We are looking into the network communication protocol map. I first saw this like 10 years ago and its awesome.
Then we check out the Big Data and Data Science Landscape image. It shows you all the tools available to do data science, machine learning and data engineering. Which is very helpful if you are researching for tools to use.
Before using the Twitter API you got to create a developer account. So, I show you how I created one. After that I tried to get Nifi to download Tweets but it is not working.
Today's podcast is a bit of a behind the scenes.
What it takes to do a audio podcast. How you can get audio to text transcriptions for free.
.Also Github questions on how to work with branches on the Cookbook
In this episode we install the Nifi docker container and look into how we can extract the twitter data.
We are also talking about the differences between infrastructure as a service, platform as a service and application as a service.
In this episode we talk about the lambda architecture with stream and batch processing as well as a alternative the Kappa Architecture that consists only of streaming. Also Data engineer vs data scientist and we discuss Andrew Ng's AI Transformation Playbook
How do you choose between Cloud vs On-Premise, pros and cons and what you have to think about. Because there are good reasons to not go cloud.
Also thoughts on how to choose between the cloud providers by just comparing instance prices. Otherwise the comparison will drive you insane.
I decided to rework the cookbook focusing more on case studies and less on explaining tools.
People keep asking me for a path to become a data engineer and, let's be honest, you will never achieve that with just knowledge of the tools.
Finding out how companies do data engineering on their data science platforms is way more useful.
Over the next weeks we will go over each study on my YouTube channel. The stuff we talk about will then go into the cookbook too.
The Internet of things is a huge deal. There are many platforms available. But, which one is actually good?
Join me on a 50 minute dive into the Siemens Mindsphere online documentation.
I have to say I was super unimpressed by what I found.
Many limitations, unclear architecture and no pricing available?
On this podcast I talk about data warehouses and data lakes.
When do people use which? What are the pros and cons of both?
Architecture examples for both and does it make sense to completely move to a data lake?
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.
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 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.
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:
Check out my Patreon for the Data Engineering Cookbook:
"Day One" by Declan DP
Attribution 3.0 Unported
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.
"Day One" by Declan DP
Attribution 3.0 Unported
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 training at home?
Real time analytics with RDBMS or HDFS?
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 questions one by one.
Turns out it's a lot of fun :)
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 source code management systems like GitHub. How does all that work? Check out this episode to find out :)
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, Azure or Google Cloud. Big Data tools like Kafka, Spark or server less like Kinesis or Lambda functions.
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 episode.
Maybe you have the same problem.
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.
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 Bandcamp. He is also on Spotify ;)
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.
There is this other data science job called data engineer and it's super important. Because data science does not equal data scientist.
In today's podcast I talk about how I finally realized that data engineering is my real passion.
Definitely check it out, chances are high that data engineering is your thing too.
I have recently been asked: "What is the difference between BI, Data Science and Big Data". So, it thought I make a quick podcast about this for you guys. I think especially beginners will help this a lot.
There are three different methods of streamging: At least once, at most once and exactly once. Listen why it makes a huge difference which one you use. Because not every system or tool supports all three.
Loosing money with data science in the short term does not matter. It's about the long run, not quick sales.
This is a story about how this happened in the insurance industry. And how to go at it to turn this loss into a win
It is very important for me to keep track of emerging technologies and trends. You want to know where the industry is headed. You don't want to miss the next big thing.
This is where Gartner's Hype Cycles help a lot.
Especially the hype cycle for emerging tech.
Unavailable cellphone coverage really pissed me off. You cannot transmit data and do cloud based analytics during that time. That is why edge devices in the field have to get more and more get analytics capabilities built in. For instance in a container ship.