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 availability. The ELT workflow.

Tools 52

Tools Software Review Systems Review Testing 52

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

DECEMBER 9, 2020

In the era of global digital transformation , the role of data analysis in decision-making increases greatly. Hard to believe, but even now there are businesses that do not use technology and manage their operations with pen-and-paper. Often, no technologies are involved in data analysis. Ground level of analytics.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

OCTOBER 2, 2020

Data integration and interoperability: consolidating data into a single view. Specialist responsible for the area: data architect, data engineer, ETL developer. Scattered across different storages in various formats, data values don’t talk to each other. There are two main approaches to data integration.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Altexsoft

JUNE 14, 2021

InsureApp is another company that contextualizes behavior and translates it into personalized insurance by combining and interpreting data from smartphone sensors and IoT devices. You can start investing in data infrastructure and analytical pipelines to automate data collection and analysis mechanisms.

Insurance 88

Insurance Machine Learning Artificial Inteligence Policies 88

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. Data analysis.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 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. While all three are about data acquisition, they have distinct differences.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

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. Typically used for data analysis and reporting, data warehouses rely on ETL mechanisms to extract , transform , and load data into a destination.

Architecture 52

Architecture Data Storage Machine Learning 52

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

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. Data analysis.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

JULY 19, 2022

Decentralized data ownership by domain. Zhamak Dehghani divides the data into the “two planes”: The operational plane presents the data from the source systems where it originates — for example, front-desk apps, IoT systems , point of sales systems , etc. And it’s their job to guarantee data quality.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

JANUARY 22, 2020

With the uprise of internet-of-things (IoT) devices, overall data volume increase, and engineering advancements in this field led to new ways of collecting, processing, and analysing data. As a result, it became possible to provide real-time analytics by processing streamed data. Batch processing.

Analytics 20

Analytics Data IoT Analysis 20

Altexsoft

SEPTEMBER 10, 2021

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. These systems can be hosted on-premises, in the cloud, and on IoT devices, etc.

Tools 52

Tools Data Software Review Technical Review 52

TIBCO - Connected Intelligence

DECEMBER 20, 2021

TIBCO DQ will become the new data quality product family, through an evolution of our current data quality offerings, significantly enhancing current capabilities available throughout the TIBCO data fabric with built-in AI and ML to automate quality, detection, monitoring, and anomaly resolution.

Innovation 26

Innovation Analytics Virtualization Cloud 26

Altexsoft

APRIL 13, 2023

Due to extensive usage of connected IoT devices and advanced processing technologies, SCCTs not only gather data and build operational reports but also create predictions, define the impact of various macro- and microeconomic factors on the supply chain, and run “what-if” scenarios to find the best course of action.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

MARCH 14, 2023

This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. Data democratization.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. Banks, car manufacturers, marketplaces, and other businesses are building their processes around Kafka to. process data in real time and run streaming analytics. Red Hat , acquired by IBM.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

OCTOBER 24, 2019

To prepare data for its further analysis, it has to be placed into a single storage. This way, different business units can query it and analyze information from multiple angles. With a data warehouse, an enterprise is able to manage huge data sets, without administering multiple databases. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

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. The term “ML” is No.

Conference 8

Conference Data Data Engineering Artificial Inteligence 8