Apache Flink with Silicon Valley Engineers

Are you using AI but still typing new prompts every single time? Code is being generated, but are team-level automation and systematization still out of reach? This course is not about learning Claude Code as a "conversation tool." It is a course where you will learn how to design AI as a system using Agents, Sub-agents, Agent Teams, Memory, and Hooks.

"Enter the world of Cloud Infrastructure Automation!" Terraform, an infrastructure automation tool, is used to provision and manage resources in a cloud environment. Now, you too can define and automate your cloud infrastructure as code to quickly and consistently configure an infrastructure environment!

"The technology that prepares you for the future, Docker" This course focuses on learning Docker quickly and easily. It explains the basic concepts and usage of Docker in an intuitive and easy-to-understand way, and through interesting practical exercises, you can learn solid skills that can be used immediately in practice, such as creating containers, building images, and setting up networks. This is the perfect course for those who want to quickly absorb and apply the latest technology to their work!

Now, your AI is no longer in the cloud. It runs directly inside your laptop. Using LM Studio and Ollama, we will show you how to build a Private AI environment that runs entirely locally— from data processing and document analysis to code generation. Without security concerns, without costs, and even faster— Move beyond simply "using" AI and start "truly mastering" it.

Learn Everything About Python from a Silicon Valley Software Engineer. Currently, as a 15-year veteran software developer, I handle everything from web applications, big data, and even SRE & DevOps, all with Python. From the basics of Python to advanced techniques, don't miss this opportunity to learn all the skills and know-how for using Python in real-world Silicon Valley practice!

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

Apache Superset is an open-source BI tool that lets you quickly create charts and dashboards by connecting multiple databases. You can visualize data without knowing code, and SQL Lab allows you to run queries directly, making it useful for everyone from beginners to professionals. The dashboards you create can be shared immediately with permission settings, making it perfect for collaboration. Learning Superset gives you the ability to make data-driven decisions quickly and build practical dashboards you can use right away at work. In short, it's perfect for anyone who wants to make data "speak." 🚀

This is a practical, hands-on course designed so that even SQL beginners can start easily and apply it right away. ✨ Learn to pull data with SELECT, connect with JOIN, organize with GROUP BY, and more— master only the most frequently used features in real work, in an easy and fun way! 📊 We minimize complex explanations and focus on realistic examples that make you think, "Ah, this is why SQL is important!" 💡 Data analyst, developer, data engineer… Whatever path you choose, SQL is an essential skill. 🚀 With this one course, solidify your fundamentals and develop the data intuition to solve problems on your own in real-world situations! 🔥 Start right now. This is the easiest first step into the world of data! 🌍💙

You will learn Apache Airflow, the most widely used Orchestrator for creating software data pipelines.

From a developer who only used ChatGPT to a developer who handles AI agents. Learn practical ways to maximize coding productivity using Codex's Rules, Hooks, Skills, and MCP.

This course teaches you how to build AI agents from scratch to a production-ready level using OpenClaw. Beyond a simple introduction to features, you will master OpenAI, local LLMs, memory, sessions, Tools, and Skills in a single, cohesive workflow. Based on hands-on experience and know-how gained from developing AI and large-scale systems in Silicon Valley, the course explains architectures and operational methods applicable to real-world projects. By the end of the course, you will be able to design your own AI agents and implement automation by integrating them with various services. This is a practice-oriented course for those who want to go beyond just using AI and learn how to build and operate it themselves.

Master AWS networking completely! Learn VPC, subnet, routing, and security with a focus on practical application, and design secure and scalable cloud network architectures. Complete the core skills that every working engineer must know in a short period of time!

Learn how modern data teams leverage dbt. Build maintainable analytics models and validated data pipelines hands-on. Master core concepts of analytics engineering with a practical focus. This course is for those who want to take their data career to the next level 🚀

Uncover true value in the age of data! 📊 Designing applications focused on data has now become essential. Develop the insight and skills your company wants with the latest trends and practical case studies. Start now with efficient data processing and design secrets! Your next step, leap into the data-driven world!

How to Read the World with Data: Explore Your Own Data Science! Dive deep and practically into the tools and techniques that form the core of modern data science. In particular, you will learn how to analyze data and implement algorithms using Anaconda, Numpy, Pandas, and Scikit-learn, essential components of data science.

🔥 Vibe Coding with Streamlit 🔥 Real-time coding experience where you can instantly create your own web service with just one line of code! Visual-focused, hands-on lectures tailored to MZ sensibilities - no complex backend knowledge required. Learn the basics of Streamlit and awaken your development instincts by directly implementing practical apps. Complete your own "personal service" with code in a vibe way right now 😎💻

“There are many ways to solve problems, but why do we feel lost during interviews?” This lecture offers coding interview survival strategies directly from a current engineer working in Silicon Valley. Focusing on problems frequently encountered in real interviews, we'll guide you not just through simple solutions, but also how to articulate your thought process and how you'll be perceived from the interviewer's perspective. Designed for mid-level developers and above, the curriculum is optimized for real-world interviews. If you are preparing for coding tests and technical interviews, gain practical skills and confidence through this lecture.

