O'Reilly Media - Ideas

MARCH 6, 2019

Artificial Intelligence for Big Data , April 15-16. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , March 13. Data Modelling with Qlik Sense , March 19-20. Foundational Data Science with R , March 26-27. Microservice Fundamentals , April 15.

Course 15

Course Artificial Inteligence Training Machine Learning 15

O'Reilly Media - Ideas

JUNE 5, 2019

Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics. Understanding Data Science Algorithms in R: Scaling, Normalization and Clustering , August 14. Real-time Data Foundations: Spark , August 15. Visualization and Presentation of Data , August 15. Programming.

Course 67

Course Training Artificial Inteligence Software Review 67

Cloudera

OCTOBER 14, 2020

A single, unified infrastructure for both majority of batch workloads and microservices. Natively support Big Data workloads. YuniKorn is designed for Big Data app workloads, and it natively supports to run Spark/Flink/Tensorflow, etc efficiently in K8s. Fine-grained access controls on shared clusters.

Policies 118

Policies Resources Systems Review Technical Review 118

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

Abhishek Tiwari

DECEMBER 27, 2017

In 2018, we will see new data integration patterns those rely either on a shared high-performance distributed storage interface ( Alluxio ) or a common data format ( Apache Arrow ) sitting between compute and storage. For instance, Alluxio, originally known as Tachyon, can potentially use Arrow as its in-memory data structure.

Trends 49

Trends Artificial Inteligence Data Big Data 49

Hu's Place - HitachiVantara

JANUARY 3, 2019

In order to utilize the wealth of data that they already have, companies will be looking for solutions that will give comprehensive access to data from many sources. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data.

Trends 86

Trends Artificial Inteligence Machine Learning Data Center 86

O'Reilly Media - Ideas

MAY 1, 2019

Spotlight on Data: Caching Big Data for Machine Learning at Uber with Zhenxiao Luo , June 17. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. Data Analysis Paradigms in the Tidyverse , May 30.

Course 60

Course Training Artificial Inteligence Machine Learning 60

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

Altexsoft

OCTOBER 21, 2022

It offers high throughput, low latency, and scalability that meets the requirements of Big Data. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. process data in real time and run streaming analytics. Cloudera , focusing on Big Data analytics.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Open Source 93

Kentik

APRIL 11, 2016

Clustered computing for real-time Big Data analytics. It has since gone on to become a key technology for running many web-scale services and products, and has also landed in traditional enterprise and government IT organizations for solving big data problems in finance, demographics, intelligence, and more.

Big Data 40

Big Data Analytics Network Architecture 40

Apiumhub

AUGUST 12, 2021

This year you will have 6 unique tracks: Cloud Computing: IaaS, PaaS, SaaS DevOps: Microservices, Automation, ASRs Cybersecurity: Threats, Defenses, Tests Data Science: ML, AI, Big Data, Business Analytics Programming languages: C++, Python, Java, Javascript,Net Future & Inspire: Mobility, 5G data networks, Diversity, Blockchain, VR.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Altexsoft

MAY 27, 2020

Analysis of more than 16.000 papers on data science by MIT technologies shows the exponential growth of machine learning during the last 20 years pumped by big data and deep learning advancements. Reasonably, with the access to data, anyone with a computer can train a machine learning model today.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

CIO

DECEMBER 10, 2024

Its a common skill for cloud engineers, DevOps engineers, solutions architects, data engineers, cybersecurity analysts, software developers, network administrators, and many more IT roles. Job listings: 90,550 Year-over-year increase: 7% Total resumes: 32,773,163 3. As such, Oracle skills are perennially in-demand skill.

UI/UX 203

UI/UX Enterprise Artificial Inteligence Database Administration 203

Kentik

DECEMBER 5, 2016

In a recent blog post by Kentik Solutions Engineer Eric Graham we explained how we “dog food” our own NPM solution to troubleshoot network performance issues within our own cloud-based application. In that post, Eric shows how he found issues on a group of internal hosts that were impacting a critical microservice. How does it work?

Network 40

Network Performance Metrics Software Review 40

Altexsoft

DECEMBER 14, 2022

delivering microservice-based and cloud-native applications; standardized continuous integration and delivery ( CI/CD ) processes for applications; isolation of multiple parallel applications on a host system; faster application development; software migration; and. Common Docker use cases. Typical areas of application of Docker are.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

Existek

JANUARY 11, 2022

Along with meeting customer needs for computing and storage, they continued extending services by presenting products dealing with analytics, Big Data, and IoT. The next big step in advancing Azure was introducing the container strategy, as containers and microservices took the industry to a new level.

Azure 52

Azure AWS Cloud How To 52

O'Reilly Media - Ideas

JANUARY 25, 2022

A quick look at bigram usage (word pairs) doesn’t really distinguish between “data science,” “data engineering,” “data analysis,” and other terms; the most common word pair with “data” is “data governance,” followed by “data science.” That’s no longer true. Programming Languages.

Trends 116

Trends Technical Review Technology Artificial Inteligence 116

BPM

JANUARY 18, 2019

The microservice movement will reignite the need for orchestration. Some analyst is bound to rename BPM engines to Microservice Orchestration Engines (MOE).wait As we move into a world that is more and more dominated by technologies such as big data, IoT, and ML, more and more processes will be started by external events.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

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.

Conference 10

Conference Data Data Engineering Big Data 10

O'Reilly Media - Ideas

JANUARY 25, 2021

We’ll be working with microservices and serverless/functions-as-a-service in the cloud for a long time–and these are inherently concurrent systems. The biggest challenge facing operations teams in the coming year, and the biggest challenge facing data engineers, will be learning how to deploy AI systems effectively.

Programming 145

Programming Cloud Artificial Inteligence Machine Learning 145