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An educational side project

The Pragmatic Engineer

I’d like to share a story about an educational side project which could prove fruitful for a software engineer who’s seeking a new job. Juraj created a systems design explainer on how he built this project, and the technologies used: The systems design diagram for the Rides application The app uses: Node.js Persistence.

Education 363
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Data Engineering Projects

Start Data Engineering

Projects 3.1. Projects from least to most complex 3.2. Introduction 2. Run Data Pipelines 2.1. Run on codespaces 2.2. Run locally 3. Batch pipelines 3.3. Stream pipelines 3.4. Event-driven pipelines 3.5. LLM RAG pipelines 4. Conclusion 1.

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dbt multi-project collaboration

Christophe Blefari

cross-project dependencies ( credits ) Over the last few years, dbt has become a de facto standard enabling companies to collaborate easily on data transformations. Whatever the number, there will be a critical point at which a single project no longer scale. Cross-project references is a key enabler to data team decentralisation.

Project 264
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Top 20 Data Engineering Project Ideas [With Source Code]

Analytics Vidhya

Aspiring data engineers often seek real-world projects to gain hands-on experience and showcase their expertise. This article presents the top 20 data engineering project ideas with their source code. Whether you’re […] The post Top 20 Data Engineering Project Ideas [With Source Code] appeared first on Analytics Vidhya.

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Apache Airflow® Crash Course: From 0 to Running your Pipeline in the Cloud

Get an overview of need-to-know foundational Airflow concepts Create your first Airflow project in a local development environment Write your first DAG Deploy your DAG to the cloud This introductory tutorial provides a crash course for writing and deploying your first Airflow pipeline.

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Data Engineering Project for Beginners - Batch edition

Start Data Engineering

Introduction 2. Objective 3. Prerequisite 4.2 AWS Infrastructure costs 4.3 Data lake structure 5. Code walkthrough 5.1 Loading user purchase data into the data warehouse 5.2 Loading classified movie review data into the data warehouse 5.3 Generating user behavior metric 5.4. Checking results 6. Tear down infra 7. Design considerations 8.

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10 ChatGPT Projects Cheat Sheet

KDnuggets

KDnuggets' latest cheat sheet covers 10 curated hands-on projects to boost data science workflows with ChatGPT across ML, NLP, and full stack dev, including links to full project details.

Project 151
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5 Early Indicators Your Embedded Analytics Will Fail

In this White Paper, Logi Analytics has identified 5 tell-tale signs your project is moving from “nice to have” to “needed yesterday.". Many application teams leave embedded analytics to languish until something—an unhappy customer, plummeting revenue, a spike in customer churn—demands change. But by then, it may be too late.

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3 Challenges of Building Complex Dashboards with Open Source Components

Speaker: Ryan MacCarrigan, Founding Principal, LeanStudio

Watch this webinar with Ryan MacCarrigan, Founding Principal of LeanStudio, to learn about key considerations for launching your next analytics project. But what happens when you have a growing user base and additional feature requests?

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Why “Build or Buy?” Is the Wrong Question for Analytics

Every time an application team gets caught up in the “build vs buy” debate, it stalls projects and delays time to revenue. There is a third option. Partnering with an analytics development platform gives you the freedom to customize a solution without the risks and long-term costs of building your own.

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How to Find and Test Assumptions in Product Development

You'll learn: Why every product leader goes into a new project with untested, hidden assumptions. Watch this webinar with Laura Klein, product manager and author of Build Better Products, to learn how to spot the unconscious assumptions which you’re basing decisions on and guidelines for validating (or invalidating) your ideas.

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The Essential Guide to Building Analytic Applications

What should product managers keep in mind when adding an analytics project to their roadmap? What are best practices when designing the UI and UX of embedded dashboards, reports, and analytics? What should software teams know about implementing security that works with the rest of their products?

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Modern Data Architecture for Embedded Analytics

Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind.

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The Definitive Guide to Dashboard Design

Great dashboards lead to richer user experiences and significant return on investment (ROI), while poorly designed dashboards distract users, suppress adoption, and can even tarnish your project or brand.

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The Definitive Guide to Embedded Analytics

Inside you will learn: How embedded analytics has become essential to business applications When to buy an embedded analytics solution and when to build one How to go-to-market, from pricing and packaging to external promotion How to build a business case and sell the project internally The future of embedded analytics …plus so much more.