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. Extract, transform, load or ETL process guide.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

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

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. What is ELT?

Systems Review 109

Systems Review Technical Review Software Review Big Data 109

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. Introducing data engineering and data science expertise.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Weak Development Team 64

Weak Development Team Artificial Inteligence Machine Learning Software Review 64

Altexsoft

JANUARY 26, 2022

In recent years, it’s getting more common to see organizations looking for a mysterious analytics engineer. As you may guess from the name, this role sits somewhere in the middle of a data analyst and data engineer, but it’s really neither one nor the other. What is an analytics engineer?

Analytics 59

Analytics Engineering Data Engineering Software Engineering 59

Cloudera

NOVEMBER 2, 2020

These are end-to-end, high volume applications that are used for general purpose data processing, Business Intelligence, operational reporting, dashboarding, and ad hoc exploration. But an important caveat is that ingest speed, semantic richness for developers, data freshness, and query latency are paramount.

Data 97

Data Analytics Storage Big Data 97

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 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker. Kafka advantages.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Open Source 93

Invid Group

AUGUST 28, 2024

All these platforms overcome the challenges behind processing complex volumes of unstructured data, like PDFs, images, video, and audio. External metrics can be implemented using Business Intelligence (BI) tools and shared with the clients to measure performance.

Technical Review 52

Technical Review Artificial Inteligence Performance Azure 52

Altexsoft

OCTOBER 3, 2019

The platform provides “ business intelligence, planning, and predictive capabilities within one product” and uses AI and ML. Data engineer builds interfaces and infrastructure to enable access to data. So, data engineers make data pipelines work. Develop UI of a solution.

Analytics 16

Analytics Company Off-The-Shelf How To 16

Altexsoft

MAY 14, 2021

It comes in all sorts of forms that differ from one application to another, and most of Big Data is unstructured. Say, a simple social media post may contain some text information, videos or images, a timestamp. Veracity is the measure of how truthful, accurate, and reliable data is and what value it brings.

Big Data 78

Big Data Analytics Tools Applications 78

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. Demo Video. Solution brief. Contributors: .

Data 82

Data Analytics Cloud Technical Review 82

Apiumhub

AUGUST 12, 2021

An International speaker, books & video author, and writer for Java Magazine, IBM Developer, Oracle, and InfoQ. Evgenii Vinogradov – Director, Analytical Solutions Department @YooMoneyon Evgenii is the Head of Data Engineering and Data Science team at YooMoney, the leading payment service provider on the CIS Market.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Altexsoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Data streaming explained.

Tools 52

Tools Software Review Systems Review Testing 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? Data mart use cases. Time-limited data projects.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Data lake architecture example. Poor data quality, reliability, and integrity.

Architecture 52

Architecture Data Storage Artificial Inteligence 52

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. BTW, we have an engaging video explaining how data engineering works. Pricing page.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Openxcell

JANUARY 3, 2025

Openxcell is always ready to understand your project needs and use AI’s full potential to deliver a solution that propels your business forward. The company offers a wide range of AI Development services, such as Generative AI services, Custom LLM development , AI App Development , Data Engineering , GPT Integration , and more.

Artificial Inteligence 40

Artificial Inteligence Company Development Artificial Intelligence 40

Kentik

MAY 23, 2017

Note that the above use cases cover network performance monitoring, planning, and business intelligence. Big data insights have the power to drive efficiency, market savvy, automation, and better service experience. It’s one thing to know that something would be good for your business, and quite another to actually achieve it.

Network 40

Network Open Source Big Data Load Balancer 40

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 Artificial Inteligence 52

Mobilunity

DECEMBER 28, 2022

Neural networks are composed of interconnected processing nodes called neurons, which can learn to recognize patterns of input data. Computer vision involves using software to interpret digital images and videos so they can be processed by a computer system. Business intelligence. Statistical data analytics.

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

Mobilunity

DECEMBER 9, 2024

Google Professional Machine Learning Engineer implies developers knowledge of design, building, and deployment of ML models using Google Cloud tools. It includes subjects like data engineering, model optimization, and deployment in real-world conditions. Computer Vision engineer. NLP engineer. Data engineer.

Engineering 52

Engineering Artificial Inteligence Machine Learning Artificial Intelligence 52

Altexsoft

SEPTEMBER 10, 2021

So, why does anyone need to integrate data in the first place? Today, companies want their business decisions to be driven by data. But here’s the thing — information required for business intelligence (BI) and analytics processes often lives in a breadth of databases and applications. Middleware data integration.

Tools 52

Tools Data Software Review Technical Review 52

Altexsoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization. Data use component in a modern data stack.

Data 59

Data Technical Review Software Review Open Source 59

Openxcell

MARCH 27, 2025

Its AI/ML engineers utilize some of the latest technologies and tools to deliver solutions across industries that automate repetitive tasks, reduce operational costs, and improve workflow efficiency, leading to more growth. to help businesses streamline operations and deliver exceptional user experiences.

Artificial Inteligence 40

Artificial Inteligence Artificial Intelligence Innovation Company 40

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Weak Development Team 52

Weak Development Team Big Data Data Artificial Inteligence 52

Altexsoft

FEBRUARY 22, 2022

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. At the same time, those novel approaches require much more data and data engineering efforts than more traditional ML methods.

Software Review 52

Software Review Technical Review Software Technology 52

Strategy Driven

AUGUST 27, 2015

Big Data involves not just the structured data (customer name and details, products purchased, how much was spent and when, etc.) that every company is used to capturing, but also unstructured data (data scraped from the Internet and social media channels that may come in a wide variety of formats, from video to voice).

Big Data 33

Big Data Data Storage Journal 33

Altexsoft

NOVEMBER 9, 2021

Check a brief video explaining how demand forecasting works. Traditionally, analytics is associated with business intelligence and data visualization that are focused on studying past events and current processes. Meanwhile, we’ll describe the process of turning raw data around you into actionable insights.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

DECEMBER 14, 2022

Docker also offers video explainers for those who prefer to listen and watch instead of reading. While you may opt for some creative strategies (such as X11 video forwarding) to run a GUI app inside a container, these solutions are cumbersome at best. The Good and the Bad of the SAP Business Intelligence Platform.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

Altexsoft

JULY 29, 2022

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. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture. Versatility.

Big Data 59

Big Data Data Google Cloud Open Source 59

Cloudera

DECEMBER 10, 2020

Some data warehousing solutions such as appliances and engineered systems have attempted to overcome these problems, but with limited success. . Recently, cloud-native data warehouses changed the data warehousing and business intelligence landscape. Watch this video to get an overview of CDW. .

Data 98

Data Technical Review Storage Systems Review 98