Altexsoft

AUGUST 29, 2023

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 article explains what a data lake is, its architecture, and diverse use cases. Who needs a data lake?

Architecture 52

Architecture Data Storage Machine Learning 52

CIO

MARCH 27, 2024

Le aziende italiane investono in infrastrutture, software e servizi per la gestione e l’analisi dei dati (+18% nel 2023, pari a 2,85 miliardi di euro, secondo l’Osservatorio Big Data & Business Analytics della School of Management del Politecnico di Milano), ma quante sono giunte alla data maturity?

ChatGPT 130

ChatGPT Government Artificial Inteligence Business Intelligence 130

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes. What have customers been telling us?

Data 90

Data Analytics Open Source Architecture 90

Strategy Driven

AUGUST 27, 2015

The Internet and cloud computing have revolutionized the nature of data capture and storage, tempting many companies to adopt a new 'Big Data' philosophy: collect all the data you can; all the time. Big Data is Not Just More Data : That’s because the nature of the data we can now collect has changed.

Big Data 33

Big Data Data Storage Journal 33

Altexsoft

APRIL 22, 2020

So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms. In this article we’ll explain what a data engineer is, the field of their responsibilities, skill sets, and general role description. What is a data engineer?

Data Engineering 69

Data Engineering Engineering Artificial Inteligence Data 69

Altexsoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

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. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Altexsoft

FEBRUARY 8, 2021

diversity of sales channels, complex structure resulting in siloed data and lack of visibility. These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics.

Big Data 59

Big Data Data Google Cloud Storage 59

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis. Migration of historical data from EDW Platform.

Data 80

Data Analytics Cloud Technical Review 80

Apiumhub

AUGUST 12, 2021

This year you will have 6 unique tracks: Cloud Computing: IaaS, PaaS, SaaS DevOps: Microservices, Automation, ASRs Cybersecurity: Threats, Defenses, Tests Data Science: ML, AI, Big Data, Business Analytics Programming languages: C++, Python, Java, Javascript,Net Future & Inspire: Mobility, 5G data networks, Diversity, Blockchain, VR.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Altexsoft

APRIL 13, 2023

Supply chain control tower architecture: main components, integrations, and data sources As we mentioned, an SCCT is typically comprised of multiple components that handle various aspects of supply chain management. Let’s look closer at what’s there under the hood and list the main components, integrations, and data sources.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

JANUARY 22, 2020

Please note: this topic requires some general understanding of analytics and data engineering, so we suggest you read the following articles if you’re new to the topic: Data engineering overview. A complete guide to business intelligence and analytics. The role of business intelligence developer.

Analytics 20

Analytics Data IoT Analysis 20

Altexsoft

MAY 14, 2021

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

Big Data 78

Big Data Analytics Tools Applications 78

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

Altexsoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Traditional data warehouse platform architecture. Data lake architecture example.

Architecture 52

Architecture Data Storage Machine Learning 52

Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Types of metadata. There are multiple ways to categorize metadata.

Tools 59

Tools Technical Review Software Review Systems Review 59

Datavail

JUNE 8, 2023

Data Summit 2023 was filled with thought-provoking sessions and presentations that explored the ever-evolving world of data. From the technical possibilities and challenges of new and emerging technologies to using Big Data for business intelligence, analytics, and other business strategies, this event had something for everyone.

Database Administration 52

Database Administration Data Artificial Inteligence Analytics 52

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

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

APRIL 16, 2021

As the topic is closely related to business intelligence (BI) and data warehousing (DW), we suggest you to get familiar with general terms first: A guide to business intelligence. An overview of data warehouse types. What is data pipeline. Online Analytical Processing Architecture.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

Mobilunity

NOVEMBER 24, 2021

The demand for specialists who know how to process and structure data is growing exponentially. In most digital spheres, especially in fintech, where all business processes are tied to data processing, a good big data engineer is worth their weight in gold. Who Is an ETL Engineer? Data modeling.

Development 52

Development Storage Recruiting Architecture 52

Altexsoft

MARCH 18, 2021

From the late 1980s, when data warehouses came into view, and up to the mid-2000s, ETL was the main method used in creating data warehouses to support business intelligence (BI). As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. Data size and type.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

CIO

AUGUST 5, 2022

Key data visualization benefits include: Unlocking the value big data by enabling people to absorb vast amounts of data at a glance. Identifying errors and inaccuracies in data quickly. They provide designers with the tools they need to create visual representations of large data sets.

Data 296

Data Analytics Travel Business Intelligence 296

Altexsoft

JANUARY 14, 2021

We’ll review all the important aspects of their architecture, deployment, and performance so you can make an informed decision. Before jumping into the comparison of available products right away, it will be a good idea to get acquainted with the data warehousing basics first. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

APRIL 26, 2022

Not long ago setting up a data warehouse — a central information repository enabling business intelligence and analytics — meant purchasing expensive, purpose-built hardware appliances and running a local data center. Data warehousing in a nutshell. Snowflake architecture overview.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

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

AUGUST 21, 2019

Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data. Using specific tools and practices, businesses implement these methods to generate valuable insights. Data engineer. Data scientists.

Development 44

Development Software Engineering Data Engineering Architecture 44

Altexsoft

OCTOBER 21, 2022

It offers high throughput, low latency, and scalability that meets the requirements of Big Data. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. Kafka architecture. Kafka cluster architecture. Kafka cluster and brokers. Scalability.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Cloudera

NOVEMBER 2, 2020

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

Data 91

Data Analytics Storage Big Data 91

Altexsoft

OCTOBER 8, 2021

But more often than not data is scattered across a myriad of disparate platforms, databases, and file systems. What’s more, that data comes in different forms and its volumes keep growing rapidly every day — hence the name of Big Data. Comparison of data integration tools. Snaplogic iPaaS architecture.

Tools 52

Tools Data Software Review Open Source 52

Altexsoft

AUGUST 4, 2021

Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit. What is a data mart? Moreover, not all organizations use data lakes.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

FEBRUARY 22, 2022

Integration with inventory management software is necessary in case you hold a big stock of parts and equipment and want to be in control. Integration with a business intelligence tool is important to receive a holistic analysis of your maintenance processes, track costs, visualize trends, and get actionable insights.

Software Review 52

Software Review Technical Review Software Technology 52

Altexsoft

DECEMBER 14, 2022

Docker architecture core components. Docker Architecture. Docker uses a client-server architecture where the Docker client communicates with the Docker daemon via a RESTful API, UNIX sockets, or a network interface. The Good and the Bad of the SAP Business Intelligence Platform. How to get started with Docker.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

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

NOVEMBER 9, 2021

Traditionally, analytics is associated with business intelligence and data visualization that are focused on studying past events and current processes. Also known as predictive analytics, it’s about discovering hidden patterns and dependencies, forecasting future events, and supporting decisions with data. Extract data.

Analytics 52

Analytics Software Review Systems Review Technical Review 52