The world is embracing on digitization by transforming the traditional business processes into smart ones. The sales and marketing is turning online, and the business operations are also going online. Be it a manufacturing industry, retail showroom or a commercial building operations, digital transformation has become essential for smart operations. The process of digital transformation generates a lot of data and that opens up a whole new world of opportunities for IT systems to handle it.

In this blog, I’m going to talk about Internet of Things that can help in digital transformation in industries like manufacturing, farming, heavy engineering, building automation, logistics etc. IoT is a network of connected devices (called things) embedded with sensors and necessary software to exchange the data over internet. This blog is written in the context of application of IoT in building automation. I am using Google Cloud Platform to implement the backbone. Equivalent services are also provided by AWS and Azure.

The figure above shows the implementation of IoT system using GCP services. While in the first glance it may look very complicated but believe me it is very straight forward and after going through this blog you would be able understand the function and the need of each and every block shown here. What you see here is the exhaustive list of services which one can use to implement full fledged IoT system, but you can always tailor it to your needs.

Devices

These are essentially a combination of hardware sensors and a software to periodically read and send it to data ingestion pipeline. The sensors keep sensing data at regular interval and sends this telemetry data in device to cloud communication. The parameter being sensed and the sensor being used, completely depends on the domain / use case. The software piece usually handles device management, data encryption, client libraries to connect to cloud services etc. In some cases it is also responsible for multiplexing, encoding and data compression.

Cloud IoT Core

This is a service provided by GCP to ingest the telemetry data in cloud. It acts like a gateway. It contains a protocol bridge which lets the on-site devices to connect and communicate through protocols like MQTT and HTTP. Data broker module then passes these messages to Cloud Pub/Sub for further distribution. IoT Core also maintains a device registry and implements authentication mechanism so that only designated devices can send in the data. It’s a fully managed, scalable, highly available service. It automatically manages the load using the load balancer.

Cloud Pub/Sub

Cloud Pub/Sub acts like a messaging queue / buffer in the pipeline. The messages received from Cloud IoT Core will be further distributed to processing pipeline. It’s a highly available, completely serverless, auto scaling and low latency service. It buffers the data for outages or compensate for rate differences, and ensures at-least-once delivery.

Cloud Dataflow

This service from GCP allows us to process the data. A single pipeline can allow processing streaming as well as batch data. The service can be used for data transformation as well as for streaming data analytics. Its an auto-scaling service and comes with default template to transfer data from Pub/Sub to BigQuery.

BigQuery

BigQuery can be leveraged to store the telemetry data from all the devices and possibly multiple sites. Its a peta-byte scale enterprise data warehouse service with very low cost storage and provides analytics capabilities on the stored data. It allows SQL based complex queries for historical analytics. Its secured, durable and highly available service.

Cloud ML / Cloud AI Platform

Once data is in BigQuery, apart from dashboards and historical analysis, the data can also be utilized in various ML based applications, for ex. predictive equipment maintenance, project future trends, detect anomalies etc. Cloud AI Platform is the service exactly meant for developing and maintaining such AI/ML applications. It allows you to train models with data present in BigQuery without transferring it. It also supports complete ML application life cycle along with collaboration within team. Its a fully managed, scalable, distributed service with features like auto hyperparameter tuning, collaboration and templates using AI Hub.

Update Device Config

A must-have feature for any IoT system is to be able to control the device remotely. This is usually provisioned by cloud-to-device communication. There are 2 possible reasons why you would want to control the device remotely. One is scheduled control, for ex. shutdown the device on weekends or run the device at a lower frequency/capacity during night time. The other is in response to preventive and predictive maintenance. In both the cases, IoT Core helps to send the updated configuration to the devices, and then devices act accordingly. Cloud Functions can be used to provision scheduled device config updates, whereas dynamic device config update initiates from data processing layer, either through Cloud Dataflow or Cloud AI Platform.

