Cloud Computing Return of Experience in France

Having worked in North America for 5 year prior to joining Cloudreach in France during 3 years helped me see how far behind we were in terms of cloud adoption in France. But during these 3 years I really got the opportunity to see AWS, GCP and Azure convincing all the large French companies and the cloud becoming more mature and an acceptable and safe option.

Some of the motivators that force companies to move to the Cloud:

  • Startups that began to compete with them and are faster are executing because they are already in the Cloud
  • Number of new services available in the Cloud, automating common solutions and making costly existing internal processes or VMs completely useless
  • IoT, AI, Containers, Datalake and data processing speed capabilities offered by the Cloud
  • Unique capabilities of the Cloud to innovate and play with different services very quickly
  • Large companies that successfully made the move to the Cloud, removing doubts that some C-level had in the past
  • Cool and trendy technologies making recruitment of talents easier

I have seen companies mostly struggling with:

  • Designing an organised Landing Zone that will host everything that they will create in the Cloud
  • Setting up correct permissions and roles for user access and machine authentication
  • Building their network including planning for the future of their organisation
  • Choosing the right tools for automation of their infrastructure and their applications
  • Estimating the cost of their infrastructure and optimising it after the first month of use
  • Dealing with operations after the creation of the resources
  • Bad knowledge of Cloud providers SLAs for the services they use and what they need to change to make it acceptable for production

Pro tips to make the move easier

Train your employees and help them to get certified with the fantastic ACloudGuru, Coursera and Udemy. Yes the certifications are costly and a very good business for Cloud providers but they will assure a correctly set up infrastructure.

Then if you haven’t read it already there is great blog post about the purpose of having a Cloud Center of Excellence (CCOE) to transform your entire organisation.

Architecting the right way

Having an experienced Cloud Architect among your team will make a significant difference. Read the Well Architected Framework from AWS. Document as much as you can, make clean designs with Lucidchart and their library of AWS, Azure and GCP icons. Iterate and keep them up-to-date.

In terms of infrastructure automation, most companies are using the solution made by their Cloud provider or Terraform. For application automation many solutions are available on the market like Ansible, Chef, Puppet, etc. Don’t try to do both with the same tool.

If you call for help to automate your infrastructure, don’t let a company or a partner just deliver the code to you. Train your people first and make them participate to the code reviews until they are able to reuse and make their own templates.

To conclude, every company has its own level in term of Cloud Adoption: some are slowly moving to instances with a bit of automation, others are born with containers and serverless. Whatever your situation is, I hope this post would have helped. Enjoy your cloudy adventure!

Google Cloud Data Engineer Professional certified!

One more GCP certification on the list! This one was by far the most interesting one in a while as it gave me a chance to review topics that I don’t work with every day: Machine learning and Big data.

Let’s dive right in, here is the preparation I followed. To begin with the online classes:

Read Google official documentation about the services in the scope of this exam:

  • Cloud Storage, Google Transfer appliance
  • Cloud SQL, Cloud Bigtable, Cloud Bigquery, Datastore
  • Pub/Sub, Dataflow, Dataprep, Datastudio, Datalab
  • Stackdriver, KMS, Machine Learning and its API

Then jump on the Google Next videos:

You can review case studies but don’t spend too much time on them. There is now a practice exam for the Data Engineer, quite similar to the type of questions you can find in the real exam:

My feedback on the exam:

  • Check the scope of this exam, be prepared for design questions on database models, optimization and troubleshooting
  • Know Bigquery VS Bigtable VS Datastore VS Cloud SQL
  • Dataflow and how to deal with batch and stream processing
  • Read as much as you can and play with machine learning!
  • How to share datasets, queries, reports is really something that comes often, don’t underestimate security aspects
  • Understand Hadoop ecosystem, learn about the typical big data lifecycle on GCP

Good luck to everyone taking this exam!

Google Cloud Platform – Machine learning APIs

I have been watching a few Google Cloud Platform videos recently from Google Cloud Next and really enjoyed the demo in one of them: Machine learning APIs (Demo @11″35).

The idea is simply to record your voice (here using the microphone on your laptop). Then the audio file is sent to Cloud Storage.

By using Google Speech, you can not only get a transcript of your record, but you can add additional context words in your API call to make sure GCP understands it perfectly.

"speechContext": { "phrases": ["GKE", "Kubernetes", "Containers"] }

I tried to work on the script to do the exact same thing and decided to share it if you want to try it at home.
Prerequisites are:

  • A GCP projet
  • Run the following command on your laptop:
    brew install sox --with-flac
  • Download and install Google Cloud SDK
  • Create a Cloud Storage bucket
  • Create an API Key and give it access to Google Speech
#!/usr/bin/env bash

# Configuration

gcloud auth login $GCP_USERNAME
gcloud config set project $GCP_PROJECT_ID

# Recording with Sox (brew install sox --with-flac)
rec --encoding signed-integer --bits 32 --channels 1 --rate 44100 recording.flac

# Upload to Cloud Storage
gsutil cp -a public-read recording.flac gs://$BUCKET_NAME

# Prepare our request parameters for Google Speech
cat <<< '
    "config": {
    "sample_rate": 44100,
    "language_code": "en-US",
    "speechContext": {
        "phrases": ["<My context word>"]
    "audio": {
}' > request.json

# API call to Google Speech
curl -s -X POST -H "Content-Type: application/json" --data-binary @request.json \

# Cleaning
rm -f recording.flac

If you are interested in learning more about AI and machine learning, there is a great video from Andrew Ng which covers the state of AI today and what you can do to be the next AI company!

Google Cloud Architect Professional certified!

Taking the GCP Architect exam is quite a challenge as there is very little study material or practice questions available at the time of writing this post.

Exam preparation

To sum up the exam without saying too much, it was 50 questions for a total of 120 minutes. Timing is friendly, I had about 15-20 minutes left before the end. Half of the exam worked pretty easily by proceeding by elimination to remove the craziest answers. I was surprise to see a split screen with questions on the left and a listbox on the right allowing to switch between the 4 use cases available at the moment.

About 15 questions were related to use cases. They seemed more complex, even confusing sometimes. I had to use only 2 use cases out of 4, the rest of the questions is more general and seemed to be what I would categorize as medium level questions.

Important points to review

  • Prepare yourself with the 4 use cases available, work on them for an hour as if they were your customer and how you would deal with each point (means which service you would use on GCP instead of what they have)
  • Read about BQ, Bigtable, CloudStorage, Pub/Sub, Dataflow, Dataproc and when to use all of them
  • Container engine vs Compute Engine vs App Engine
  • Know cloud related business terms: capex, opex, tco, capacity planning
  • Best practices regarding IAM, audit logs and how to secure them
  • Know resources that are global vs regional vs zonal (some major differences with AWS)
  • Know how are structured the different databases
  • Learn everything about instance groups, load balancers, stress tests
  • CI/CD on GCP, how to architect perfectly dev/qa/stg/prod environments
  • You will have to look at Java and Python code as expected
  • Cloud deployment manager is part of the exam and interesting to know in details
  • Migration: how do you deal with existing DC, move data around, etc
  • Network: VPN, firewall, tags

Once again, good luck to everyone taking this exam!