O'Reilly Media - Ideas

DECEMBER 5, 2018

Data science and data tools. Apache Hadoop, Spark, and Big Data Foundations , January 15. Python Data Handling - A Deeper Dive , January 22. Practical Data Science with Python , January 22-23. Developing Incremental Architecture , February 11-12. Fundamentals of IoT with JavaScript , February 14-15.

Artificial Inteligence 103

Artificial Inteligence Course Training Blockchain 103

Mobilunity

MAY 20, 2021

Cloud engineers deal with all types of tasks related to SaaS, PaaS, and IaaS services. For example, they can create custom applications, help with the migration of existing data, and manage and support cloud architectures. A cloud developer is an experienced IT professional that works with cloud services.

Engineering 52

Engineering Cloud Google Cloud Azure 52

O'Reilly Media - Ideas

JANUARY 2, 2019

Artificial Intelligence for Big Data , February 26-27. Data science and data tools. Apache Hadoop, Spark, and Big Data Foundations , January 15. Python Data Handling - A Deeper Dive , January 22. Practical Data Science with Python , January 22-23. SQL Fundamentals for Data , February 19-20.

Course 58

Course Artificial Inteligence Training Blockchain 58

ParkMyCloud

JANUARY 17, 2020

Display a basic understanding of core AWS services, uses, and basic AWS architecture best practices. Demonstrate that they are capable of developing, deploying, and debugging cloud-based applications using AWS. Design and maintain network architecture for all AWS services. AWS Certified Big Data – Speciality.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

O'Reilly Media - Ideas

MAY 1, 2019

Spotlight on Cloud: The Hidden Costs of Kubernetes with Bridget Lane , June 6. Spotlight on Data: Caching Big Data for Machine Learning at Uber with Zhenxiao Luo , June 17. Apache Hadoop, Spark and Big Data Foundations , June 5. Real-time Data Foundations: Kafka , June 11. Practical Docker , June 11.

Course 45

Course Training Artificial Inteligence Machine Learning 45

Confluent

FEBRUARY 6, 2019

Managed model server in the public cloud like Google Cloud Machine Learning Engine: The cloud provider takes over the burden of availability and reliability. The data scientist “just” deploys its trained model, and production engineers can access it. So how can the Kafka ecosystem help here?

Machine Learning 106

Machine Learning Artificial Inteligence Scalability Data Engineering 106

Altexsoft

MAY 20, 2020

For the most part, they belong to the Internet of Things (IoT), or gadgets capable of communicating and sharing data without human interaction. The number of active IoT connections is expected to double by 2025, jumping from the current 9.9 Source: IoT Analytics. IoT architecture layers. billion to 21.5

IoT 141

IoT Azure AWS Transportation 141

Confluent

MAY 22, 2019

Through the Internet of Things (IoT), it is also connecting humans to the machines all around us and directly connecting machines to other machines. In light of this, we’ll share an emerging machine-to-machine (M2M) architecture pattern in which MQTT, Apache Kafka ® , and Scylla all work together to provide an end-to-end IoT solution.

IoT 96

IoT Google Cloud Scalability Microservices 96

Altexsoft

JANUARY 22, 2020

Similar to a real world stream of water, continuous transition of data received the name streaming , and now it exists in different forms. Media streaming is one of them, but it’s only a visible part of an iceberg where data streaming is used. As a result, it became possible to provide real-time analytics by processing streamed data.

Analytics 20

Analytics Data IoT Analysis 20

Trigent

MARCH 29, 2023

Rather it requires a conscious effort to design and develop an architecture that delivers an integrated experience regardless of the distribution partners. This allows InsurTechs to take advantage of additional data available with the distribution partner to personalize the coverage and premiums.

Insurance 95

Insurance Strategy Serverless Big Data 95

O'Reilly Media - Ideas

FEBRUARY 6, 2019

Machine Learning for IoT , March 20. Data science and data tools. Business Data Analytics Using Python , February 27. Designing and Implementing Big Data Solutions with Azure , March 11-12. Cleaning Data at Scale , March 19. Practical Data Cleaning with Python , March 20-21.

Artificial Inteligence 11

Artificial Inteligence Course Training Artificial Intelligence 11

O'Reilly Media - Ideas

MARCH 6, 2019

Artificial Intelligence for Big Data , April 15-16. Managing Enterprise Data Strategies with Hadoop, Spark, and Kafka , April 15. Real-Time Data Foundations: Flink , April 17. Data Pipelining with Luigi and Spark , April 17. Real-Time Data Foundations: Time Series Architectures , April 18.

Course 10

Course Artificial Inteligence Training Microservices 10

Saviynt

AUGUST 20, 2019

Problematically, complex, integrated architectures often create a poor end-user experience. If you take a look at online resources, for example, AWS, Google Cloud, and Azure have different definitions for users, roles, groups, and attributes. Why is it difficult to create an IAM Policy? Intelligent Role-Mining with Context.

Policies 10

Policies Analytics Security Software Review 10

Confluent

OCTOBER 10, 2019

The Internet of Things (IoT) is getting more and more traction as valuable use cases come to light. A key challenge, however, is integrating devices and machines to process the data in real time and at scale. Confluent MQTT Proxy , which ingests data from IoT devices without needing a MQTT broker.

IoT 20

IoT Internet Weak Development Team Scalability 20

Altexsoft

OCTOBER 21, 2022

Apache Kafka is an open-source, distributed streaming platform for messaging, storing, processing, and integrating large data volumes in real time. It offers high throughput, low latency, and scalability that meets the requirements of Big Data. Kafka architecture. Kafka cluster architecture. Scalability.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

FEBRUARY 24, 2023

The shift to non-application jobs driven by the ability to support various types of workloads turns Kubernetes into a universal platform for almost everything and a de-facto operating system for cloud-native software. Learn more about Docker architecture, advantages, and alternatives from our article The Good and the Bad of Docker Containers.

Weak Development Team 83

Weak Development Team Load Balancer Technical Review Microservices 83

Cloudera

APRIL 2, 2019

Cloudera and Intel have a long history of innovation, driving big data analytics and machine learning into the enterprise with unparalleled performance and security. A key characteristic of an enterprise data cloud is its ability to run multiple workloads on shared data without encountering “noisy neighbor” problems.

Performance 47

Performance Artificial Inteligence IoT Scalability 47

Altexsoft

JUNE 25, 2019

Notably, data warehouse doesn’t support as many concurrent users as a database, while being designed for a small group of analysts and business users. Data warehouse architecture. To structure a data warehouse, four basic components are combined. Data warehouse storage. Data lake architecture.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Altexsoft

MAY 12, 2023

Whether it’s human- or machine-generated, unstructured data is challenging to handle, as it typically requires advanced techniques and tools to extract meaningful insights. Sensor data. As the data world evolves, more formats may emerge, and existing formats may be adapted to accommodate new unstructured data types.

Examples 59

Examples Tools Data Artificial Inteligence 59