Key Challenges

1. Security: Security is a critical concern when deploying and managing IoT devices. Security needs to be handled at all the stages of data pipeline. IoT Core provisions per-device authentication using limited duration JWT token which is signed through public/private key pair (specific to each device). Further data transfer between various services is encrypted and access is controlled through IAM. The data at rest is always encrypted on GCP. So, all in all, GCP has all the means to secure your data, we just need to configure it right way.
2. Connectivity: If the data is being ingested into cloud over a cellular network, it is very important to provision for network outages. The devices should be configured to send the previous offline data whenever network resumes. This can be sent through regular IoT Core way or batch uploading to Cloud Storage. This calls for a batch data processing pipeline to eventually process and insert into BigQuery. Dataflow allows to leverage the same pipeline for streaming as well as the batch data.
3. Dynamic Scaling: IoT applications usually start with small scale due to few clients / sites / devices and then it grows with its popularity. To cater to this it is important that we design system, which does not require any upfront investment, which is fully scalable and also handle periodic or seasonal variations. Cloud IoT Core, Cloud Pub/Sub, Dataflow and BigQuery are fully scalable services. So scaling can be easily handled with GCP.
4. Actionable Insights: BigQuery provisions for historical analytics. Analytical and monitoring dashboards can be setup using Looker, Data Studio or any third party BI tool, for business insights. The data can be analyzed further using Datalab and also ML based analytics can be implemented using Cloud AI Platform. All in all GCP provides all the services to analyze data and draw actionable insights.

Conclusion

GCP offers a suite of services to help you implement end to end IoT data ingestion pipeline comprising of ingesting, processing, storing and analyzing. The good part about the pipeline is it will inherently have features like auto-scaling, auto-healing, auto-upgrade, load balancing, high availability, pay-per-use etc. Since you are utilizing managed services, it reduces the time to implement and hence time to market.

For further reading please refer https://cloud.google.com/architecture/iot-overview

Building an IoT Solution using GCP was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

Google Professional Cloud Architect (PCA) Certification is intended for individuals who perform (or willing to perform) a cloud architect or solution architect role and have some experience in Google Cloud. Google recommends more than a year’s experience in designing and managing GCP solutions and overall 3+ years of industrial experience, before appearing for the certification. In this blog, I’m going to talk about how to prepare for the Google Professional Cloud Architect (PCA) Certification and some tips to succeed.

You might already be aware that exam contains 50 questions to be answered in 2 hours. Upon completing the exam, you would immediately get the result stating Pass or Fail. The exam console/portal does not let you know the score or even insights on correct / incorrect answers. Also it is not very clear what is the passing criteria.

Preparation

1. The syllabus is quite wide and it needs good understanding of all the major services that GCP provides. Go through suitable online course, follow a learning path, attempt practice tests and register for test only when you feel comfortable.
2. Be thorough with all case studies. Understand existing implementation mentioned in the case study, analyze the current limitations, what they want to achieve etc. You may get 20 — 30% test questions on these case studies. Another important point to note is you may not have enough time to read the case study during the test. You have to almost remember the whole case study to save time during test. The best is revise the case studies as a part of your last day revision.
3. Take practice quiz to validate your understanding or to check how ready are you for the test. No questions from practice tests are asked in final test.
4. While taking your sample test, do not just focus on right answer, pay equal attention to why other options are not suitable. If required, go back to the documentation and clear your understanding. That analysis will definitely help you to deepen your understanding of concepts.

Tips

1. Do not take it lightly: Many people take it lightly as it is an MCQs exam. But believe me, it’s not easy as it appears. The options are quite close to each other. Many a times, you might feel multiple options are suited / feasible for question / case study. Choosing one out of them is bit tricky as well as needs an analysis of pros and cons of each options. The answer needs to be chosen not just based on feasible or not, but also on based on best practice / recommended over others.
2. Hands on is must: This will deepen your understanding and expertise. Make the best use of Qwiklabs if you do not have working experience of any service. Only theoretical knowledge may not help you.
3. Think thoroughly and Choose answer: Questions are not as simple as fill in the blanks or describe in one line, kind of questions. Most of the questions are situational based, which needs you to put yourself into that situation and think thoroughly.
4. Speed up is the key: Do you think 2 hours is comfortably enough for this test? It may not be, at least I did not feel so. You need to answer 50 questions in 120 minutes, which is less than 2 and half minutes per question on an average. Usually those questions are also big (you need to scroll the screen sometimes to read the complete question). Reading big question, putting yourself into that situation and choose one answer within 2.4 minutes is tough. Be prepared for it and practice well.
5. Thinking on your feet: Imagine how many unique technical situations / cases study you would be handling in a week at your workplace. I believe it won’t be more than 10 per week for most of us. In this test, you are handling 50 unique situations in just 2 hours. Believe me, you need high dynamicity and easy context switching to succeed here.
6. Keep track of timer: Timer is continuously visible at the top of the screen. It would be a good idea to pre-calculate time you want to allocate to each question. Make checkpoints for yourself. For ex. within first half an hour, I should be done with 15 questions etc. This will allow you to stick to timelines and help you speed up if you are drifting.

