O'Reilly Media - Ideas

APRIL 9, 2019

A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. Data engineering is not in the limelight.

Data Engineering 111

Data Engineering Engineering Data Technical Review 111

InfoQ Culture Methods

SEPTEMBER 6, 2019

Is it all about customer-centric business, or is there any data left? Can we integrate data analytics and customer empathy? This article explores how we can move towards a more customer-centric business and what information we require in order to understand the most valuable thing we have: our customer. By Almudena Rodriguez Pardo.

Analytics 65

Analytics Agile Data Big Data 65

O'Reilly Media - Ideas

MAY 1, 2019

Spotlight on Data: Caching Big Data for Machine Learning at Uber with Zhenxiao Luo , June 17. Core Agile , May 9. Spotlight on Data: Data as an Asset with Friederike Schüür and Jen van der Meer , May 20. Agile for Everybody , June 19. Core Agile , July 10. Data science and data tools.

Course 46

Course Training Artificial Inteligence Machine Learning 46

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. When you execute a query against the data lake, data transformation are performed at run-time or in-memory. Classic ETL.

Storage 40

Storage Big Data Google Cloud Analysis 40

Altexsoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Architecture 52

Architecture Data Storage Machine Learning 52

Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. In order to utilize the wealth of data that they already have, companies will be looking for solutions that will give comprehensive access to data from many sources.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Coforge

AUGUST 31, 2020

The drastic shift from traditional and orthodox system frameworks and operations can be unarguably attributed to the influx of intelligent technologies such as Artificial Intelligence, Machine Learning, Big Data, Blockchain/DLT led smart contracts, and widespread usage of cloud systems. .

Insurance 40

Insurance Technology Artificial Inteligence Policies 40

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

FEBRUARY 8, 2021

Machine learning techniques analyze big data from various sources, identify hidden patterns and unobvious relationships between variables, and create complex models that can be retrained to automatically adapt to changing conditions. Define data sources. Cost control. Quality management.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

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. into the digital twin.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

BPM

JANUARY 18, 2019

Continuing the journey toward true business agility initiated in the software realm. Business Architecture is growing as a movement, but it will only find success if it is able to provide an agile method for business transformation. Process mining will be incorporated into larger suites of professional business process analysis tools.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Kentik

JUNE 13, 2016

Detection requires visibility and analysis. Visibility requires information, and analysis requires the ability to contextualize the information. The data is not merely correlated but fully unified to create useful metadata for enhanced context. Reducing a network's intruder dwell time is mostly about detection.

Network 40

Network LAN Big Data Scalability 40

Altexsoft

AUGUST 4, 2021

Data mart vs data warehouse vs data lake architectures. Data marts shouldn’t be confused with OLAP cubes either. An OLAP or Online Analytical Processing cube is the tool used to represent data for analysis in a multidimensional way. For example, a company has a data mart containing all the financial data.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

NOVEMBER 23, 2021

But this data is all over the place: It lives in the cloud, on social media platforms, in operational systems, and on websites, to name a few. Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization.

Virtualization 69

Virtualization Tools Data Architecture 69

Altexsoft

NOVEMBER 23, 2020

It is an essential element of the data management strategy , that enables connectivity between systems and gives access to the content across a wide array of subjects. Consolidated datasets are a prerequisite for accurate analysis, extracting business insights, and reporting. The integral part of ETL is data mapping.

Data 104

Data Transportation Backup Storage 104

Altexsoft

OCTOBER 26, 2022

However, keep in mind that in practice a detailed analysis will have to be carried out concerning all possible corporate areas related to digitization. Cultural changes will also include the introduction of new forms of work and the development of agile skills among employees. Data understanding. CIO vs CDO. Business acumen.

CTO 52

CTO Organization Technical Review Culture 52

Altexsoft

DECEMBER 9, 2020

In the era of global digital transformation , the role of data analysis in decision-making increases greatly. Diagnostic analytics identifies patterns and dependencies in available data, explaining why something happened. Often, no technologies are involved in data analysis. Analytics maturity model.

Analytics 102

Analytics Technical Review Technology Applications 102

Agile Engine

DECEMBER 27, 2023

From emerging trends to hiring a data consultancy, this article has everything you need to navigate the data analytics landscape in 2024. What is a data analytics consultancy? Big data consulting services 5. 4 types of data analysis 6. Data analytics use cases by industry 7. Key takeaways 2.

Big Data 52

Big Data Analytics Data Analysis 52

DataRobot

OCTOBER 3, 2017

Enterprise data architects, data engineers, and business leaders from around the globe gathered in New York last week for the 3-day Strata Data Conference , which featured new technologies, innovations, and many collaborative ideas. He is creating information services for his clients, an emerging use case for SSDP.

Trends 52

Trends Azure Conference Media 52