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

APRIL 13, 2023

To store all this diverse information, you’ll have to utilize a centralized data repository such as a data warehouse or data lake. You can also consider a cloud data lakehouse as an option since it addresses the limitations of the aforementioned repository types and works with various data workloads. Data siloes.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Apiumhub

AUGUST 12, 2021

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. Also, he serves as the Program Director for Data science/Data Engineering Educational Program at Skillbox.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

OpenCredo

JULY 13, 2018

Either way, even if the engineering delivery is first rate, the resulting uptake of Project Positron, once delivered, will still be lower than expected because ready and willing users are not available. At first it looked like a fairly straightforward data engineering problem. This part was readily understood.

Architecture 40

Architecture Fractional CTO CTO Coach Engineering 40

Cloudera

DECEMBER 10, 2020

Recently, cloud-native data warehouses changed the data warehousing and business intelligence landscape. A containerized framework orchestrated by Kubernetes constantly monitors user workloads and enables the fast, agile, and automated provisioning. . Expected cost benefits, however, often do not materialize.

Data 98

Data Technical Review Storage Systems Review 98

Altexsoft

FEBRUARY 10, 2021

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. It covers the entire data analytics lifecycle, from data extraction to visualization and reporting, using Agile practices to speed up business results.

Analytics 75

Analytics DevOps Development Software Review 75

Altexsoft

JULY 19, 2022

This basic principle corresponds to that of agile software development or approaches such as DevOps, Domain-Driven Design, and Microservices: DevOps (development and operations) is a practice that aims at merging development, quality assurance, and operations (deployment and integration) into a single, continuous set of processes.

Architecture 52

Architecture Data Analytics Data Engineering 52

Abhishek Tiwari

JULY 20, 2019

When it comes to organising engineering teams, a popular view has been to organise your teams based on either Spotify's agile model (i.e. As a bare minimum, I can think of an engineering organisation of 6 Spotify like squads with each team consisting of 8-10 people including engineers (frontend/backend), BA, PO, and an agile coach.

Engineering 61

Engineering Weak Development Team Software Review Technical Review 61

Cloudera

SEPTEMBER 9, 2018

Businesses need to be agile and quick in using data to drive insights, lest they lose the opportunity they are trying to capitalize on. It’s no surprise that there is a strong correlation between successful businesses and those that exceed in extracting value from data. Try Altus Data Warehouse today.

Data 40

Data Data Engineering Analytics Cloud 40

Cloudera

JANUARY 3, 2018

In a big data world, we often see three new roles emerge and work more closely together: data engineers, data scientists and architects. The data engineering team is a strategic necessity as data itself is more agile. You can think of them as the data workhorse.

Big Data 40

Big Data Data Data Engineering Engineering 40

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

Cloudera

DECEMBER 7, 2017

Self-service access to a universal data in a single data store for all of your applications, not siloed into a fragmented service for each type of data science, business intelligence (BI), data engineering, or real-time operational analytics you want to do.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Friction associated with getting a data sandbox has also resulted in the proliferation of spreadmarts , unmanaged data marts, or other data extracts used for siloed data analysis. Published originally on O’Reilly.com.

Cloud 42

Cloud Data Storage Analytics 42

Cloudera

OCTOBER 25, 2022

The data lakehouse is gaining in popularity because it enables a single platform for all your enterprise data with the flexibility to run any analytic and machine learning (ML) use case. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Cloud 52

Cloud Data Firewall AWS 52

TIBCO - Connected Intelligence

DECEMBER 20, 2021

The Unify Portfolio Highlights Breakthroughs in Data Integration and Lays the Foundation for the Future of Data Fabric. Enhancements in the Unify portfolio address organizations’ data management challenges head-on, empowering the business to assemble an agile data fabric, unrestricted by siloed data, on any cloud platform.

Innovation 26

Innovation Analytics Virtualization Cloud 26

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

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

TIBCO - Connected Intelligence

JUNE 7, 2021

The past year has taught everyone the importance of agility. With TIBCO, you can leverage real-time data for better business outcomes and gain a competitive edge by: Democratizing streaming analytics : Continuous intelligence is a team sport. The power of real-time, streaming analytics.

Analytics 29

Analytics Sport Report Business Intelligence 29

Agile Engine

DECEMBER 27, 2023

A data analytics consultancy has a team of specialists and engineers who perform data analytics for companies that don’t have the capacity to do it in-house. Predictive analytics, recommendation engines, and AI-driven insights provide businesses with proactive decision support systems, improving accuracy and efficiency.

Big Data 52

Big Data Analytics Data Analysis 52

TIBCO - Connected Intelligence

SEPTEMBER 29, 2021

Gogos, Global AVP of Data Architecture, from customer GM Financial, sat down with TIBCO SVP and General Manager of Analytics, Mark Palmer, to share their journey partnering with TIBCO. As the finance subsidiary of General Motors, GM Finance wanted to become more agile. “We We have a much more agile process now,” Gogos added.

Data 49

Data Artificial Inteligence Energy Artificial Intelligence 49