Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Happy New Year and welcome to 2019, a year full of possibilities.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

O'Reilly Media - Ideas

MAY 1, 2019

Real-Time Streaming Analytics and Algorithms for AI Applications , May 15. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. Data Analysis Paradigms in the Tidyverse , May 30. IoT Fundamentals , June 17-18.

Course 46

Course Training Artificial Inteligence Machine Learning 46

Altexsoft

JUNE 25, 2019

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Altexsoft

JULY 19, 2022

She formulated the thesis in 2018 and published her first article “How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh” in 2019. What data mesh is NOT. So, to avoid any confusion, please be aware that data mesh is NOT.

Architecture 52

Architecture Data Analytics Data Engineering 52

ParkMyCloud

MAY 29, 2019

A BI analyst has strong skills in database technology, analytics, and reporting tools and excellent knowledge and understanding of computer science, information systems or engineering. BI Analyst can also be described as BI Developers, BI Managers, and Big Data Engineer or Data Scientist. IoT Engineer.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

BPM

JANUARY 18, 2019

As we move into a world that is more and more dominated by technologies such as big data, IoT, and ML, more and more processes will be started by external events. Companies will start demanding that their investments in Predictive Analytics, Machine Learning and AI show a real ROI. Lloyd Dugan BPM.com [link].

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

O'Reilly Media - Ideas

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

Conference 8

Conference Data Data Engineering Artificial Inteligence 8

Altexsoft

OCTOBER 24, 2019

Enterprise data warehouse vs usual data warehouse: what’s the difference? Any data warehouse is a database which is always connected with raw-data sources via data integration tools on one end and analytical interfaces on the other. Reflects the source data. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

Architecture 52

Architecture Artificial Inteligence Technical Review Data 52

Altexsoft

DECEMBER 9, 2020

In the era of global digital transformation , the role of data analysis in decision-making increases greatly. Still, today, according to Deloitte research, insight-driven companies are fewer than those not using an analytical approach to decision-making, even though the majority agrees on its importance. Stages of analytics maturity.

Analytics 102

Analytics Technical Review Technology Applications 102

Datavail

SEPTEMBER 8, 2022

According to an IDG survey , companies now use an average of more than 400 different data sources for their business intelligence and analytics processes. What’s more, 20 percent of these companies are using 1,000 or more sources, far too many to be properly managed by human data engineers. Conclusion.

Data 40

Data Nonprofit Quality Assurance Education 40