Altexsoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data 87

Data Data Engineering Big Data Architecture 87

Altexsoft

NOVEMBER 10, 2021

A data lakehouse , as the name suggests, is a new data architecture that merges data warehouse and data lake into a single whole, aiming at addressing each one’s limitations. In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes.

Architecture 52

Architecture Data Storage Machine Learning 52

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. 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.

Architecture 52

Architecture Data Storage Machine Learning 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

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

Datavail

JULY 7, 2022

Meanwhile, in an informal survey of attendees at a recent Datavail webinar, the majority (75 percent) of attendees said that their organization was pursuing a “hybrid” (partly on-premises and partly in the cloud) strategy for business intelligence and analytics. data that belongs in a row-column tabular database).

Analytics 52

Analytics Artificial Inteligence Cloud Artificial Intelligence 52

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 97

Data Analytics Storage Big Data 97

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. The data journey from different source systems to a warehouse commonly happens in two ways — ETL and ELT.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

OCTOBER 21, 2022

Apache Kafka is an open-source, distributed streaming platform for messaging, storing, processing, and integrating large data volumes in real time. It offers high throughput, low latency, and scalability that meets the requirements of Big Data. By the way, you can watch our video to understand how APIs work in general.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

SEPTEMBER 23, 2021

Yet, more than often, businesses can’t make use of their most valuable asset — information. Evidently, common storage solutions fail to provide a unified data view and meet the needs of companies for seamless data flow. What is Data Hub? Data warehouse vs data hub. Data hub architecture.

Architecture 52

Architecture Data Storage IoT 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 30, 2021

The movement of data from its source to analytical tools for end users requires a whole infrastructure, and although this flow of data must be automated, building and maintaining it is a task of a data engineer. Data engineers are programmers that create software solutions with big data. Feature engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Altexsoft

DECEMBER 14, 2020

Unstructured data doesn’t have any pre-defined structure to it and comes in all its diversity of forms. The examples of unstructured data vary from imagery and text files like PDF documents to video and audio files, to name a few. Such data can only be used for its intended purpose. Unstructured data examples.

Data 93

Data Artificial Inteligence Storage Analytics 93

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Separated Big Data cluster from other programs for Data Science / Discovery to isolate workloads.

Data 82

Data Analytics Cloud Technical Review 82

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. Data warehouse architecture. Analytics maturity model.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

FEBRUARY 10, 2021

New approaches arise to speed up the transformation of raw data into useful insights. Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. The core difference lies in the point where raw data is transformed.

Analytics 75

Analytics DevOps Development Software Review 75

Altexsoft

MARCH 14, 2023

Also, there are NoSQL databases that can be home to all sorts of data, including unstructured and semi-structured (images, PDF files, audio, JSON, etc.) For instance, video streaming platforms like TikTok may have different databases for user registration and uploaded videos. Data storage component in a modern data stack.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Kentik

MAY 23, 2017

Given the advanced capabilities provided by cloud and big data technology, there’s no longer any justification for legacy monitoring appliances that summarize away all the details and force operators to swivel between siloed tools. ISPs can gain similar advantages by becoming far more data driven.

Network 40

Network Open Source Big Data Load Balancer 40

CIO

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Pulling it all together.

Artificial Inteligence 248

Artificial Inteligence Data Artificial Intelligence Analytics 248

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? Data mart use cases. Just like display cases in a store.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

JUNE 20, 2019

In relational DBMS, the data appears as tables of rows and columns with a strict structure and clear dependencies. Due to the integrated structure and data storage system, SQL databases don’t require much engineering effort to make them well-protected. Simple data access, storage, input, and retrieval.

Systems Review 85

Systems Review System Software Review Open Source 85

Altexsoft

DECEMBER 14, 2022

Containers require fewer host resources such as processing power, RAM, and storage space than virtual machines. Docker also offers video explainers for those who prefer to listen and watch instead of reading. The Good and the Bad of the SAP Business Intelligence Platform. Common Docker use cases.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

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

Xicom

OCTOBER 15, 2021

Often, service providers have their mobile apps built to address specific services; there are low chances that individuals wouldn’t download them as no mobile app has mammoth storage to impel them all. This is where a super app like GoJek comes into the big picture. VoIP Feature. Regular Currency Update.

Technical Review 52

Technical Review Technology Mobile VOIP 52

O'Reilly Media - Ideas

SEPTEMBER 19, 2023

It progressed from “raw compute and storage” to “reimplementing key services in push-button fashion” to “becoming the backbone of AI work”—all under the umbrella of “renting time and storage on someone else’s computers.” ” (It will be easier to fit in the overhead storage.)

Data 108

Data Artificial Inteligence Machine Learning Hardware 108

Altexsoft

NOVEMBER 9, 2021

Check a brief video explaining how demand forecasting works. Digitization has already greatly improved this business aspect, facilitating contract creation and editing, introducing e-signatures, increasing security, tracking expiration dates, and enabling convenient search and storage functionalities. Process data.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

OCTOBER 17, 2019

Low-quality data can also impede and slow down the integration of business intelligence and ML-powered predictive analytics. US company executives that participated in the Data trust pulse survey by PricewaterhouseCoopers noted that unreliable data is one of the obstacles to monetizing data.

Tools 16

Tools Data Technical Advisors Technical Review 16

Altexsoft

OCTOBER 3, 2019

Mark Huselid and Dana Minbaeva in Big Data and HRM call these measures the understanding of the workforce quality. The platform provides “ business intelligence, planning, and predictive capabilities within one product” and uses AI and ML. A provider maintains the platform and handles the storage of your data.

Analytics 16

Analytics Company Off-The-Shelf How To 16