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GoogleCloud Essentials (NEW). This course is designed for those who want to learn about GoogleCloud: what cloud computing is, the overall advantages GoogleCloud offers, and a detailed explanation of all major services – what they are, their use cases, and how to use them. BigData Essentials.
GoogleCloud Essentials (NEW). This course is designed for those who want to learn about GoogleCloud: what cloud computing is, the overall advantages GoogleCloud offers, and a detailed explanation of all major services – what they are, their use cases, and how to use them. BigData Essentials.
GoogleCloud Essentials (NEW). This course is designed for those who want to learn about GoogleCloud: what cloud computing is, the overall advantages GoogleCloud offers, and a detailed explanation of all major services – what they are, their use cases, and how to use them. BigData Essentials.
Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics. AI-driven Future State Cloud Operations , June 7. Understanding Data Science Algorithms in R: Scaling, Normalization and Clustering , August 14. Real-time Data Foundations: Spark , August 15. Programming.
GoogleCloud Essentials (NEW). This course is designed for those who want to learn about GoogleCloud: what cloud computing is, the overall advantages GoogleCloud offers, and a detailed explanation of all major services – what they are, their use cases, and how to use them. BigData Essentials.
We will also cover the different data types that are allowed in MySQL, and discuss user access and privileges. GoogleCloud Functions is a serverless, event-driven, managed platform for building and connecting cloud services. GoogleCloud Essentials (NEW). BigData Essentials.
Introduction to Migrating Databases and Virtual Machines to GoogleCloud Platform — This course covers the various issues of migrating databases and virtual machines to GoogleCloud Platform. BigData Essentials – BigData Essentials is a comprehensive introduction to the world of bigdata.
To use Docker Compose to deploy Microservices to Docker. Use Secrets to protect sensitive data like passwords. BigData Essentials – BigData Essentials is a comprehensive introduction addressing the large question of, “What is BigData?” No prior AWS experience is required.
1] This combination of search and usage data provides a holistic view; search data shows the areas where subscribers are exploring, and usage identifies topics where they’re actively engaged. There are four aspects of the Next Architecture, each of which shows up in the platform’s search and usage data. Decomposition.
Artificial Intelligence for BigData , February 26-27. Data science and data tools. Apache Hadoop, Spark, and BigData 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.
GoogleCloud Concepts. This course is for the true GoogleCloud Platform beginner. What is the cloud or GoogleCloud? Why do we use GoogleCloud? We’ll provide a simple introduction to the concepts of Cloud Computing, GoogleCloud Platform, and it’s core services.
Data science and data tools. Apache Hadoop, Spark, and BigData Foundations , January 15. Python Data Handling - A Deeper Dive , January 22. Practical Data Science with Python , January 22-23. Microservices Architecture and Design , January 16-17. How to Give Great Presentations , February 7.
GoogleCloud Essentials (NEW). This course is designed for those who want to learn about GoogleCloud: what cloud computing is, the overall advantages GoogleCloud offers, and a detailed explanation of all major services – what they are, their use cases, and how to use them. BigData Essentials.
Artificial Intelligence for BigData , April 15-16. Developing Applications on GoogleCloud Platform , April 29-30. Introduction to GoogleCloud Platform , April 3-4. Cloud Computing on the Edge , April 9. Microservice Fundamentals , April 15. Microservice Collaboration , April 16.
Get hands-on training in machine learning, microservices, blockchain, Python, Java, and many other topics. Data science and data tools. Business Data Analytics Using Python , February 27. Designing and Implementing BigData Solutions with Azure , March 11-12. Cleaning Data at Scale , March 19.
GoogleCloud Essentials (NEW). This course is designed for those who want to learn about GoogleCloud: what cloud computing is, the overall advantages GoogleCloud offers, and a detailed explanation of all major services – what they are, their use cases, and how to use them. BigData Essentials.
Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics. AI-driven Future State Cloud Operations , June 7. Understanding Data Science Algorithms in R: Scaling, Normalization and Clustering , August 14. Real-time Data Foundations: Spark , August 15. Programming.
Most IoT-based applications (both B2C and B2B) are typically built in the cloud as microservices and have similar characteristics. It is helpful to think about the data created by the devices and the applications in three stages: Stage one is the initial creation, which takes place on the device, and is then sent over the network.
Containers have become the preferred way to run microservices — independent, portable software components, each responsible for a specific business task (say, adding new items to a shopping cart). Modern apps include dozens to hundreds of individual modules running across multiple machines— for example, eBay uses nearly 1,000 microservices.
