Mobilunity

MAY 3, 2024

When it comes to financial technology, data engineers are the most important architects. As fintech continues to change the way standard financial services are done, the data engineer’s job becomes more and more important in shaping the future of the industry. Knowledge of Scala or R can also be advantageous.

Data Engineering 52

Data Engineering Fintech Engineering Data 52

Apiumhub

NOVEMBER 20, 2023

As the organizers of the Global Software Architecture Summit , we recognized the significance of introducing this subject in the forthcoming edition. Annie Freeman – Platform Engineer at Xero, Enrolled Barrister and Solicitor at Xero Annie serves as a platform software engineer at Xero, situated in New Zealand.

Fractional CTO 82

Fractional CTO Software CTO Sustainability 82

CIO

APRIL 4, 2023

Already, we see its impact across industries and applications. In order to move AI forward, we need to first build and fortify the foundational layer: data architecture. This architecture is important because, to reap the full benefits of AI, it must be built to scale across an enterprise versus individual AI applications.

Architecture 130

Architecture Data Analysis Construction 130

Altexsoft

OCTOBER 30, 2021

Engineers are not only the ones bearing helmets and operating on construction sites. Explaining the difference, especially when they both work with something intangible such as data , is difficult. We will try to answer your questions and explain how two critical data jobs are different and where they overlap.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Netflix Tech

MARCH 4, 2024

The rule-based classifier relies on data platform engineers to manually add rules for recognizing errors based on the known context; otherwise, the errors will be unclassified. From this identifier, the service constructs the feature vector to be used in inference calls. spark.executor.memory, spark.executor.cores).

Machine Learning 84

Machine Learning Artificial Inteligence Data Systems Review 84

Altexsoft

JUNE 25, 2019

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next.

Big Data 63

Big Data Data Storage Microservices 63

Altexsoft

JANUARY 14, 2021

We’ll review all the important aspects of their architecture, deployment, and performance so you can make an informed decision. Before jumping into the comparison of available products right away, it will be a good idea to get acquainted with the data warehousing basics first. What is a data warehouse?

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. Airflow architecture.

Weak Development Team 52

Weak Development Team Technical Review Software Review Systems Review 52

Apiumhub

APRIL 5, 2022

The Data Innovation Summit 2022 is constructed so it equally addresses all the elements of data-driven and AI-ready business: data, people, processes and technology. Focus on Data-Ops, ML Ops, Auto ML, Cloud ML, Fast Data, data pipeline, data lineage, modeling, data flow monitoring, feature extraction and much more.

Innovation 52

Innovation Data Artificial Inteligence Case Study 52

Altexsoft

AUGUST 4, 2021

Depending on a goal, it may take weeks or months to set up a data lake. Moreover, not all organizations use data lakes. Data mart vs data warehouse vs data lake architectures. Data marts shouldn’t be confused with OLAP cubes either. Data mart designing. Data mart constructing.

Data 64

Data Analytics Construction Cloud 64

O'Reilly Media - Ideas

JULY 15, 2020

Decomposing monolithic applications into microservices is also a major challenge, but complexity may be the biggest challenge of all. Software engineers comprise the survey audience’s single largest cluster, over one quarter (27%) of respondents (Figure 1). If you combine the different architectural roles—i.e.,

Microservices 141

Microservices Weak Development Team Survey Architecture 141

Netflix Tech

OCTOBER 18, 2022

Meson was based on a single leader architecture with high availability. It serves thousands of users, including data scientists, data engineers, machine learning engineers, software engineers, content producers, and business analysts, for various use cases. Figure 1 shows the high-level architecture.

Data 81

Data UI/UX Systems Review Software Review 81

Altexsoft

SEPTEMBER 28, 2021

It provides a standard and mature way of building web applications, saving development time. They allow developers to mix languages, borrow functionality their main technology lacks and run the ‘foreign’ code inside their applications. Scientific programming and data science. Portability and extensibility to other languages.

Weak Development Team 94

Weak Development Team Programming Software Review Systems Review 94

Cloudera

OCTOBER 7, 2021

In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Data integration and Democratization fabric. Introduction to the Data Mesh Architecture and its Required Capabilities.

Architecture 127

Architecture Data Security Technical Review 127

Altexsoft

SEPTEMBER 20, 2019

Desktop applications themselves are really heavyweight ( Tableau 1.6 Popular data visualization tools. In most cases, tooling for data visualization is a desktop application, that represents a command dashboard. The integration with data sources is done via API, so datasets will be uploaded automatically.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. As a result, alternative data integration technologies (e.g., CRM platforms). CRM platforms). public, private, hybrid cloud)?

Scalability 95

Scalability Technical Review Data Analytics 95

O'Reilly Media - Ideas

JANUARY 25, 2021

While we can’t compare in-person conference data with virtual event data, we can make a few observations. The most successful superstream series focused on software architecture and infrastructure and operations. The in-person O’Reilly Software Architecture Conference was small but growing. Programming Languages.

Programming 145

Programming Cloud Artificial Inteligence Machine Learning 145