Google Cloud Concepts Part II

This blog is going to be a continuation of below article so if you have not seen that please read that and come back here. https://annmaryjoseph.blogspot.com/2018/12/google-cloud-platform-concepts.html 

Here I am talking about,

Google Cloud Storage Basics

Google Cloud Storage implies virtually unlimited data storage and access globally, and it's available 24/7.

Why use Cloud Storage? Well, you could use it to, for example, serve website content, provide an archive for disaster recovery, as well as for direct downloads of large data objects.

 

Google offers a number of Cloud Storage options, and which one you use depends on the application and the workload profile.

Cloud Storage options support structured, unstructured, transactional, as well as relational data types.

Cloud Storage solutions are available for a wide array of use cases, including solutions for mobile applications, for hosting commercial software, for providing data pipelines, as well as basic backup storage.

Google Compute Engine is an Infrastructure-as-a-Service offering for provisioning flexible and self-managed VMs hosted on Google's infrastructure.

Google Compute Engine (GCE) is the Infrastructure as a Service (IaaS) component of Google Cloud Platform which is built on the global infrastructure that runs Google's search engine, Gmail, YouTube and other services. Google Compute Engine enables users to launch virtual machines (VMs) on demand.

VMs can be launched from the standard images or custom images created by users. GCE users must authenticate based on OAuth 2.0 before launching the VMs. Google Compute Engine can be accessed via the Developer Console, RESTful API or command-line interface (CLI).

Supported virtual machine operating systems include a number of Windows-based operating systems as well as many of the common Linux distributions. Compute Engine integrates with other Google Cloud Platform services like Cloud Storage, App Engine, and BigQuery.This extends the service to address more complex application requirements.

Google Compute Engine includes predefined machine configurations as well as the ability to define custom machine types that you could optimize for your organization's requirements. With Compute Engine, you can run large compute and batch jobs using preemptible VMs, which are very inexpensive and short-lived compute instances. Fixed-pricing with no contracts or commitments simplify the provisioning and shutdown of virtual machines. All data that's written to persistent disk using Compute Engine is encrypted transparently and then transmitted and stored in encrypted form. And Google Compute Engine also complies with a number of data and security certifications. Compute Engine offers local solid-state drive block storage that is always encrypted. Local solid-state drives are physically attached to the hosting server supporting very high IOPS and low latency. With Google Compute Engine instances, maintenance is transparent.

Google data centers use their live migration technology, providing proactive infrastructure maintenance. This improves reliability and security. Live virtual machines are automatically moved to nearby hosts, even while under extreme load, so that underlying host machines can undergo maintenance. This means no need to reboot your virtual machines due to host software updates, or even in the event of some types of hardware failure. Compute Engine instances are highly scalable. And global load balancing technology enables the distribution of incoming requests across pools of Compute Engine instances across multiple regions. The high performance Compute Engine virtual machines boot quickly and have persistent disk storage. In addition, Compute Engine instances use Google's private global fiber network with data centers located worldwide.

CPUs on Demand with Google Compute Engine

Google Compute Engine has predefined machine types from micro instances to instances with 96 virtual CPUs and 624 GB of memory. The tier of available configurations include Standard, High memory, and High CPU. With Compute Engine, you can create virtual machines with the configuration that's customized for your particular workloads. Again, from 1 to 64 virtual CPUs and up to 6.5 GB of memory available for each CPU.

This flexibility means that you can potentially save money, because you don't have to overcommit to a hard-sized, basically oversized, virtual machine. No, instead, you can customize to optimize your virtual CPUs and memory. By offering custom machine types and predefined machine types, this flexibility means that you can create infrastructure that is customized for your organization's particular workload requirements.

Compute Engine is very scalable and there is no commitment, meaning that you aren't locked in to whatever configuration you initially choose. So, for example, Compute Engine offers a start/stop feature that allows you to move your workload to smaller or larger custom machine type instances or to a predefined machine type

 

Cloud Storage

Google Cloud Storage offers a number of different storage and database options. Which you choose depends on the application, the data type, and the workload profile. Types of data supported include structured, unstructured, transactional, as well as relational data. So let's have a look at the different options.

First, Persistent Disk. This is fully managed block storage suitable for virtual machines and containers. So for Compute Engine and Kubernetes Engines instances. It's also recommended for snapshots for data backup. Typical workloads include disks for virtual machines, read only data sharing across multiple virtual machines, as well as quick and durable backups of virtual machines.

 

Google Cloud Storage is a scalable, fully managed, reliable, and cost efficient object and blob store. It's recommended for images, pictures, videos, objects, and blobs, as well as unstructured data. Common workloads include streaming and storage of multimedia, as well as custom data analytics pipelines, and archives, backups, and disaster recovery.