Resources

1. Google recommended and designed course — https://www.coursera.org/professional-certificates/gcp-cloud-architect Do bookmark or take screenshots of important slides to refer on the last day.
2. Good collection of all resources (books, courses, training videos, articles etc.) https://github.com/sathishvj/awesome-gcp-certifications/blob/master/professional-cloud-architect.md
3. Good last day revision notes https://jayendrapatil.com/google-cloud-professional-cloud-architect-certification-learning-path/

Final words

This blog gives you few tips on how to succeed. The details are not to scare you but to make you well prepared for the test with the confidence. A lot of folks try to attempt certifications using Certification dumps — trust me it can help you crack a cert, but not when you are on the role of an architect or developer, Don’t forget there is no shortcut to success !!

How to crack Google Professional Cloud Architect Certification was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

Google Cloud Next — The flagship event for all Cloud enthusiasts and an amazing opportunity to connect with folks in this buzzing industry..This year it was a Digital event ‘Cloud Next OnAir’ spanning across 9 weeks of mammoth efforts and equally humongous content leading to some refreshing learning experience.

Every year, the GDGs (Google Developer Groups) conduct their own local #Extended event having the best from Googlers, Google Developer Experts and other Industry experts locally. This year a bunch of GDGs from India West region decided to come together to have our Cloud Next On Air #Extended version of the event, fostering a spirit of Community in a true sense.

Here are the communities that united in bringing this together ..

You can still watch these talks on YouTube, here is a list of talks that were delivered throughout the 9 weeks of Cloud Next On Air Extended.

Week 1:

Talk: Google Cloud Platform: A Tour into recent features
Speaker: Romin Irani
Link: https://www.youtube.com/watch?v=GNoWmNBrDPU
Abstract: Google Cloud Platform has seen significant momentum in the last year with key platform differentiators and a growing customer base. This talk takes you through a whirlwind tour of key GCP differentiators and some key features that have been released over the last several months.

Week 2:

Talk: Only with Apps Script
Speaker: Sourabh Choraria
Link: https://www.youtube.com/watch?v=7ccB_aHnSJo
Abstract: With most businesses adapting G Suite to collaborate and optimize their workflows, there’s an opportunity for makers to use its low-code developer platform and leverage its capabilities to build things that can only be done using the Apps Script ecosystem viz. Add-ons & Data Studio Community Connectors.

Talk: Reimagine Work with AI/ ML, Chatbots, & Connected Systems over GSuite
Speaker: Shashank Todwal
Link:https://www.youtube.com/watch?v=EFIPgkQLp98
Abstract: This talk will enable businessmen to do “EVERYTHING” on G Suite, Google’s Productivity, and Collaboration Suite. A complete Google Solution can lead to up to 2/3rd savings on Technology and up to 10X increase in productivity for organizations. In this talk, we shall focus on AI/ML facilities in G Suite, Chat Rooms, and Chatbots on Google Chat and how you can truly transform work by connecting your ERP/ CRM with G Suite and live and breath completely on Cloud.

Week 3:

Talk: Infrastructure: Google Compute Engine — What’s new ?
Speaker: Rohan Singh
Link: https://www.youtube.com/watch?v=jQEOBQgRAWI
Abstract: Google Compute Engine is an infrastructure service provided as part of the Google Cloud Platform. It is available at several Google data centers worldwide and is provided exclusively on an on-demand basis.

Talk: Infrastructure: Demystifying Containers
Speaker: Mohan Pawar & Dharmesh Vaya (Yup that’s me)
Link: https://youtu.be/jQEOBQgRAWI?t=2179
Abstract: Containers are quickly becoming an industry standard for the deployment of software applications. The business and technological advantages of containerizing workloads are driving many teams towards moving their applications to containers.

Talk: CDN Delivery on the Go
Speaker: Praveen Thirumurugan
Link: https://www.youtube.com/watch?v=eI6LqmruIuo
Abstract: As the name implies, a Content Delivery Network is primarily responsible for delivering content such as web pages, images, videos, and data to users globally, by using a network of servers distributed throughout a number of different geographic locations.

Talk: Using Skaffold for building and deploying applications to GKE
Link: https://youtu.be/eI6LqmruIuo?t=3192
Speaker: Vishal Biyani
Abstract: Skaffold handles the workflow for building, pushing, and deploying your application, allowing you to focus on what matters most. Want to know the logic behind this magic ? Watch this talk ..