Are you still managing ML experiments manually? 🤔 Automate everything from experiment tracking to model deployment with MLflow, and dramatically boost your development productivity! An essential skill for data scientists and ML engineers 💡 Start right now! 🚀

The essential technology of the cloud era, the complete guide to Amazon EKS! Manage Kubernetes clusters easily and quickly with EKS, from container orchestration to practical application.

Gradle Masterclass for Efficient Build Management – Learn how to automate complex builds and increase project productivity. This course covers the core concepts and practical application of Gradle in detail. You will be able to build a professional-level build environment in no time.

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

You will learn Apache Airflow, the most widely used Orchestrator for creating software data pipelines.

This is a practical, hands-on course designed so that even SQL beginners can start easily and apply it right away. ✨ Learn to pull data with SELECT, connect with JOIN, organize with GROUP BY, and more— master only the most frequently used features in real work, in an easy and fun way! 📊 We minimize complex explanations and focus on realistic examples that make you think, "Ah, this is why SQL is important!" 💡 Data analyst, developer, data engineer… Whatever path you choose, SQL is an essential skill. 🚀 With this one course, solidify your fundamentals and develop the data intuition to solve problems on your own in real-world situations! 🔥 Start right now. This is the easiest first step into the world of data! 🌍💙

SQLD, where should I start? For those of you feeling completely overwhelmed, this is the lecture for you. You will meet a transformed version of yourself after completing the course. SQLD All-in-One Pass!

Learn how modern data teams leverage dbt. Build maintainable analytics models and validated data pipelines hands-on. Master core concepts of analytics engineering with a practical focus. This course is for those who want to take their data career to the next level 🚀

It covers basic data concepts, useful R functions and packages, and data analysis exercises, to allow anyone to perform big data analysis using R programming.

Redis is a fast and scalable open-source database. This technology is used to maximize performance in real-time applications, games, social media platforms, and more. However, to truly understand the power of Redis and use it effectively, you need a deep understanding. Now is your chance to learn Redis in a professional way. "Redis with Silicon Valley Engineers" covers a wide range of topics, from the core concepts of Redis to advanced features. This course is designed for everyone, from beginners who are new to Redis to advanced users.

Learn the process of building data pipelines using Apache Airflow, from the basics to practical application. Understand the core concepts and architecture of Airflow, and master advanced design patterns frequently used in the field—such as dynamic DAGs, parallel processing, distributed processing, and custom operators—through hands-on practice. Configure a practice environment using Python and Docker to develop the practical skills needed to design and operate real-world workflows.

We introduce methods for planners and marketers without data analysis experience or technical skills to try data analysis at the most basic level, complete with various examples. Built on years of hands-on practice with over 2,000 participants in more than 100 companies and public institutions, the content is designed around the most practical analysis methods for data non-experts.

Most developers are still stuck on Batch and CronJobs when it comes to data processing. However, in real-world production environments, data is generated continuously, and failing to process that flow immediately leads to latency, bottlenecks, and consistency issues. Having personally dealt with real-time recommendations, state synchronization, and event delay problems in high-traffic environments, I’ve often asked myself, "Is batch processing really the right approach for this?" This course begins with that very question. It explores how to use Apache Flink to perform computations the moment data flows, manage state securely, and produce accurate results based on Event Time—all from a practical perspective. Beyond simple theoretical explanations, you will experience how real-time stream processing systems are designed and operated through actual source code and architectures. For those who found real-time processing vague or were curious about the world beyond messaging, this course will provide a clear sense of direction.

Uncover true value in the age of data! 📊 Designing applications focused on data has now become essential. Develop the insight and skills your company wants with the latest trends and practical case studies. Start now with efficient data processing and design secrets! Your next step, leap into the data-driven world!

This course does not teach the grammar of C#. It simply uses C# as a tool for designing object-oriented programs. This course covers in depth the essential content that intermediate and higher level programmers need to know, as well as the content that is essential to developing programs that are linked to databases.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

This course teaches Python-based text mining: basic theory and practice. It covers fundamental text mining data analysis for practical or thesis writing.

mongoDB, NoSQL You hear a lot these days, but is it still an unfamiliar database? Aren't you using it like a relational database (RDS/SQL)? No matter how good the technology is, if it's not used correctly, it will have the opposite effect. That's why we often see cases of failure in using MongoDB. This lecture will teach you everything from basic concepts to practical know-how so that you can use MongoDB as MongoDB-like as possible.