Next is Cloud Bigtable. Cloud Bigtable is a scalable, fully managed, NoSQL wide column database. And this suitable for both real time access and analytics workloads. Cloud Bigtable is indicated for low-latency read/write access, high-throughput of analytics as well as native time series support. Typical workloads include Internet of Things, streaming data, finance, adtech, which is marketing, as well as monitoring, geospatial datasets and graphs, and personalization.

 

Cloud Datastore is a fully managed NoSQL document database for web and mobile applications. It's recommended for semi-structured application data, as well as hierarchical data, and durable key-value data. Typical workloads include user profiles, product catalogs, for e-commerce, and game state for example.

 Google Cloud SQL is a fully managed MySQL, and PostgreSQL, database service. And it's built on the strength and reliability of Google's infrastructure, of course. It's recommended for web frameworks, as well as structured data, and online transaction processing workloads. Common workloads include web sites and blogs, content management systems and business intelligence applications, ERP, CRM, and e-commerce applications, as well as geospatial applications.

Google Cloud Spanner is a mission critical relational database service with strong transactional consistency at global scale. It's recommended for mission critical applications, as well as high throughput transactions, and for scale and consistency requirements. Typical work loads include adtech and financial services as well as global supply chain and retail.

 

Google BigQuery is a scalable, fully managed enterprise data warehouse with SQL and very fast response times. Consequently it's recommended for OLAP workloads, big data, examination, investigation processing, as well as reporting using business intelligence tools. Typical workloads include large data analytical reporting, data science, and advanced analyses, as well as processing big data using SQL.

And Google Drive. This is a collaborative space for storing, sharing and editing files, including Google Docs. It's recommended for interaction with docs and files by end users, collaboration, as well as file synchronization between cloud and local devices. Typical workloads include global file access via web, apps, and sync clients, coworker collaboration on documents, as well as backing up photos and other forms of media.

Google Cloud Storage options include a separate line for mobile. And, for example, we have cloud storage for Firebase, which is a mobile and web access service to could storage with serverless third-party authentication and authorization. Firebase Realtime Database is a realtime NoSQL JSON database for web and mobile applications. Firebase Hosting is production ready web and mobile content hosting for developers. And Cloud Firestore for Firebase is a NoSQL document database. Cloud Firestore for Firebase serves to simplify storing, querying, and syncing data for web apps and mobile apps, especially at global scale.

Google Shell

Google Cloud Shell is a shell environment for the Google Cloud platform. You can use it to manage projects and resources. And there's no need to install the Google Cloud SDK, it comes pre-installed. It can also be used with the gcloud command-line tool and any other utilities.

The Cloud Shell is a temporary Compute Engine VM instance. When you activate Cloud Shell, it provisions a g1-small Compute Engine instance on a per-user, per-session basis. The environment persists while the session is active. It also comes with a code editor, which is a beta version based on Orion, the open source development platform. Can also use Cloud Shell to browse file directories, view, and edit files.

Cloud Shell features include command line access. So you access the virtual machine instance in a terminal window. And it supports opening multiple shell connections to the same instance. That way you can work in one to, say, start a web server, and then work in another shell to perform some other operations. You can also use Cloud Shell to launch tutorials, open the code editor, and download files, or upload files.

It comes with 5 gigs of persistent disk storage and it's mounted as the $HOME directory on that virtual machine instance. And this is on a per-user basis across multiple projects. With respect to authorization, it's built in for access to projects and resources that are hosted on Cloud Platform because you are already logged in to Cloud Platform, it uses those credentials.

So there's no need for additional authorization. So you have access to platform resources, but the thing is, what access you have to those resources depends on the role that's been assigned to the Google Cloud platform user that you've logged in with. Cloud Shell also comes pre-installed with language support for Java, Go, Python, Node.js, PHP, Ruby, as well as .NET. Cloud Shell comes with a number of pre-installed tools. So for example, we have Linux shell interpreters and utilities like bash and sh for your shell interpreters. And it comes with the standard Debian system utilities. For text editors you're got emacs, vim and nano.

For source control, Cloud Shell supports Git and Mercurial. For Google SDKs and tools, you've got the Google App Engine SDK, the Google Cloud SDK, as well as gsutil for cloud storage. It also comes pre-installed with some build and package tools. For example, Gradle, Make, and Maven, as well as npm, nvm, and pip. Additional tools include the gRPC compiler, the MySQL client, Docker, and IPython. Cloud Shell also provides a web preview function that allows you to run web apps on the virtual machine instance and preview them from the Cloud console. The web applications must listen for HTTP requests on ports within a range of 8080 to 8084. And those ports are only available to the secure Cloud Shell proxy service, and that restricts access over HTTPS to your user account only.