Week 4:

Talk: Securing Cloud-Native Applications on GCP
Link: https://www.youtube.com/watch?v=VhLiiYj7dmM
Speaker: Akash Agarwal
Abstract: How do you make your apps even better? By building them intelligently with Security from Day 1!
Securing Cloud-Native Applications on GCP, Understanding the fundamentals of GCP Security, Building Cloud-Native Applications & Security Design

Talk: Leveraging GCP for Security in Your Next Project
Link: https://youtu.be/VhLiiYj7dmM?t=3351
Speaker: KC Ayyagari
Abstract: How do you make your apps even better? By building them intelligently with Security from Day 1! Secure your apps intelligently with Command Center, Data protection, IAM recommendation, Security Analytics.

Week 5:

Talk: Know it all about BigQuery and AutoML
Link: https://www.youtube.com/watch?v=wUUHovScvCs
Speaker: Rushabh Vasa
Abstract: BigQuery is a serverless, highly scalable, and cost-effective data warehouse designed to help you turn big data into informed business decisions.

Talk: Building scalable IoT systems with GCP
Link: https://youtu.be/wUUHovScvCs?t=3454
Speaker: Nirav Kothari
Abstract: Google Cloud IoT is a complete set of tools to connect, process, store, and analyze data both at the edge and in the cloud. The platform consists of scalable, fully-managed cloud services; an integrated software stack for edge/on-premises computing with machine learning capabilities for all your IoT needs.

Week 6:

Talk: Getting started with BigQuery
Link: https://www.youtube.com/watch?v=kM0INWKf31w
Speaker: Pradeep Bhadani
Abstract : Hands-on demo and practical use cases of BigQuery, a serverless, highly scalable, and cost-effective data warehouse designed to help you turn big data into informed business decisions.

Week 7:

Talk: Application Modernization using Anthos
Link: https://www.youtube.com/watch?v=LEb5zTCn-xY
Speaker: Kaivalya Shah
Abstract: Are you struggling to find consistent development and operations experience for hybrid and multi-cloud environments?
Here we bring “Anthos” Google Cloud’s Application Modernization platform which will help you to modernize existing applications and build cloud-native apps anywhere to promote agility and cost savings. We are going to learn Config Management, Service Mesh, and Migrate services and some solutions that can be built using Anthos.

Week 8:

Talk: ML on GCP: Design-Develop-Deploy
Link: https://www.youtube.com/watch?v=MWj0-f4OQiI
Speaker: Charmi Choksi
Abstract: The Machine Learning models developed once with limited resources has to reach multiple clients which should be capable of scaling and learning in real-time. Google’s AI Platform makes it easy to take your ML projects from ideation to production and deployment, quickly and cost-effectively. Learn about the necessary engineering skills to Design, Develop, and Deploy your first Machine Learning model by training at scale to shorten dev cycle using Google Cloud Platform and its integrated products such as ML APIs, AutoML, and Cloud ML Engine.

Week 9:

Talk: Create a chatbot using Dialogflow
Link: https://www.youtube.com/watch?v=TY6LNpN4FyE
Speaker: Gajesh Naik
Abstract: Learn how this 12 year child creates a chatbot using DialogFlow..Yes that’s a 12 year school kid building bots. You must hear this one.

Talk: AppSheet — NoCode App Development
Link: https://youtu.be/TY6LNpN4FyE?t=563
Speaker: Dharmesh Vaya
Abstract: Make apps with no-code. AppSheet’s no-code app building platform allows you to quickly build apps to collect, or connect to, data. Start building for free now. Create cross device & platform apps for your business in no time.

Whoa !! That’s all folks … If you wish you connect with the Speakers you may find their credentials on this link

Speakers: https://cloudnextextendedin.web.app/speakers

This post can’t end without appreciating an amazing bunch of volunteers across GDGs (there have been a lot more behind the scenes too and were too shy to be on this page)..You folks deserve a pat on the back, tap yourselves :)

Team: https://cloudnextextendedin.web.app/team

And ofcourse, a huge thanks to the Google Developer Relations India Team in supporting our endeavours, you folks are an amazing support system to us !!

What’s next ? Coming up DevFest India 2020 .. A power packed online event..Register now https://devfestindia.com/

Cloud Next Extended — India West Edition was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

Google Cloud Next is the most awaited annual event from Google, hosting keynotes from the top executives, a conference to share ideas and thoughts, industry insights, breakout sessions, hands-on trainings, connect with experts and networking opportunities. This year the event is organized completely online, spread across 9 weeks and made absolutely free of cost.

