O'Reilly Media - Ideas

JUNE 4, 2019

Real-time Data Foundations: Spark , August 15. Visualization and Presentation of Data , August 15. Python Data Science Full Throttle with Paul Deitel: Introductory AI, Big Data and Cloud Case Studies , September 24. AWS Certified Big Data - Specialty Crash Course , June 26-27.

Course 66

Course Training Artificial Inteligence Software Review 66

O'Reilly Media - Ideas

JUNE 5, 2019

Real-time Data Foundations: Spark , August 15. Visualization and Presentation of Data , August 15. Python Data Science Full Throttle with Paul Deitel: Introductory AI, Big Data and Cloud Case Studies , September 24. AWS Certified Big Data - Specialty Crash Course , June 26-27.

Course 49

Course Training Artificial Inteligence Software Review 49

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

Cloudera

AUGUST 31, 2021

It outperforms other data warehouses on all sizes and types of data, including structured and unstructured, while scaling cost-effectively past petabytes. Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. Migration of historical data from EDW Platform.

Data 82

Data Analytics Cloud Technical Review 82

Altexsoft

JUNE 11, 2021

An Italian management consulting company HSPI publishes a database of process mining projects and case studies annually. In the 2020 application database , there are 551 case studies from 27 countries around the world, proving the spread of process mining adoption and growth of interest in these techniques.

Applications 69

Applications Weak Development Team Systems Review Software Review 69

Altexsoft

APRIL 13, 2023

You can read the details on them in the linked articles, but in short, data warehouses are mostly used to store structured data and enable business intelligence , while data lakes support all types of data and fuel big data analytics and machine learning. Data siloes. Lack of skilled experts.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Apiumhub

APRIL 5, 2022

With over 1000 practical case studies presented on the past 6 editions and with new geo events in the MEA and the APAC region, the event is a worldwide movement, ushering the community of data, analytics and AI practitioners across functions, companies, industries, sectors, countries and regions to collaborate, benchmark, share and innovate.

Innovation 52

Innovation Data Artificial Inteligence Case Study 52

CIO

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). You can intuitively query the data from the data lake.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

Kentik

MARCH 7, 2023

Solving these problems for distributed cloud networks has required a big data approach, ultimately resulting in the evolution of network observability. Rich context and real-time datasets allow network engineers to dynamically filter, drill down, and map networks as queries adjust. Leverage automated insights and response flows.

Network 99

Network Cloud Data Case Study 99

Exadel

AUGUST 26, 2020

So, to know what data is available and in what structure it is organized simplifies the overall business processes and makes it possible to see the whole picture in a clear and transparent way. For example, a company may have millions of lines of data in its database, but business leaders need a summary report for just the previous month.

Analytics 52

Analytics Data How To Machine Learning 52

Kentik

OCTOBER 25, 2016

Big data to the rescue. DDoS is a big data problem — too big for scale-up architecture. By recognizing that DDoS is a big data problem and removing the constraints of scale-up architecture. Monitor against multiple data dimensions. For more detail, read our PenTeleData case study.

Scalability 40

Scalability Architecture Big Data Analytics 40

Kentik

MAY 31, 2017

It’s high time to move away from this legacy paradigm to a unified, scalable, real-time solution built on the power of big data. Kentik’s founders, who ran large network operations at Akamai, Netflix, YouTube, and Cloudflare, well understand the challenges faced by teams working with siloed legacy tools and fragmented data sets.

Network 40

Network Tools Big Data Metrics 40

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

DECEMBER 9, 2020

Diagnostic analytics identifies patterns and dependencies in available data, explaining why something happened. Predictive analytics creates probable forecasts of what will happen in the future, using machine learning techniques to operate big data volumes. Introducing data engineering and data science expertise.

Analytics 102

Analytics Technical Review Technology Applications 102

Agile Engine

DECEMBER 27, 2023

What is a data analytics consultancy? Big data consulting services 5. 4 types of data analysis 6. Data analytics use cases by industry 7. The data analytics process 8. What to look for when hiring a data analytics consultancy 10. Case study: leveraging AgileEngine as a data solutions vendor 11.

Big Data 52

Big Data Analytics Data Analysis 52

Datavail

SEPTEMBER 8, 2022

Big data presents challenges in terms of volume, velocity, and variety—but that doesn’t mean you have to suffer from a bloated IT ecosystem to address these challenges. In fact, many businesses can realize significant advantages from streamlining their data integration pipelines, trimming away unnecessary tools and services.

Data 40

Data Nonprofit Quality Assurance Education 40