Start Data Engineering
MAY 11, 2022
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.
Simon Späti
APRIL 6, 2022
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.
KDnuggets
DECEMBER 30, 2022
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.
Confessions of a Data Guy
DECEMBER 10, 2022
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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Whether you’re creating complex dashboards or fine-tuning large language models, your data must be extracted, transformed, and loaded. ETL and ELT pipelines form the foundation of any data product, and Airflow is the open-source data orchestrator specifically designed for moving and transforming data in ETL and ELT pipelines. This eBook covers: An overview of ETL vs.
Netflix Tech
NOVEMBER 9, 2022
By Vadim Filanovsky and Harshad Sane In one of our previous blogposts, A Microscope on Microservices we outlined three broad domains of observability (or “levels of magnification,” as we referred to them)?—?Fleet-wide, Microservice and Instance. We described the tools and techniques we use to gain insight within each domain. There is, however, a class of problems that requires an even stronger level of magnification going deeper down the stack to introspect CPU microarchitecture.
Confluent
DECEMBER 2, 2022
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.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
Start Data Engineering
DECEMBER 11, 2022
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.
KDnuggets
DECEMBER 30, 2022
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.
KDnuggets
DECEMBER 29, 2022
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
KDnuggets
DECEMBER 19, 2022
Revise the concepts of machine learning algorithms, frameworks, and methodologies to ace the technical interview round.
Speaker: Tamara Fingerlin, Developer Advocate
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
KDnuggets
DECEMBER 19, 2022
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.
KDnuggets
DECEMBER 16, 2022
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.".
KDnuggets
DECEMBER 9, 2022
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.
KDnuggets
DECEMBER 1, 2022
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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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.
KDnuggets
NOVEMBER 23, 2022
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.
KDnuggets
NOVEMBER 18, 2022
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.
KDnuggets
NOVEMBER 16, 2022
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.
KDnuggets
NOVEMBER 16, 2022
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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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?
KDnuggets
NOVEMBER 16, 2022
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.
KDnuggets
NOVEMBER 10, 2022
This article is the write-up of a Machine Learning Lighting Talk, intuitively explaining an important data science concept in 3 minutes.
KDnuggets
NOVEMBER 7, 2022
This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.
KDnuggets
NOVEMBER 2, 2022
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.
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.
KDnuggets
NOVEMBER 2, 2022
Want to master data visualization? This list of 30 resources and tools will help you get started on your path toward mastering data visualization.
KDnuggets
OCTOBER 24, 2022
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.
KDnuggets
OCTOBER 25, 2022
As more businesses experiment with data, they realize that developing a machine learning (ML) model is only one of many steps in the ML lifecycle.
KDnuggets
OCTOBER 19, 2022
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?
Speaker: Nikhil Joshi, Founder & President of Snic Solutions
Is your manufacturing operation reaching its efficiency potential? A Manufacturing Execution System (MES) could be the game-changer, helping you reduce waste, cut costs, and lower your carbon footprint. Join Nikhil Joshi, Founder & President of Snic Solutions, in this value-packed webinar as he breaks down how MES can drive operational excellence and sustainability.
KDnuggets
OCTOBER 17, 2022
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.
KDnuggets
OCTOBER 14, 2022
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.
KDnuggets
OCTOBER 14, 2022
Check out this free ebook covering the fundamentals of mathematics for machine learning, as well as its companion website of exercises and Jupyter notebooks.
KDnuggets
OCTOBER 12, 2022
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?
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.
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