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.
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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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Apache Airflow® is the open-source standard to manage workflows as code. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries. Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering.
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The Pragmatic Engineer
NOVEMBER 17, 2022
👋 Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. We cover one out of five topics in today’s subscriber-only The Scoop issue. To get this newsletter every week, subscribe here. This article was updated in December 2022. In the midst of gloomy news about hiring freezes and layoffs, let's highlight companies which are growing and hiring.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
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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.
Cloudera
APRIL 22, 2022
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. .
Christophe Blefari
DECEMBER 30, 2022
kitsch moment, from me to you ( credits ) Hey you, this is the last article of the year and it's gonna be about the articles and trends that made 2022 according to me. You'll see articles that I've already share during the year. 💡 You can also read the 2021's must-read that I've done one year and half ago or how to learn data engineering that contains key articles to understand the field.
Data Engineering Podcast
DECEMBER 29, 2022
Summary Making effective use of data requires proper context around the information that is being used. As the size and complexity of your organization increases the difficulty of ensuring that everyone has the necessary knowledge about how to get their work done scales exponentially. Wikis and intranets are a common way to attempt to solve this problem, but they are frequently ineffective.
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!
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Seattle Data Guy
DECEMBER 29, 2022
AWS has jumped on the bandwagon of removing the need for ETLs. Snowflake announced this both with their hybrid tables and their partnership with Salesforce. Now, I do take a little issue with the naming “Zero ETLs”. Because at the very surface the functionality described is often closer to a zero integration future, which probably… Read more The post Should We Get Rid Of ETLs?
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.
Simon Späti
JUNE 20, 2022
Data consumers, such as data analysts, and business users, care mostly about the production of data assets. On the other hand, data engineers have historically focused on modeling the dependencies between tasks (instead of data assets) with an orchestrator tool. How can we reconcile both worlds? This article reviews open-source data orchestration tools (Airflow, Prefect, Dagster) and discusses how data orchestration tools introduce data assets as first-class objects.
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.
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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.
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Confessions of a Data Guy
DECEMBER 29, 2022
Data engineering is a vital field within the realm of data science that focuses on the practical aspects of collecting, storing, and processing large amounts of data. It involves designing and building the infrastructure to store and process data, as well as developing the tools and systems to extract valuable insights and knowledge from that […] The post I asked ChatGPT to write a blog post about Data Engineering.
The Pragmatic Engineer
DECEMBER 8, 2022
👋 Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. We cover one out of five topics in today’s subscriber-only The Scoop issue. To get this newsletter every week, subscribe here. On Thursday, 29 November, Snap CEO Evan Spiegel, sent an email announcing Snap will mandate 4 days/week in the office, starting from January.
Netflix Tech
DECEMBER 3, 2022
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.
Confluent
JULY 26, 2022
Explore GitHub Actions for your Kafka CI/CD pipeline, automate Schema Registry, and transform the development and testing of Kafka client applications.
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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
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.
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.".
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
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.
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.
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
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.
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.
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