The elf teams used data engineering to improve gift matching and deployed big data to scale the naughty and nice list long ago , before either approach was even considered within our warmer climes. Get the latest data cataloging news and trends in your inbox. And this is just the beginning. Subscribe to Alation's Blog.