Altexsoft

OCTOBER 24, 2019

We will define how enterprise warehouses are different from the usual ones, what types of data warehouses exist, and how they work. The focus of this material is to provide information about the business value of each architectural and conceptual approach to building a warehouse. What is an Enterprise Data Warehouse?

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

MARCH 31, 2020

These can be data science teams , data analysts, BI engineers, chief product officers , marketers, or any other specialists that rely on data in their work. The simplest illustration for a data pipeline. Data pipeline components. Data pipeline components. When do you need a data pipeline?

Data 76

Data Storage Analytics Data Center 76

Altexsoft

FEBRUARY 22, 2022

Such an approach requires a great deal of investment since a whole ecosystem has to be created, including IoT sensors installation, acquiring specialized software, creating and maintaining machine learning (ML) models, engaging IT and data science specialists , and so on. Collecting data from connected sensors and external sources.

Software Review 52

Software Review Technical Review Software Technology 52

Altexsoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. How data engineering works in a nutshell.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Mobilunity

DECEMBER 28, 2022

Neural networks are composed of interconnected processing nodes called neurons, which can learn to recognize patterns of input data. Business intelligence. Business intelligence involves using data analysis techniques to help businesses make better decisions about their operations and strategies.

Machine Learning 52

Machine Learning Artificial Inteligence Engineering How To 52

Altexsoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. The number of possible applications tends to grow due to the rise of IoT , Big Data analytics , streaming media, smart manufacturing, predictive maintenance , and other data-intensive technologies.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

OCTOBER 2, 2020

Key disciplines and roles in data management. Data architecture: aligning technologies with business goals. Specialist responsible for the area: data architect. Data architecture is a starting point for any data management model. Snowflake data management processes.

Database Administration 64

Database Administration Strategy Data Technical Review 64

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

MAY 14, 2021

To understand Big Data, you need to get acquainted with its attributes known as the four V’s: Volume is what hides in the “big” part of Big Data. This relates to terabytes to petabytes of information coming from a range of sources such as IoT devices, social media, text files, business transactions, etc. Apache Hadoop.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

DECEMBER 9, 2020

We will describe each level from the following perspectives: differences on the operational level; analytics tools companies use to manage and analyze data; business intelligence applications in real life; challenges to overcome and key changes that lead to transition. Data warehouse architecture.

Analytics 102

Analytics Technical Review Technology Applications 102

Agile Engine

DECEMBER 27, 2023

Case study: leveraging AgileEngine as a data solutions vendor 11. Key takeaways Any organization that operates online and collects data can benefit from a data analytics consultancy, from blockchain and IoT, to healthcare and financial services The market for data analytics globally was valued at $112.8

Big Data 52

Big Data Analytics Data Analysis 52

Altexsoft

MARCH 14, 2023

Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. What is a modern data stack? A data stack, in turn, focuses on data : It helps businesses manage data and make the most out of it.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

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