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 Bandcamp. He is also on Spotify ;) Have fun!
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.
Big Data tools are very important. But how deep should you really go as a data scientist? How do you best learn all this stuff? Some questions I try to answer in this episode.
Time series data is tricky. Especially if you have missing data. In this episode I talk about a few things you can do to handle this problem.
A 5 minute rant why the cloud is safe.
Hey Podcast, in this episode I talk about the core functions of Hadoop.
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.
Where to get dirty data to train cleaning it? What are unicorn data scientists and what is THE skill you need if you aren't one.
How to define a data science problem.
NoSQL databases like HBase are awesome! But why and when should you use them? How does a key value store like HBase work? Today I am talking about exactly that
Creating a Gaming-AI with Reinforcement Learning • Creating a Gaming-AI with Reinforcement Learning
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
Swish the Swedish Fintech that Blew My Mind in 2017
Chat and Q&A about data science with data scientists
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.
How to convince people that machine learning actually works? It's simpler than you think. You have the data. Data doesn't lie!
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.
In this episode I talk about the 4 Vs of big data. And how Big Data can save you from catastrophic success.
Doing machine learning in production is very different then for proof of concepts or in education. One of the hardest parts is keeping models updated.
Choose between Big Data & Data Science • Choose between Big Data & Data Science • Choose between Big Data & Data Science
How Data Science Transforms Agriculture | DailyKayy 004 • How Data Science Transforms Agriculture | DailyKayy 004 • How Data Science Transforms Agriculture | DailyKayy 004
Insurance companies use data science to detect fraudulent cases. Saving themselves and the customers money
How Smartwatches and Fitbits Transform Healthcare • How Smartwatches and Fitbits Transform Healthcare