In this micro-blog I want to highlight some of the important announcements related to GCP made by Google.

Confidential Computing

Keeping data secured by encryption is utmost important. But just encrypting the data in transit and at rest is not enough for some of the compliances, as it still leaves room for hackers while processing the data inside the machines. Confidential VMs is the first step within confidential computing strategy to cross this major hurdle, by ensuring the data is encrypted while it’s in use and being processed. Google collaborated with AMD to develop hardware to support encrypted memory without affecting performance. These VMs will be available under N2D series. Here is the official demo of confidential VM.

BigQuery Omni

BigQuery already supports external data sources which means we can query the data even if is not stored inside big query. And this is supported for various GCP services like Bigtable, Cloud SQL, Cloud Storage and Google Drive. With announcement of BigQuery Omni, it will be able to query data stored outside the GCP services like AWS S3 and Azure services. This means that Omni extends the analytics platform to other clouds without leaving BigQuery user interface and without having to import data into BigQuery. This is very efficient way as it avoids expensive egress cost of moving data between clouds. This has been possible due to BigQuery’s strategy of decoupling the compute and storage, where storage can reside within or outside GCP.

Assured Workloads for Government

GCP has come up with important updates for US government workloads. Customers can select the right security settings and the appropriate cloud controls will be in place, making it easier to meet security compliance requirements for processing government data. Till now other cloud providers have provided separate data centres to run government workloads but they are not equipped with all the latest technologies, eventually customers end up running multiple applications or parts of application in multiple clouds, which adds to complexity and cost. Assured workloads for government provides seamless platform compatibility between government and commercial cloud and also simplifies the compliance configuration process.

Google Cloud ISV/SaaS Center of Excellence

GCP has launched a new Centre of Excellence (CoE) service for independent software vendors (ISVs) to support them in evangelism. As a part of this service, GCP will provide them dedicated resources to accelerate their cloud transformation journey for their customers. The dedicated resources will include training, hands-on design workshops and field enablement with Google’s best practices and experience of delivering services at massive scale. As a part of this service, center will help ISV to get the products certified, provide necessary support and methodologies of building scalable applications, so they have repeatable way of doing deploying software or cloud transformation.

I am sure these announcements are enough for you to get started and tryout some of these new features. It would also be interesting to see what other 8 weeks of the conference offer.

Keep watching this space for announcements in the upcoming weeks at Google Cloud Next On Air.

Top Announcements from Google Cloud Next ’20: OnAir was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

Speaker : Nirav Kothari

Building an application and evaluating where to deploy it? Join Nirav Kothari as he walks us through the various ways to deploy and choose the right one for your application!

This talk will cover the evaluation strategy on

— When to use IAAS or PAAS or FAAS

— Which GCP services to use for your application

— App Engine, Compute Engine, GKE, Cloud Functions, Cloud Run

— Some case studies

Choosing the right Compute Service on GCP was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

A 40-minute session to get you started with DialogFlow and build your very own Chatbot!

Link to Presentation: https://drive.google.com/file/d/1WvR4...

Want to create your own chatbot — Here is a tutorial for yor reference ..

https://medium.com/vaibhav-malpanis-blog/faq-bot-in-less-than-5-minutes-using-google-dialogflow-knowledge-base-10d2b8face97

Building your First Chatbot was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

Contact centers are one of the most important ways businesses respond to and meet customer needs, with AI you can transform your customer experience to an unprecedented level.

In this talk, Dharmesh will tell us about how Google’s Contact Center AI helps you improvise Customer experience using Contact Center, to drive results that matter to your customers and your business.

How AI is transforming Contact Center Experience was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

Recently, Google Developer Relations launched a fantastic approach of learning (& self-learning) — The Cloud Study Jams. Before we get into more details, let me quickly explain what are these study jams.

Cloud Study Jams are community-run study groups to help the developers learn different Cloud Technologies right from Containers, Chat-bots, Machine Learning or even advanced Data engineering. These are facilitated by community organizers (with the support of your regional Google DevRel Team) and helps the developers grow their technical skills such that they can be industry-ready. This also means that these programs are not only for student enthusiasts but also designed for working professionals looking to up-skill themselves or solve a particular challenge that they might be already facing. Read more on the official details for Cloud Study Jams.

