Plumbers of Data Science

In this episode I show you the first version of my data engineering cookbook.

Getting a book and reading it cover to cover is useless. In this episode I show you my strategy of buying books complimentary to your work. And 5 great books I read over the years that helped me get where I am now.

In this podcast we talk about the differences between data scientists, analysts and engineers. Which are the three main data science jobs. All three super important.

After all the BS solutions using Blockchain I thought I finally found one that makes sense. Of all the possibilities it's the EU data protection law GDPR. Well, one problem I overlooked in this podcast is, that it is impossible to delete data after it is in the chain. That's however a rule for GDPR. So, I was wrong. Again :D

In this episode Kate Strachnyi interviews me for her humans of data science podcast. We talk about how I found out that I am more into the engineering part of data science. 

In this episode I show you how much data science graduates are actually payed in Germany. All over the internet you can find that Data Science salary is over 100k Dollars. Data Engineer or Data Scientist. It's way lower then that. Then I give you a few really good tips on how to choose the right company to work for. Huge corporation, startup or small company? Here's how to choose.

In this podcast I am showing you how I use GitHub to write my Data Engineering Cookbook with LaTex.

What is the best editing tool to write a thesis, a dissertation or a paper? NOT Word or Pages! It's LaTeX. In today's video I show you why I decided to use LaTeX to write my data engineering cookbook. I used it before for my diploma thesis and I am in love again :) Here's the link to the cheatsheet: https://wch.github.io/latexsheet/latexsheet.pdf Check out my Patreon for the Data Engineering Cookb...

You have certifications or a university degree, but can't find a job? Sharing your ideas and knowledge will increase your chances! Here's how you can do that. Music: "Day One" by Declan DP https://soundcloud.com/declandp Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/

I love agile development. People keep telling you to do Scrum, like it's the only and best choice to be agile. It's not. Here's my take on scrum and my four main beefs with it. Watch out for these issues if you are doing scrum.

So, Cloudera and Hortonworks merge... In today's Plumbers of Data Science Podcast I talk about what these, big data vendors do. How they enable companies, admins and developers to do data science and many more things. If you are interested in the whole hadoop ecosystem you need to check out this episode. You won't regret it ;)

Is ETL dead in Data Science and Big Data? In today's podcast I share with you my views on your questions regarding ETL (extract, transform, load). Data Lakes & Data Warehouse where is the difference? Is ETL still practiced or did pre processing & cleansing replace it What would replace ETL in Data Engineering? How to become a data engineer? (check out my facebook note) How to get experience traini...

What's the difference between Data Scientists & Data Analysts? What to do to find internships or a full time job? Data Scientist and Engineer in large and small companies where's the difference? Are Data Engineers generalists or specialists? Just some questions I go over in this podcast. You sent me over 100 Questions so, I finally worked up the guts to start with the Q&A videos. Answering your qu...

Without the proper tools and techniques of version control the team's efficiency goes down the drain. In this episode I talk about how tools like Jira enable you to collect bugs, future features or change requests. How they enable you to create and organize versions, add items to a version and assign items to developers. Once this is done, the team can efficiently start coding with the help of sou...

You need to become comfortable with distributed processing. Data Science or the Internet of Things, the amount of data that is getting produced and processed grows like crazy. In this podcast I talk about how a platform for distributed processing looks like. I talk about the different layers that need parallelization, as well as the tools you can use for on premise installations or clouds like AWS...

For me, school and university was hard. The lectures, sitting down and getting told how things work. Reading books and learning dry stuff was a drag. I was never good at writing tests. Some people excel at this. I was often envious. Over the years I found out what my problem is. I learn differently. I am a learning by doing guy. What does that means and how am I dealing with it? Check out this epi...

Becoming an expert in single skill is not the way to go for a data engineer. In this episode I talk about which talents go good together in terms of technical and personal ones. So, that you build up a stack of knowledge that will make you a great data engineer.

Strong APIs make a good platform. In this episode I talk about why you need APIs and why Twitter is a great example. Especially JSON APIs are my personal favorite. Because JSON is also important in the Big Data world, for instance in log analytics. How? Check out this episode!

Security is everything! That's why today, I took some time to give you some tips about how to make a good design. The Lambda Architecture with stream and batch processing is one of the cornerstones for Big Data and Data Science. How does that fit into a security zone design? Check out this episode :)

The understanding of how information is transported over the network is super important. OS wise you will mostly encounter Linux so here are some important Linux basics you need to know. Firewalls, Ports, IP-Adresses, Routers and Switches, only a few things I talk about in this podcast. Networking infrastructure also matters for Big Data systems like Hadoop, Kafka and Spark.

Knowing the hardware is super important for a data engineer. Even if you are using cloud servers. CPU, RAM, GPU, HDD, SSD... Especially the GPU is a great help to Data Scientists who are doing machine learning.

I am bringing the Podcast back! Lets call it season 2. New name, new mission: Helping you become a data engineer. Daily podcast, recorded in my car or my office, getting you up to speed ASAP.

8 V's, 10 V's, 12 V's . The best way to explain Big Data is to use the four V's: Volume, Velocity, Variety and Veracity. In this podcast episode I talk about why nobody needs 10 or more V's of big data. And how Big Data is almost a must have, to do data science and especially machine learning. The music in this episode is the song The Quiet Earth by Thomas Barrandon. Check out his awesome music on...

In this episode I give you my take on why companies badly need data scientists and engineers. Because in this data driven world, you can accomplish a lot with just a few people. All you need is a vision, some sense for business and a lot of skill.

This podcast is all about what you as a data engineer really do. From building platforms to collaboration with data scientists and customers. Everything you need to know to get insight into a data engineers life.