Data Engineering for microservices

Microservices are vastly different from monolithic architectures. Therefore their data engineering implications also differ from one another. Microservices manage their own data, and data engineering provides the data pipelines that move data for microservices.

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DevOps Concepts

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DevOps Concepts

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This chapter will go through what DevOps is, discover its principles and see what benefits it brings. You will discover how it is applied to different kinds of software development. Lastly, you will look at the basics of project management to see different approaches and how they can power up DevOps to bring substantial advantages to organizations.

Exercise 1: What is DevOps?Exercise 2: Benefits of DevOps Exercise 3: Characteristics of DevOps Exercise 4: Use Cases for DevOps Exercise 5: Which engineer?Exercise 6: Employing the correct model Exercise 7: DevOps implementations Exercise 8: Project Management Methodologies for DevOps Exercise 9: Project management frameworks Exercise 10: Factors to decide the best project management framework Exercise 11: Choosing the best project management model

This chapter introduces the main components of IT infrastructure, change management, and software architecture. You will see how different parts come together to compose online products, how they interact with each other, and how teams collaborate on the DevOps Change Management Model. You’ll explore the main elements of DevOps in detail. At the end of this chapter, you will be empowered to tackle some of the complex software engineering problems, like architectural design, and improve online products safely and quickly.

Exercise 1: The DevOps Change Management Model Exercise 2: DevOps cycle components Exercise 3: Data Engineering in the DevOps cycle Exercise 4: Main Software Architecture Systems Exercise 5: Data in motion Exercise 6: Making architectural decisions Exercise 7: Modern IT Infrastructure Concepts Exercise 8: IT infrastructure components Exercise 9: Change management components Exercise 10: Elements of DevOps Exercise 11: CI/CD pipelines Exercise 12: DevOps concepts vs. generic infra concepts

Here, you will embark on a journey to navigate DevOps through the lens of a data engineer. You will explore what the DevOps architecture means from a Data Engineering standpoınt. Further, you will look at the main concepts related to Data Pipelines and how they are used in a microservices architecture. After learning about data pipelines, you will put your Data Scientist hat on and look at DevOps from a reporting perspective. Lastly, you will look at the tooling environment of DevOps and discuss the main tools used for DevOps.

Exercise 1: Data Engineering Implications of the DevOps Architecture Exercise 2: Microservices vs. monolithic architecture Exercise 3: Data Engineering for microservices

Exercice en cours

Exercise 4: Data Pipelines Exercise 5: Main data operations Exercise 6: Batch vs. stream processing Exercise 7: Analytics & Reporting with DevOps Exercise 8: Change management metrics Exercise 9: Reporting architecture for DevOps Exercise 10: Tools: Ecosystem Exercise 11: Architecture systems and tools Exercise 12: Tools in DevOps cycle Exercise 13: Automating data pipelines

In this final chapter, you will examine how DevOps helps ensure high data quality. Furthermore, you will look into quality and reliability in software. Last but not least, you will look at the cultural concepts enabling DevOps. Then, you will revamp everything you have learned and be fully equipped with the power of DevOps.

Exercise 1: Ensuring Data Quality Standards with DevOps Exercise 2: Data quality Exercise 3: Data quality elements Exercise 4: Observability and Reliability Exercise 5: Observability vs. testing Exercise 6: Increasing reliability Exercise 7: DevOps Cultural Concepts Exercise 8: Cultural concepts Exercise 9: Reacting to an incident Exercise 10: Tying it all together Exercise 11: Benefits of DevOps Exercise 12: DevOps cycle Exercise 13: CONGRATS!