Let me share the entire experience of this seasons’ Study Jams executed by Google Cloud Developers Community (Mumbai).

We started off by identifying Facilitators (Speakers/Trainers/Mentors) who were experts in some or the other areas of these study jams. The facilitators need not necessarily be your community team members, if you find external speakers, Googlers or even regional Google Developer Experts (Btw, I recently became a GDE for Google Cloud Platform), looking to help the community, feel free to go ahead and approach them. This phase somewhat became easy for us as most of our core community team members were kinda experts in at least one of the areas of these study jams. Meet our facilitators —

Once we finalized the facilitators, it was now time to self-learn the areas that we intended to cover as a part of these study jams; although we all had been professionally working on these areas — preparation is the key to delivering any talk/session. Facilitators get credits on Qwiklabs — the platform used to run these study jams. More on it in another blog; but you may refer a quick How To use Qwiklabs. So as facilitators, we first completed the labs we had planned to take up during the study jams. Participants during the Study Jams will get 1 month of free Qwiklabs access worth $55 and can get additional month of access upon completing quests.

Next was our planning around a schedule and venue; not too difficult but was quite a tight schedule I must say. We kept all sessions on weekends to engage maximum participation from both working professionals as well as students. A huge thanks to Xoriant Solutions for facilitating us with the Venue as their Mumbai office for the entire duration of the Study Jams.

Coming to the important one — Reaching out to your audience/participants. We used Meetup platform to launch and manage our events and some heavy round of social media activity using every possible channel.

And yes, don’t keep them hungry, order some delicious and healthy food for your participants 🍕 🍟 🍩

Here are the links to the Qwiklabs quests for Cloud study jams that we conducted —

16-Feb-2019 : Machine Learning (Beginner)

23-Feb-2019 : Machine Learning APIs (Advanced)

09-Mar-2019 : Cloud Architecture : Infrastructure Track AND Architecture Track

23-Mar-2019 : Kubernetes : K8S in Google Cloud AND K8S Solutions

30-Mar-2019 : This one is on special demand; we ended the season with a ‘Student Only’ Study jam for the Machine Learning Track and also a TensorFlow Summit viewing party. Thanks to AP Shah Institute of Technology for hosting us.

Afew experiences/lessons learnt or you may say Do’s and Don’ts while we executed this huge program :

1) You may not always want to run the Beginner and Advanced tracks as separate Study Jamsy; it can be run as one-single day study jam. We gauged this during our 1st two study jams and found out that majority of the audience grasps things quickly and are already at a stage where they can also run with advanced stuff.

2) Each Qwiklab quest has labs that are individual topics that help you understand the technology. These labs are timed, so please be careful of the delivery style while running a Qwiklab. As an instructor, what we experienced is that the below works best to deliver a session-

• First explain the concept behind the technology.
• Run through the steps given in the lab and explain the meaning of each step.
• Once your lab has ended, then you ask the participants to perform the lab by themselves and guide them wherever they need assistance.

We learnt that if the participants run the labs along-with the instructors and in case they get stuck, most of them would end up running out of time (as well as the instructor) while the instructors explain the concepts. Running it classroom-style works best !!

3) Two important things : Power ⚡️and Internet 📡…. Please ensure that you have adequate power sockets available for the participants, you don’t want participants struggling with low battery while running labs. Try and have a good Internet connection that can handle the approx no. of participants. Also do remember to test whether the Qwiklabs work fine using the WiFi connections at your venue; it may often happen that you are hosting at a Co-working space or some Corporate organization and their firewalls might block certain links required to start the labs.

4) Don’t aim at running all the labs in a given Qwiklab Quest, curate a list of relevant labs from the beginner and advanced topics and then run these during your study jam. Also it is sometimes important to choose topics that would create an interest and curiosity in the participants, create a story around the topic, use practical scenarios and examples to explain those technology components. This will help working professionals a lot as most of them would be quick to grasp the technology, but a practical scenarios using industry examples would make sense for them.

5) Participation is the key to Study Jams, encourage your audience to actively engage in conversations with facilitators. Also don’t forget to motivate the participants if they are like — Regular Attendees, Complete all Qwiklabs (There is a way to showcase your Qwiklab Badges that you have earned from your Qwiklab profile page), Helps in volunteering/facilitating other participants etc.

6) With any kind of meetups, be prepared on the fluctuating participation levels. In certain sessions you might have a huge and interactive audience and in some you might even struggle to fill in seats. Lastly, but importantly, don’t forget to collect feedback and improvise based on it. After all you are running this as a community, keep that momentum going..

