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

O'Reilly Media - Ideas

JANUARY 29, 2019

In 2017, we published “ How Companies Are Putting AI to Work Through Deep Learning ,” a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning. Data scientists and data engineers are in demand.

Sustainability 85

Sustainability Company Machine Learning Artificial Inteligence 85

Altexsoft

JANUARY 14, 2021

It is usually created and used primarily for data reporting and analysis purposes. Thanks to the capability of data warehouses to get all data in one place, they serve as a valuable business intelligence (BI) tool, helping companies gain business insights and map out future strategies. Data loading.

Backup 115

Backup Azure Software Review Systems Review 115

ParkMyCloud

MAY 29, 2019

Anecdotally, many organizations we talk to share that getting and keeping talent on board is a challenge as they seek to evolve their use of cloud services. The cloud jobs that are available in the market today are a result of employer demand to drive innovation and are paramount for new business applications and services to the end-user.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

Apiumhub

AUGUST 12, 2021

His current technical expertise focuses on integration platform implementations, Azure DevOps, and Cloud Solution Architectures. Steef-Jan is a board member of the Dutch Azure User Group, a regular speaker at conferences and user groups, and he writes for InfoQ, and Serverless Notes. Twitter: [link] Linkedin: [link]. Twitter: [link].

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Altexsoft

OCTOBER 2, 2020

Data is a valuable source that needs management. If your business generates tons of data and you’re looking for ways to organize it for storage and further use, you’re at the right place. Read the article to learn what components data management consists of and how to implement a data management strategy in your business.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Altexsoft

AUGUST 19, 2022

In our blog, we’ve been talking a lot about the importance of business intelligence (BI), data analytics, and data-driven culture for any company. Multiple studies continuously demonstrate the superiority of analytics-based organizations (e.g., Power BI data sources. What is Power used for?

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Weak Development Team Data Azure Analytics 52

Cloudera

SEPTEMBER 9, 2018

It’s no surprise that there is a strong correlation between successful businesses and those that exceed in extracting value from data. Cloudera Altus Data Warehouse is designed with agile data teams in mind. This makes it easy to meet the ever-changing needs of your data teams. Try Altus Data Warehouse today.

Data 40

Data Data Engineering Analytics Cloud 40

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. ELT vs ETL. Order of process phases.

Tools 52

Tools Software Review Systems Review Testing 52

O'Reilly Media - Ideas

FEBRUARY 11, 2019

In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Temporal data and time-series analytics. Forecasting Financial Time Series with Deep Learning on Azure”. Foundational data technologies. Deep Learning.

Big Data 85

Big Data Technology Tools Cloud 85

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. By the type of deployment, data warehouses can be categorized into. Source: Snowflake.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

JULY 19, 2022

That’s exactly what every data-driven organization has been trying to find for years,” someone would come up with a new, better solution. Data mesh is another hot trend in the data industry claiming to be able to solve many issues of its predecessors. How a data mesh may look like.

Architecture 52

Architecture Data Analytics Data Engineering 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 lakes are mostly used by data scientists for machine learning projects.

Data 76

Data Storage Analytics Data Center 76

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 Machine Learning 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. Robust community.

Big Data 59

Big Data Data Google Cloud Storage 59

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Business intelligence (BI) and analytics in the cloud is an area that has gained the attention of many organizations looking to provide a better user experience for their data analysts and engineers. Clouds (source: Pexels ).

Cloud 43

Cloud Data Storage Analytics 43

CIO

AUGUST 18, 2022

To find out, he queried Walgreens’ data lakehouse, implemented with Databricks technology on Microsoft Azure. “We Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. Enter the data lakehouse. Enter the data lakehouse.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

Altexsoft

OCTOBER 8, 2021

To drive deeper business insights and greater revenues, organizations — whether they are big or small — need quality data. But more often than not data is scattered across a myriad of disparate platforms, databases, and file systems. Data with errors can be then corrected, recycled, or accepted. Pricing model.

Tools 52

Tools Data Software Review Open Source 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. 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.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Perficient

APRIL 1, 2024

Traditionally, organizations used to provision multiple services of Azure Services, like Azure Storage, Azure Databricks, etc. Case Study A private equity organization wants to have a close eye on equity stocks it has invested in for their clients. Fabric brings all the required services into a single platform.

Data 64

Data Azure Case Study Data Engineering 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.

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