Hey you!!! Happy new year!! 2020 was a strange year, but I can’t complain. Lot of things, knowledge, worked a lot. So, let’s keep going in 2021!!!

So, let’s talk a little about security. I’ve been working within an interesting project to make GCP Security Command Center to work with some external tools and automation with Cloud Functions. And guess what: as I love automation, here’s what I’ve being studying:

Hey you!!! Happy new year!! 2020 was a strange year, but I can’t complain. Lot of things, knowledge, worked a lot. So, let’s keep going in 2021!!!

So, let’s talk a little about security. I’ve been working within an interesting project to make GCP Security Command Center to work with some external tools and automation with Cloud Functions. And guess what: as I love automation, here’s what I’ve being studying:

I’ve finally got some “free” time to learn a little more about Machine Learning. So, that said, I’m almost crazy with Math (again, same feeling of 90's).

So, I'm using this to study:

• The Hundred-Page ML Book
• Building ML and Deep Learning Models on GCP
• Loooot of MIT Youtube videos related with the topic
• Google Qwiklabs
• Kaggle
• Google Colab ❤️

It has been really productive, even for me (I'm not a developer, so it's hard sometimes to go ahead with a few topics). So, my starting point was to run the Titanic study case from Kaggle but using now Bigquery…

Data Pipeline is basically move some data from one place to another. This move may have or not changes, processing, …

For companies that works with big ammount of data, this is basic job. Data is received, transformed, enriched with other data if needed, moved to data lakes or any other place, and most of times, finished with some beautiful dashboard.

We have in Google Cloud the Data Fusion service. Basically is a service for efficiently building ETL/ELT data pipelines. It uses Cloud Dataproc cluster to perform all transforms in the pipeline. …

If you don’ know the best way to use it, of course!

BigQuery it’s the Data Warehouse solution fom Google Cloud. And it’s REALLY amazing what you can do with this tool!!! But at same time, it can take you to ruin!! Some of great points about BigQuery is that is serverless, highly-scalable and you can plugin a lot of sources to analyze.

There’s also a lot of main points like multi-cloud analytics, machine learning with SQL, GIS, BI, Sheets connections, … sky is the limit to analysis.

The only point here is about the pricing. BigQuery charges for data…

Terraform is great. It helps you keep things clear and allow you to provision almost everything in the Cloud (I know, not only in Cloud, but this is what I've being using).

One more challenge that I took to myself when I got "in charge" for Terraform in a new project… 1 month to certificate (less, actually). As I did about AWS on this post, again, I plan my "journey" to take the HashiCorp Certified: Terraform Associate Certification.

Início de Maio eu coloquei como meta estudar 30 dias para a certificação AWS Data Analytics Specialty. Não cumpri, e levei 35 dias. Mas consegui!

What kind of name is Kinesis? Well, looks like it means "movement in response of an external stimulus". And in fact, it makes sense… in AWS, Kinesis works as Data Streams, so, it will "receive" something and "supply" this something.