To activate Google Cloud Shell, you just click on the activate Google Cloud Shell icon in the toolbar. And that opens underneath the current window, or within the current window that is.

Now, to list your config defaults, you just type gcloud config list

gcloud compute instances delete deletes one or more Google Compute Engine virtual machine instances.

Now I'll just type clear and press Enter. Now to set any defaults, you use gcloud config set project

 So now let's say that we want to set up my default compute zone. So gcloud config set compute/zone. And we'll say that I want to set it up as europe-west2

Zone names is available here https://cloud.google.com/compute/docs/regions-zones/

And you can verify it using gcloud config list

Okay, so now let's clear this by typing C-L-E-A-R and pressing Enter. Now, you can also see what components that come pre-installed. And you can also see the current Cloud SDK version you're using as well as the latest available version by typing gcloud components list.

 I am in latest version but if you like to update the gcloud components update

Using Sudo command

 

If you have update Press Y and this may take some time

Shell environment again supports the standard Debian system utilities, well that means that we can, for example, create an alias for a command.

So let's type alias c=clear. So now if I type c, that's like issuing a clear command.

That also means that you can configure variables and so on by placing those directly in the .profile file. In the tilde, or dollar sign home directory, and that's where we're at right now. So if I type pwd, this is my home directory, if I type echo $HOME, again same thing. 

So if I go tail to list the last ten lines of .profile, you'll see here that I have in my profile setup some environment variables. 

And I've also set up an alias command here. So in actuality any time that I launch the Shell I will have that alias set up on this machine, those environment variables will also be populated with the values that you see there. Okay, so that's Cloud Shell.

  

Setting Shell and Environmental Variables

Environmental variables are variables that are defined for the current shell and are inherited by any child shells or processes. Environmental variables are used to pass information into processes that are spawned from the shell.

Shell variables are variables that are contained exclusively within the shell in which they were set or defined. They are often used to keep track of ephemeral data, like the current working directory.

We can see a list of all of our environmental variables by using the env or printenv commands.

The set command can be used to see the shell variables This is usually a huge list. You probably want to pipe it into a pager program to deal with the amount of output easily:

set | less

We will begin by defining a shell variable within our current session. This is easy to accomplish; we only need to specify a name and a value. We'll adhere to the convention of keeping all caps for the variable name, and set it to a simple string.

Creating a Cloud Storage Bucket in Cloud Shell

Now we'll create a cloud storage bucket using the gsutil tool in Google Cloud Shell.

Gsutil is the cloud storage command line tool

Gsutil is a Python application that allows you to access cloud storage from the command line. You can use Gsutil to do a number of things with cloud storage buckets. And also perform object to management tasks like creating and deleting buckets, uploading, downloading and deleting objects. Listing buckets and objects moving, copying and renaming objects, as well as editing object and bucket access control lists. So before we get started, I just want to show that I've logged into my Google Cloud platform here, and I've already activated Cloud Shell.

You need python 2.7 for this so please verify. Now, this is already installed in Google Cloud Shell. But if you're using Cloud SDK on your local machine you'll need to install Python 2.7. So to see if you have gsutil tool installed, type gcloud components list.

More instructions here https://cloud.google.com/storage/docs/gsutil

Command to create bucket is

gsutil mb gs://mybucket

And you can see service exception 409 bucket my bucket already exists. See the message it displays .

And obviously, somebody else across Cloud storage has already used that. And that's the thing, the name of your bucket has to be unique across cloud storage. So this one might work, however. Let's try this one, my bucket, 1258

gsutil mb gs://mybucket1258

Next I am creating some fake images and then copying the same to new bucket I created

touch london.png

touch delhi.png

touch apple.jpeg

ls

Command to copy images to bucket I just created is do one at a time

gsutil cp london.png gs://mybucket1258

gsutil cp delhi.png gs://mybucket1258

gsutil cp apple.jpeg gs://mybucket1258

Now go to GCP - Storage -Browser - Refresh Bucket and the images are there

 Okay, now, it's always a good idea to clean up after yourself, right? You don't want to incur any charges if you don't have to. So to do so what we can do is, we can use this command, right?