Running such a long series of Study Jams can be hectic, but will be a good ride if you plan well in advance and you shall definitely sail through, it’s a Team effort. Feel free to reach out to us in case you need any help running such Study Jams or meetups 😃

Here is the awesome GCDC-Mumbai Team.. 🔥

Oh yes and we did collaborate with GDG MAD during our 1st Study Jams for a Panel discussion around Community Contributions… & we had Biswajeet and Harsh from DevRel Team visiting us.

Finally a huge round of thanks to our Google India DevRel Team — Biswajeet Mallik, Harsh Dattani, Siddhant Agarwal and GoogleDevsIndia team — Thanks for all your support, looking forward to the drive ahead 👍 😃

Running a series of Cloud Study Jams was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

How to setup Nginx as a reverse proxy for sub-domain with Odoo (OpenERP) on Google Cloud Platform ?

What is Odoo also known as OpenERP ?

Odoo is an open source ERP [Enterprise Resource Planning] platform that is freely available and anyone can customize it based on their organization/process requirements. Odoo is an abbreviation for On-Demand Open Object. It’s basically a large set of business/operations modules like CRM, Sales, Inventory, Warehouse, HRMS etc and that essentially form a basis of any ERP Software package. More about Odoo.. Read here

Odoo on Google Cloud Platform

Odoo is provided as an application by Bitnami on the GCP Marketplace.

On your GCP Console you can goto Marketplace or click on this link to open the Odoo offering.

Google Cloud Platform

As you can see, there is no usage/license fee for Odoo usage, it is only the Compute Engine costs that you need to incur.

Sub-domains with Odoo

I have often come across situations where internal divisions use different modules of the Odoo package and they would like to host it as sub-domains under their organization.

Say your company domain is openerp.com and you want the HRMS module to be available as hrms.openerp.com. Likewise if you have several such sub-domains how do you configure this using Nginx as a reverse proxy.

Reverse Proxy : A reverse proxy is an intermediary proxy service which takes a client request, passes it on to one or more servers, and subsequently delivers the server’s response to the client.

Nginx Reverse proxy has quite some benefits like Load Balancing, Increased Security, Better performance and easy audit logs.

Let us now begin with our configuration of Nginx reverse proxy for sub-domains -

SSH into your VM

Note : Use ‘sudo’ if you are not a root user and install nginx

apt-get update

apt-get install nginx

Within the /etc/nginx/sites-available directory, we shall create the configuration that is responsible for the reverse proxy information and management. Name this file as the sub-domain itself, something like hrms.openerp.com (Not mandatory, but in future if you keep on increasing sub-domains it would just become easy to remember this naming convention).

upstream hrmsopenerp {
    server 127.0.0.1:8069;
}
server {
        listen 443 default;
        server_name hrms.openerp.com;

access_log /var/log/nginx/oddo.access.log;
        error_log /var/log/nginx/oddo.error.log;

if ($scheme = http) {
                return 301 https://hrms.openerp.com$request_uri;
        }

# SSL cerificate details - Use Lets Encrypt in Live (this is testing)
        ssl on;
        ssl_certificate     /etc/nginx/ssl/nginx.crt;
        ssl_certificate_key /etc/nginx/ssl/nginx.key;

# limit ciphers
        ssl_protocols TLSv1.2 TLSv1.1 TLSv1;
        ssl_ciphers ECDHE-RSA-AES256-GCM-SHA384:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-SHA384:ECDHE-RSA-AES256-SHA:DHE-RSA-AES256-SHA;
        ssl_prefer_server_ciphers on;

proxy_buffers 16 64k;
        proxy_buffer_size 128k;
        location / {
                proxy_next_upstream error timeout invalid_header http_500 http_502  http_503 http_504;
                proxy_redirect off;
                proxy_set_header Host $host;
                proxy_set_header X-Real-IP $remote_addr;
                proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
                proxy_set_header X-Forwarded-Proto $scheme;
                proxy_pass http://hrmsopenerp;
        }
        location ~* /web/static/ {
                proxy_cache_valid 200 60m;
                proxy_buffering on;
                expires 864000;
                proxy_pass http://hrmsopenerp;
        }
        gzip_types text/css text/less text/plain text/xml application/xml application/json application/javascript;
        gzip on;
}

## http redirects to https ##
server {
    listen      80;
    server_name hrms.openerp.com;
    # Strict Transport Security
    add_header Strict-Transport-Security max-age=2592000;
    rewrite ^/.*$ https://$host$request_uri? permanent;
}

Once this file is created, you need to activate it by linking it to sites-enabled using the command -

ln -s /etc/nginx/sites-available/hrms.openerp.com /etc/nginx/sites-enabled

Now just restart your nginx and you are good to proceed.

sudo systemctl restart nginx

Test your application on any browser -

http://hrms.openerp.com/
https://hrms.openerp.com/

Note : You must have your DNS entries pointing to your server before applying these steps.