[Strategic Roadmap for System Control and Prevention of Cognitive Atrophy in the AI Era] 1. Introduction: Technical Initiative and Strategic Command (Strategic Command vs. Passive Dependence) A core insight derived from the past 40 years of automotive R&D and corporate management is that entities that lose technical control are highly likely to devolve from beneficiaries of a system into its dependents. In particular, the proliferation of Artificial Intelligence (AI) technology—comparable to high-performance engines—presents a crossroads: it will either leave humans as "passive passengers" of technology or allow them to leap forward as "strategic commanders" who master the system. The indiscriminate dependence on AI observed today is accelerating "Cognitive Offloading," a phenomenon where humans entirely delegate their inherent thinking and analytical mechanisms to machines. This induces the deactivation of the brain's Executive Control Network (ECN) and carries the risk of a structural crisis known as "Cognitive Atrophy," leading to the functional decline of the frontal lobe in the long term. This course aims to present a strategic methodology to strengthen human cognitive capabilities and safeguard intellectual sovereignty in response to this intellectual crisis. 2. Five Core Methodologies for Safeguarding Cognitive Sovereignty ① Maintaining Cognitive Plasticity and Designing Intentional Cognitive Load (Cognitive Gym) The convenience of AI, which provides immediate and seamless answers, can lead to a disconnection in thinking and the omission of critical review processes. To prevent this, it is necessary to design intentional "Cognitive Friction" within work processes. By counter-utilizing AI's automation functions to forcibly delay and deepen the human thinking process, advanced training must be conducted to stimulate neuroplasticity and raise the threshold of thought. ② Establishing an Adversarial Verification System Based on Multi-Agent Systems (MAS) Human cognitive systems are susceptible to "Automation Bias," the tendency to uncritically accept AI outputs. To offset this bias, it is effective to operate a "Critique Agent" or a virtual "Red Team" that analyzes and attacks the vulnerabilities of the logic, separate from the primary model that follows the user's instructions. This forces the process of building defensive logic, thereby activating "System 2 (deliberative thinking)" as defined by Daniel Kahneman. ③ Implementation of Literacy-Based Dual-Track and RQTDW Learning Methods The ability to verify digital tools is directly proportional to one's analog foundational thinking system. While maintaining literacy to deeply grasp the context of text, it is recommended to internalize the 5-step RQTDW roadmap: Read (Deep Reading): Perform a multi-faceted identification of the source of information. Question (Questioning): Pose critical questions regarding logical consistency and the validity of premises. Think (Confronting Contradictions): Analyze and contemplate trade-offs and logical gaps between information. Discuss (In-depth Discussion): Diversify points of contention through virtual or actual discussions. Write (Reconstruction): Refine the results of expanded thinking into human-specific language for systemic internalization. ④ Applying the Sandwich Workflow to Clarify Accountability Delegating the entire work process to AI poses a high risk of cognitive paralysis; therefore, it is essential to establish a rigorous workflow that structurally separates the roles of humans and AI. Context Design Phase (Top Bun): Setting the purpose of the task, imposing constraints, and designing the overall architecture must be performed under human leadership. Data Processing Phase (Meat): Tasks involving repetitive and large-scale resources, such as calculating vast data, sorting, and drafting, are delegated to AI. Final Verification Phase (Bottom Bun): Ethical judgment, precise cross-referencing of facts (Fact-check), and final value attribution are returned to the human domain of responsibility to ensure system stability. ⑤ Hallucination Control and Strengthening Epistemic Boundaries via the SIFT Model AI possesses the attributes of a "stochastic parrot," combining tokens based on probabilistic frequency without a substantive understanding of meaning. Therefore, one must be wary of the "illusion of knowledge" created by AI's fluent output. To this end, a 3-step fact-checking protocol and the SIFT model must be strictly applied in practice. The habit of "Lateral Reading"—tracking original sources and contrasting them with external data—becomes a key mechanism to prevent intellectual free-riding on technical convenience. 3. Conclusion: The Strategic Mission of the Hyper-Intelligent Helmsman While the phenomenon of intelligence is manifested through engineering design, the core agent that controls it in a meaningful direction and creates business value remains rigorous human thinking. This masterclass is designed to ensure that students acquire the capabilities of a "Hyper-Intelligent Helmsman" who controls the powerful power source of AI and designs organizational systems. Strengthen the cognitive muscles of individual members and actively respond to technical challenges. When rigorous engineering control is combined with advanced cognitive abilities, AI will finally function as a strategic asset that drives the sustainable growth of both members and the organization.

This is a practical project lecture that collects data and analyzes big data using Arduino and wind sensors.

SQLD Yellow Book 176 Problem-Solving Course for those who studied but can't solve problems. After completion, you'll go beyond passing to become an expert. SQLD All-in-One Pass!

You can learn the core essentials of GA4 (Google Analytics 4) e-commerce setup—which is often too difficult to learn alone just by searching the internet—through hands-on practice. This course is designed so that performance marketers and beginner developers setting up GA4 e-commerce for the first time can easily understand it by practicing step-by-step on a Cafe24 demo shopping mall. Since we delegate the developer's role to ChatGPT during the practice sessions, even marketers, PMs, or planners without professional development knowledge can learn how to set up GA4 e-commerce.

Let's use the R language, which processes data easily and conveniently, to help even non-computer science majors develop their skills in utilizing computer programs related to their own fields!