2022

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Data Engineering Project for Beginners - Batch edition

Start Data Engineering

1. Introduction 2. Objective 3. Design 4. Setup 4.1 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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Personal Knowledge Management Workflow for a Deeper Life — as a Computer Scientist

Simon Späti

With burnout and mental stress at every level of our lives, I find my Personal Knowledge Management (PKM) system even more valuable. As a human, I forget lots of things. As a dad, I have more responsibilities with remembering all things related to my kid. As a developer and knowledge worker, I re-use code snippets or create new things. That’s why a PKM system such as a Second Brain to store all of it in a sustainable way is crucial to me.

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Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science

KDnuggets

Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.

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Dataframe Showdown – Polars vs Spark vs Pandas vs DataFusion. Guess who wins?

Confessions of a Data Guy

There once was a day when no one used DataFrames that much. Back before Spark had really gone mainstream, Data Scientists were still plinking around with Pandas a lot. My My, what would your mother say? How things have changed. Now everyone wants a piece of the DataFrame pie. I mean it tastes so good, […] The post Dataframe Showdown – Polars vs Spark vs Pandas vs DataFusion.

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A Guide to Debugging Apache Airflow® DAGs

In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

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Ready-to-go sample data pipelines with Dataflow

Netflix Tech

by Jasmine Omeke , Obi-Ike Nwoke , Olek Gorajek Intro This post is for all data practitioners, who are interested in learning about bootstrapping, standardization and automation of batch data pipelines at Netflix. You may remember Dataflow from the post we wrote last year titled Data pipeline asset management with Dataflow. That article was a deep dive into one of the more technical aspects of Dataflow and didn’t properly introduce this tool in the first place.

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Building a Telegram Bot Powered by Apache Kafka and ksqlDB

Confluent

ksqlDB use case: see how apps can use ksqlDB to ingest, filter, enrich, aggregate, and query data directly with Kafka—no complex architectures or data stores needed.

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Telco 5G Returns Will Come from Enterprise Data Solutions

Cloudera

This blog post was written by Dean Bubley , industry analyst, as a guest author for Cloudera. . Communications service providers (CSPs) are rethinking their approach to enterprise services in the era of advanced wireless connectivity and 5G networks, as well as with the continuing maturity of fibre and Software-Defined Wide Area Network (SD-WAN) portfolios. .

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Data Pipeline Design Patterns - #1. Data flow patterns

Start Data Engineering

1. Introduction 2. Source & Sink 2.1. Source Replayability 2.2. Source Ordering 2.3. Sink Overwritability 3. Data pipeline patterns 3.1. Extraction patterns 3.1.1. Time ranged 3.1.2. Full Snapshot 3.1.3. Lookback 3.1.4. Streaming 3.2. Behavioral 3.2.1. Idempotent 3.2.2. Self-healing 3.3. Structural 3.3.1. Multi-hop pipelines 3.3.2. Conditional/ Dynamic pipelines 3.3.3.

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More Data Science Cheatsheets

KDnuggets

It's time again to look at some data science cheatsheets. Here you can find a short selection of such resources which can cater to different existing levels of knowledge and breadth of topics of interest.

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Top 38 Python Libraries for Data Science, Data Visualization & Machine Learning

KDnuggets

This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.

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What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

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7 Super Cheat Sheets You Need To Ace Machine Learning Interview

KDnuggets

Revise the concepts of machine learning algorithms, frameworks, and methodologies to ace the technical interview round.

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What Can AI-Powered RPA and IA Mean For Businesses?

KDnuggets

RPA and IA have stunned the business world by availing impressive, intelligent automation capabilities for scales of businesses across industries, which we'll know in this blog.

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How To Overcome The Fear of Math and Learn Math For Data Science

KDnuggets

Many aspiring Data Scientists, especially when self-learning, fail to learn the necessary math foundations. These recommendations for learning approaches along with references to valuable resources can help you overcome a personal sense of not being "the math type" or belief that you "always failed in math.".

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We Don’t Need Data Scientists, We Need Data Engineers

KDnuggets

As more people are entering the field of Data Science and more companies are hiring for data-centric roles, what type of jobs are currently in highest demand? There is so much data in the world, and it just keeps flooding in, it now looks like companies are targeting those who can engineer that data more than those who can only model the data.

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Agent Tooling: Connecting AI to Your Tools, Systems & Data

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

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How I Got 4 Data Science Offers and Doubled My Income 2 Months After Being Laid Off

KDnuggets

In this blog, I shared my story on getting 4 data science job offers including Airbnb, Lyft and Twitter after being laid off. Any data scientist who was laid off due to the pandemic or who is actively looking for a data science position can find something here to which they can relate.

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How Much Math Do You Need in Data Science?

KDnuggets

There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.

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Introduction to Pandas for Data Science

KDnuggets

The Pandas library is core to any Data Science work in Python. This introduction will walk you through the basics of data manipulating, and features many of Pandas important features.

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If I Had To Start Learning Data Science Again, How Would I Do It?

KDnuggets

While different ways to learn Data Science for the first time exist, the approach that works for you should be based on how you learn best. One powerful method is to evolve your learning from simple practice into complex foundations, as outlined in this learning path recommended by a physicist who turned into a Data Scientist.

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How to Modernize Manufacturing Without Losing Control

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri

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Git for Data Science Cheatsheet

KDnuggets

Knowing git is no longer an option for data professionals. Grab this handy reference sheet now and make sure you know how to git the job done.

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What To Expect for AI Quality Trends In 2023

KDnuggets

Based on the recent discussions with dozens of Fortune 500 data science teams, we can expect to see a continued spotlight on AI model quality in 2023.

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Understanding Bias-Variance Trade-Off in 3 Minutes

KDnuggets

This article is the write-up of a Machine Learning Lighting Talk, intuitively explaining an important data science concept in 3 minutes.

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Approaches to Text Summarization: An Overview

KDnuggets

This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.

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Optimizing The Modern Developer Experience with Coder

Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

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30 Resources for Mastering Data Visualization

KDnuggets

Want to master data visualization? This list of 30 resources and tools will help you get started on your path toward mastering data visualization.

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9 Skills You Need to Become a Data Engineer

KDnuggets

A data engineer is a fast-growing profession with amazing challenges and rewards. Which skills do you need to become a data engineer? In this post, we’ll take a look at both hard and soft skills.

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Easy Guide To Data Preprocessing In Python

KDnuggets

Preprocessing data for machine learning models is a core general skill for any Data Scientist or Machine Learning Engineer. Follow this guide using Pandas and Scikit-learn to improve your techniques and make sure your data leads to the best possible outcome.

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Frameworks for Approaching the Machine Learning Process

KDnuggets

This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?

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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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Sparse Matrix Representation in Python

KDnuggets

Leveraging sparse matrix representations for your data when appropriate can spare you memory storage. Have a look at the reasons why, see how to create sparse matrices in with Python, and compare the memory requirements for standard and sparse representations of the same data.

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Mathematics for Machine Learning: The Free eBook

KDnuggets

Check out this free ebook covering the fundamentals of mathematics for machine learning, as well as its companion website of exercises and Jupyter notebooks.

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The Complete Free PyTorch Course for Deep Learning

KDnuggets

Do you want to learn PyTorch for machine learning and deep learning? Check out this 24 hour long video course with accompanying notes and courseware for free. Did I mention it's free?

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How to Build a Data Science Enablement Team: A Complete Guide

KDnuggets

A Data Science Enablement Team consists of people from various departments like marketing, sales, product development, etc. They are responsible for providing the necessary tools and resources to help the data scientists do their job more efficiently.

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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.