Smart Data Collective

SEPTEMBER 23, 2020

Type of Data: structured and unstructured from different sources of data Purpose: Cost-efficient big data storage Users: Engineers and scientists Tasks: storing data as well as big data analytics, such as real-time analytics and deep learning Sizes: Store data which might be utilized.

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Smart Data Collective

DECEMBER 1, 2022

As we look ahead to 2022, there are four key trends that organizations should be aware of when it comes to big data: cloud computing, artificial intelligence, automated streaming analytics, and edge computing. Each of these trends will continue to shape the way companies use data in the coming years.

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Smart Data Collective

JUNE 19, 2019

As such, you should concentrate your efforts in positioning your organization to mine the data and use it for predictive analytics and proper planning. The Relationship between Big Data and Risk Management. Big data analytic tools provide essential metrics that you can use to monitor your production.

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AWS Machine Learning Blog

JANUARY 30, 2025

Ben West is a hands-on builder with experience in machine learning, big data analytics, and full-stack software development. In her free time, Lauren enjoys reading an playing the piano and cello.

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Dataconomy

AUGUST 9, 2023

This data can then be collected, analyzed, and utilized to gain insights, make informed decisions, and optimize processes. This data includes information about user behavior, preferences, locations, interactions, transactions, and more. IoT and connected devices : The Internet of Things has played a crucial role in datafication.

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IBM Journey to AI blog

MARCH 20, 2024

artificial intelligence (AI) , edge computing, the Internet of Things (IoT) ). Analytics With the rise of data collected from mobile phones, the Internet of Things (IoT), and other smart devices, companies need to analyze data more quickly than ever before.

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Pickl AI

JULY 20, 2023

Top 15 Data Analytics Projects in 2023 for Beginners to Experienced Levels: Data Analytics Projects allow aspirants in the field to display their proficiency to employers and acquire job roles. These may range from Data Analytics projects for beginners to experienced ones.

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IBM Journey to AI blog

JUNE 29, 2023

It integrates advanced technologies—like the Internet of Things (IoT), artificial intelligence (AI) and cloud computing —into an organization’s existing manufacturing processes. Industry 4.0

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Dataconomy

APRIL 20, 2023

Digit-computers are capable of processing and analyzing vast amounts of data quickly and accurately, which has enabled significant advances in fields like artificial intelligence, machine learning, and big data analytics.

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IBM Journey to AI blog

SEPTEMBER 4, 2024

Producers and consumers A ‘producer’, in Apache Kafka architecture, is anything that can create data—for example a web server, application or application component, an Internet of Things (IoT) , device and many others. Here are a few of the most striking examples.

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Pickl AI

JUNE 29, 2023

Furthermore, The platform’s versatility extends beyond data analysis. Moreover, one needs to have some proficiency in data analytics and query languages. Learning Splunk equips individuals with valuable data analysis skills, which are highly sought after in today’s data-driven world.

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Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Pickl AI

DECEMBER 2, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Pickl AI

NOVEMBER 25, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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IBM Journey to AI blog

FEBRUARY 6, 2024

Cloud-based applications and services Cloud-based applications and services support myriad business use cases—from backup and disaster recovery to big data analytics to software development. Uber, for example, depends on a microservices architecture to build and release its ride-hailing and food-delivery services quickly.

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Pickl AI

JANUARY 16, 2024

Trends shaping careers, like AI integration and real-time analytics, highlight the evolving industry demands. The Data Analyst career growth section emphasises the value of specialisation in a field with substantial job openings in India. ’ In this digital era, Data Analysts fuel innovation.

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Dataconomy

APRIL 20, 2023

Digit-computers are capable of processing and analyzing vast amounts of data quickly and accurately, which has enabled significant advances in fields like artificial intelligence, machine learning, and big data analytics.

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Pickl AI

AUGUST 24, 2023

This minimizes the risk of data loss and downtime. Innovation: Cloud Computing encourages innovation by providing access to advanced technologies and services, such as artificial intelligence, machine learning, big data analytics, and more.

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Pickl AI

JULY 29, 2024

It utilises the Hadoop Distributed File System (HDFS) and MapReduce for efficient data management, enabling organisations to perform big data analytics and gain valuable insights from their data. Hadoop Cluster Setup Hadoop clusters offer numerous advantages for organisations managing large datasets.

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IBM Journey to AI blog

AUGUST 6, 2024

Big data analytics Serverless dramatically reduces the cost and complexity of writing and deploying code for big data applications. Today, serverless helps developers build scalable big data pipelines without having to manage the underlying infrastructure.

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Pickl AI

JULY 14, 2023

Big Data and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of Big Data analytics. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks.

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Smart Data Collective

NOVEMBER 20, 2022

In addition, contact tracing in certain offerings could combat the coronavirus outbreak via mobile technologies such as GPS, cellphone masts, and AI-powered big data analytics to assist the government in understanding and managing the spread of COVID-19 within their communities.

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Mlearning.ai

AUGUST 18, 2023

As a discipline that includes various technologies and techniques, data science can contribute to the development of new medications, prevention of diseases, diagnostics, and much more. Utilizing Big Data, the Internet of Things, machine learning, artificial intelligence consulting , etc.,

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Discoveries and improvements across seed genetics, site-specific fertilizers, and molecule development for crop protection products have coincided with innovations in generative AI , Internet of Things (IoT) and integrated research and development trial data, and high-performance computing analytical services.

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