Additional Notes:

The above configuration also solves the following configuration problems generally encountered -

Redirect HTTP to HTTPS

Reverse Proxy on nginx for custom ports

Finally, in order to get your sites marked as secured whenever someone visits it, ensure that you have set the right SSL protocols and ciphers in your nginx configs and also preferably use certificates from Let’s Encrypt.

Setup Nginx as a reverse proxy for sub-domain with Odoo (OpenERP) on Google Cloud Platform was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.

An application has to talk to databases, managed services and other third party applications, for its successful operation. And every outgoing request has to be secured with some or the other way. If you have ever built an application, you would know that managing the secrets (passwords, keys and tokens) for all these services/applications is really challenging. Also as your application grows, it becomes difficult to manage them for multiple environments (dev, staging, prod etc.)

In this blog I will explain how developers usually go about managing the secrets in step by step manner. Towards the end of the blog, I will talk about the optimum way to manage your secrets.

Storing secrets in code

Developers usually start with prototyping the application and usually provide all the secrets in the source code itself like this -

# app/process.py

DB_PASSWORD = "a3d!9FS2309"
API_ACCESS_TOKEN = "jgradxasd4526d3nvtgu2zgv"

It’s the quickest and easiest approach to have the secret written in code, but it’s not a good idea mainly because of following reasons.

1. Every team member, who has the access to the repository, can see the secrets.
2. In case of changing any password, you need to commit the code, restart the application etc.
3. Difficult to manage secrets for various environments

Storing secrets in Config file

In this approach all secrets are moved out of the source code and put into config file. A separate config file is created for each environment and kept on those application servers. And these config files are not checked in to the repository. Of course you can have masked version of config file as a part of repo.

# app/config.txt

DB_PASSWORD = "a3d!9FS2309"
API_ACCESS_TOKEN = "jgradxasd4526d3nvtgu2zgv"

This approach has few pros and cons over storing secrets in source files as

1. Access to secrets reduces from all repo users to all VM users
2. Managing secrets for multiple environment is eased out
3. To change secrets, you don’t need any commits, but you still need to restart the application

Storing secrets in Environment variables

In this approach secrets are stored as environment variables. Depending on the environment, you can set the values of each environment variables. This is the concept of many configuration management tools. In simple terms, you can set the secrets using this approach as below

# app/set_env.sh

#!/bin/sh
if [ $1 = 'dev' ];
then
 export DB_PASSWORD=a3d!9FS2309
 export API_ACCESS_TOKEN='jgradxasd4526d3nvtgu2zgv'
fi

if [ $1 = 'prod' ];
then
 export DB_PASSWORD=h6@fj451q
 export API_ACCESS_TOKEN='djsiue31092fgqwedhgn1k9b'
fi

This approach is quite similar to previous approach. The additional benefit is you do not need to maintain multiple copies of config files for different environments.

Using Key Management Services

This is a very clean and secure approach of managing your secrets. Majority of the cloud providers provide Key management service. These services allow you to create, use, rotate and destroy cryptographic keys. These keys are used to encrypt the secrets and then you can store the encrypted version of these secrets in your file system or repo. Since the secrets are encrypted, humans can not make sense of the content. Only authorized user or application (service account), with appropriate permissions, can decrypt the content and use it as needed.

Permission management is handled by IAM services which provides a fine grained permission control. You can either give permission for using of keys for encryption and decryption or permit admin activities like managing key or key rotation.

The advantages of this approach are

1. You can control who or which application can access the secrets
2. Managing secrets for multiple environment is eased out
3. Flexibility of rotating the keys with version control without any downtime

Conclusion

Using the KMS gives the most secured way of managing the secrets. It’s a good approach to maintain secrets if your application is running in cloud and is sufficiently large application, otherwise managing through environment variables should suffice.

Manage your secrets securely was originally published in GDGCloudMumbai on Medium, where people are continuing the conversation by highlighting and responding to this story.