Docker Essentials: A Developer Introduction

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Djordje Zivanovic

Course about Docker essentials, how to create images, build, run, deploy, use Docker Swarm etc.

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1/6/23, 6:29 PM IBM CO0101EN Certificate | Cognitive Class
https://courses.cognitiveclass.ai/certificates/f6301aeea080480086a1e3cbddac6633 1/2
Djordje Zivanovic
successfully completed and received a passing grade in
Docker Essentials: A
Developer Introduction
A course on cognitiveclass.ai
Powered by IBM Developer Skills Network.
Issued by
Cognitive Class
January 6, 2023
Authenticity of this certificate can be validated by going to:
https://courses.cognitiveclass.ai/certificates/f6301aeea080480086a1e3cbddac6633
This is to certify that
(CO0101EN, provided by IBM)

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Modeling languages are generally applied for developing systems and software – both internally, with domain-specific languages, and with standardized languages targeting a general purpose and a wide audience. Way too often these languages are weakly created and defined leading to poor quality: Language definitions tend to contain errors and inconsistencies; notations do not recognize the communication and problem-solving needs of humans; standardization organizations push exchange formats that do not fully work and offer certificates that do not measure mastery of the language. We describe common problems in language development and point them out with examples from known cases. To overcome these problems, we suggest several solutions to improve language development, including using modeling languages specifically designed to define modeling languages, continuous testing and prototyping, and keeping language users in the loop.

What Goes Wrong with Language Definitions and How to Improve the Situation

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ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

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This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!

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