Introduction to GoogleCloud Platform , June 3-4. Data science and data tools. Apache Hadoop, Spark, and BigData Foundations , April 22. Data Structures in Java , May 1. Cleaning Data at Scale , May 13. BigData Modeling , May 13-14. Fundamentals of Data Architecture , May 20-21.
Change is inevitable, and as programming languages continue to lean in to optimization for new trends in the cloud, microservices, bigdata, and machine learning, each language and its ecosystem will continue to adapt in its own unique way. KotlinConf sold out three years in a row with more than 1,700 attendees in 2019.
With the increasing adoption of next-gen technologies 94% of enterprises adopting cloud services, 97% using or planning to embrace microservices, and 97% relying on APIs for digital transformation businesses demand resilient and flexible backend solutions to stay competitive.
Different teams can develop, maintain, and change integration to devices and machines without being dependent on other sources or the sink systems that process and analyze the data. Microservices, Apache Kafka, and Domain-Driven Design (DDD) covers this in more detail. Kai Waehner works as technology evangelist at Confluent.
Introduction to Migrating Databases and Virtual Machines to GoogleCloud Platform — This course covers the various issues of migrating databases and virtual machines to GoogleCloud Platform. BigData Essentials – BigData Essentials is a comprehensive introduction to the world of bigdata.
The code can be written in either a completely serverless code that uses no provisioned servers, or in combination with a traditional approach like microservices. For example, a web app could be written using both microservices and serverless code. The adoption of serverless architecture is growing rapidly.
Managed model server in the public cloud like GoogleCloud 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. Kai Waehner works as technology evangelist at Confluent.
Spotlight on Cloud: The Hidden Costs of Kubernetes with Bridget Lane , June 6. Spotlight on Data: Caching BigData for Machine Learning at Uber with Zhenxiao Luo , June 17. Data Analysis Paradigms in the Tidyverse , May 30. Data Visualization with Matplotlib and Seaborn , June 4. Network DevOps , June 6.
Capside delivers Cloud training in all knowledge areas and technologies related to Cloud. Architecture, Agility and DevOps in Amazon AWS, Microsoft Azure and GoogleCloud. They also offer training to leverage Cloud and DevOps technologies, to create a Continous Delivery Pipeline. Microservices with AWS Lambdas.
CrossKube is a packaged solution that gives organizations the ability to deploy everything that is needed for a highly scalable, cloud-based application, including application best-practice templates for Kubernetes, microservices, containerization, application discovery, database connectivity, front-end application structure and automated testing.
In a small company, infrastructure engineers will likely be masters of all trades while in enterprises, this position may focus on a specific problem like cloud migration, continuous app deployments, or designing bigdata structures. Architecting with Google Compute Engine Specialization. GoogleCloud Fundamentals.
They are suited for scale-out workloads such as web servers, containerized microservices , caching fleets, distributed data stores, and development environments. The r i nstance family is memory-optimized, which you might use for in-memory databases, real-time processing of unstructured bigdata, or Hadoop/Spark clusters.
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 BigData. process data in real time and run streaming analytics.
Microservices Architecture : Java frameworks like Spring Boot and Eclipse MicroProfile simplify the creation and deployment of microservices, enabling flexible and scalable applications. Cloud Computing and Serverless Architecture : Java’s platform independence and scalability make it ideal for cloud computing environments.
Clustered computing for real-time BigData analytics. But the current epoch of distributed computing is often traced to December of 2004, when Google researchers Jeffrey Dean and Sanjay Ghemawat presented a paper unveiling MapReduce. While the use of data cubes boosts Hadoop’s utility, it still involves compromise.
DevOps has become an integral part of the cloud – in GoogleCloud , AWS , and Azure. Who should take this course: We suggest you take our BigData Essentials and Linux Essentials courses before taking this course. Use Docker Compose to deploy Microservices to Docker. Difficulty Level: Intermediate.
With so many options available, finding the right machine type for your workload can be confusing – which is why we’ve created this overview of Azure VM types (as we’ve done with EC2 instance types , and GoogleCloud machine types ). The Ev4 and Esv4-series are ideal for various memory-intensive enterprise applications. Lsv2-series.
It provides a comprehensive set of features for building enterprise applications and is widely used for creating web applications, RESTful web services, and microservices. Another commonly used technology in software development for financial services, banks, and fintech is Microservices.
“AWS,” “Azure,” and “cloud” were also among the most common words (all in the top 1%), again showing that our audience is highly interested in the major cloud platforms. Both “GCP” and “GoogleCloud” were in the top 3% of their respective lists. Units viewed and year-over-year growth for software development topics.
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. It’s possible that AI (along with machine learning, data, bigdata, and all their fellow travelers) is descending into the trough of the hype cycle. The result?
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