What is AWS Lambda function and how it works? Lambda function is a popular service on AWS cloud platform. Because it is more cost efficient and easy to deploy your serverless application to production. This video also talks about Serverless Architecture /. Function as a Service, which is the actual design pattern behind the AWS Lambda
The following video discuss about the Statefulset in Kubernetes. And diffreences between Stateful Applications and Stateless Applications. It is important to know why we need Statefulset component in Kubernetes when we have Deployment component.
Check the following video to know more about Statefulset in Kubernetes
CQRS - Event Sourcing Architecture Patterns enables us build efficient Event Driven Systems.
If any organization wants to build Event Based systems, they should adhere to the principles defined by CQRS, Event Sourcing & Domain Driven Design principles. The following videos makes you to better understand how these design patterns and can help you in dealing with performance, validation, parallel updates in the data and events. And also explained how pessimistic locking and optimistic locking works with respect to Event Driven Architecture. By following the principles of these architectures you can build powerful microservices which can be easily scalable and better in performance and you will not be ended with building monolithic application.
Checkout the video on deep dive on these 2 patterns complement each other to build efficient Event Driven Systems. If you like the video, subscribe to channel and share with your friends and colleagues.
Generally to create a docker image for Spring application or any other app, we create a dockerfile and we run docker build command. But from Spring 2.3 version onwards we no need to write any dockerfile for our application. Since cloud-native buildpacks support is natively added in Spring 2.3 version onwards. This will reduce a lot overhead for developer to put more focus on business logic implementation.
In the following video I explained how we can create a docker image using Spring 2.3 version. Checkout the video below. If you like the video, subscribe to it and share with friends and colleagues.
We can utilize ready made templates provided by Spring Boot framework to connect with Kafka to write messages and read from with Kafka Producer and Kafka Consumer.
In the following you will see complete demo of building Spring Boot with Kafka Producer and Kafka Consumer classes to write and read data to and from Kafka topic. And also we will learn how to dockerize Kafka and run it with Docker Compose.
Just check the following video. If you like the video like it and subscribe to channel: https://www.youtube.com/c/Techtter
#springboot #kafkaconsumer #kafka #techtter #kafkaproducer #docker #dockercompose
Spring Boot and Cassandra NoSQL DB can be dockerized can be run as microservices using docker compose. In the following video you will learn how to integrate or connect Spring Boot Application with Cassandra data base. And you will learn how to build REST API using Spring Boot framework. Finally we test the Spring Boot application whether it can connect with Cassandra or not by testing with Postman tool.
To get to know complete details how to run both Spring Boot and Cassandra using Docker Compose, check the following video.
If you like the video like it and subscribe to channel: https://www.Youtube.com/c/Techtter
#springboot #cassandra #dockercompose #docker #crud #api #restapi
Do you know we can Dockerize Spring Boot Application without Dockerfile? Yes we can do that from Spring Boot 2.3 version. Spring Boot 2.3 added with natively support of cloud-native buildpacks. So that we no need to have Dockerfile in our project base directory. To know complete details how we can dockerize Spring Boot, just watch the following video:
If you like the video, like it and subscribe to channel @ https://www.YouTube.com/c/Techtter
#techtter #springboot #docker #buildpacks #cloudnativearchitecture
Exploratory Data Analysis or Data Exploration is another process that is critical in machine learning pipeline. Since it makes you understand what kind of data you have and what is data format. And also by exploring the data you will know how that data is useful to take business decisions. It may contain very raw data and it may null values. By exploring the you can get complete knowledge of your data and you can know how pre process it by eliminating null values and duplicates in the data.
To get to know more about how you can explore the data, just check the following video. If you like the video like it and subscribe to channel @ https://www.youtube.com/c/Techtter
Data Collection and Data Conenctivity are important processes in ML Pipeline. You should know how to connect and collect the data from the data sources. The data source located in on-premises or remote locations. Irrespective of data locality you should be able to connect the data source and collect the data and should store the data in centalized location.
To know more about Data Collection and Connectivity, watch the following video and subscribe to channel if you like the video.: https://www.youtube.com/c/Techtter
Which tool?? Glue or Lambda function ?? Kinesis or Kafka??, Redshift or Redshift Spectrum?? Quicksight for visualization?? Which AWS service is better?
how to design and implement. There are number of tools or services on AWS, but what is the right tool.
I have demonstrated my experience while building data pipeline on AWS in the following video. Please watch the video and let me know your thoughts in comments section.
If you like the video, go to youtube and subscribe to my channel: https://www.YouTube.com/c/Techtter
Watch the video to know more about Helm and Helm Charts and why Tiller is removed from Helm version 3.
#kubernetes #helm #helmcharts #devops #devopstools #tiller
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