Remove Business Intelligence Remove Machine Learning Remove Storage Remove Video
article thumbnail

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

Altexsoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. 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 will simplify further reading.

article thumbnail

5 Technical Reasons for a Cloud Analytics Migration

Datavail

Meanwhile, in an informal survey of attendees at a recent Datavail webinar, the majority (75 percent) of attendees said that their organization was pursuing a “hybrid” (partly on-premises and partly in the cloud) strategy for business intelligence and analytics. Artificial intelligence and machine learning.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Data Science use cases & tools

Apiumhub

Recognition (image, text, audio, video, facial, …). Machine Learning. Machine learning is the backbone of data science. Using machine learning, predictive analytics and data science, self-driving cars can adjust to speed limits, avoid dangerous lane changes and even take passengers on the quickest route. .

Tools 72
article thumbnail

Cost Conscious Data Warehousing with Cloudera Data Platform

Cloudera

Recently, cloud-native data warehouses changed the data warehousing and business intelligence landscape. Appealing directly to end-users in the Lines of Business (LOBs), these solutions can dramatically shorten time to value, lower administrative burdens, and promise continuous agility in response to changing business demands.

Data 98
article thumbnail

Data Architect: Role Description, Skills, Certifications and When to Hire

Altexsoft

What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority. machine learning and deep learning models; and business intelligence tools. By the way, we have a video dedicated to the data engineering working principles.

Data 87
article thumbnail

Accenture’s Smart Data Transition Toolkit Now Available for Cloudera Data Platform

Cloudera

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data 86
article thumbnail

Data Collection for Machine Learning: Steps, Methods, and Best Practices

Altexsoft

We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. It’s the first and essential stage of data-related activities and projects, including business intelligence , machine learning , and big data analytics. Set up data storage technology.