Gsutil rb for remove bucket or gsutil rm -r to remove the bucket and anything underneath it. So let's copy these commands.  In order to use rb for remove bucket, it must be empty. However, this command, gsutil rm -r will remove all the contents and the bucket. 

gsutil rm gs://mybucket1258/*

gsutil rb gs://mybucket1258/

First command will delete the contents in bucket

Second Command deletes the bucket

and we'll see now that we've got no buckets. So we successfully cleaned up after ourselves.

https://cloud.google.com/storage/docs/gsutil/commands/rm

Data Analysis with GCP

Modern applications typically generate large amounts of data from many different sources. Devices or sensors can capture raw data in unprecedented volumes, and this data is perfect for analysis. This analysis can provide insight into an organization's operating environment and business.

 BigQuery is a serverless (In the context of a data warehouse, serverless means being able to simply store and query your data, without having to purchase, rent, provision or manage any infrastructure or software licensing.) highly scalable enterprise data warehouse on Google Cloud Platform. It's fully managed, so no infrastructure to manage, eliminating the need for a database administrator. So an organization can focus on analyzing the data using SQL. It enables real-time capture and analysis of data by creating a logical data warehouse. And with BigQuery, you can set up your data warehouse in minutes and start querying against huge amounts of data.

 Cloud Dataflow is a unified programming model and managed service from Google Cloud Platform using Apache Beam SDK. Apache Beam SDK supports powerful operations that resemble MapReduce operations, powerful data windowing and verifying correctness control against streaming or batch data. It's used for a wide range of data processing patterns, including against streaming and batch computations and analytics as well as extract, transform, load operations. [or ETL.] Since it's fully managed, it lets you focus on operational tasks like capacity planning, resource management, and performance optimization.

Cloud Dataproc is a fully managed and fast cloud service that is used to run Apache Spark and Hadoop clusters. You can take advantage of Apache big data ecosystem using tools, libraries, and documentation for Spark, Hive, Hadoop, and even Pig. Features include the fact that it's fully managed, and that means that [automated cluster management.] you've got a managed deployment, logging, as well as monitoring. This means an organization can focus on the data not the cluster, and clusters are scalable, speedy, and robust.

With Cloud Dataproc, clusters can also utilize Google Cloud Platform's flexible virtual machine infrastructure, including custom machine types, as well as preemptible virtual machines. So this provides for perfect sizing and scaling. Since it's on Google Cloud Platform, Cloud Dataproc integrates with several other Cloud Platform services like Cloud Storage, BigQuery, BigTable, Stackdriver Logging and Monitoring. And this results in a comprehensive powerful data platform. Versioning lets you switch versions of big data tools like Spark, Hadoop, as well as others. And you can operate clusters with multiple master nodes and set jobs to restart on failure, ensuring high availability.

Google's Cloud SQL is a fully managed database service that simplifies the creation, maintenance, management, and administration of relational databases on Cloud Platform. Cloud SQL works with MySQL or PostgreSQL databases. Cloud SQL offers fully managed MySQL Community Edition database instances in the cloud, and these are available in the US, EU, or Asia. Cloud SQL supports both first and second generation MySQL instances. And data encryption is provided on Google's internal networks, as well as for databases, temporary files, and backups. You can use the Cloud Platform Console to create and manage instances. The service also supports instance cloning.

 

Cloud SQL for MySQL also supports secure external connections using Cloud SQL Proxy or SSL. It also provides automatic failover for data replication between multiple zones. And you can import and export databases either with mysqldump or you can import and export CSV files. Cloud SQL for MySQL offers on-demand back ups and automated point-in-time recovery, as well as Stackdriver integration for logging and monitoring.

Cloud SQL for PostgreSQL is a Beta service from Google supporting PostgreSQL 9.6 database instances in the cloud, and these, again, are available in the US, EU, or Asia. It supports custom machine types and up to 416 gigs of RAM and 32 CPUs as well as up to 10 terabytes of storage, and that can also be increased if required. Similar to Cloud SQL for MySQL, you can use Cloud Platform Console to create and manage instances. And data encryption is provided on Google's internal networks, and for databases, temporary files, and backups.

Cloud SQL for PostgreSQL supports secure external connections using the Cloud SQL Proxy or SSL. You can use SQL dump to import and export databases. And Cloud SQL offers PostgreSQL client-server protocol and standard PostgreSQL connectors support. Cloud SQL for PostgreSQL offers on-demand and automated backups, as well as Stackdriver integration for logging and monitoring.

Creating a MySQL Database with Cloud SQL

 I'm logged into my Google Cloud Platform account. I'm going to click on burger menu and I'm going to scroll down. And I'll click on SQL under Storage, so here I am on the Cloud SQL page, and I'm going to click Create Instance. [

Choose a database engine - MySQL Version 5.6 or 5.7 or PostgreSQL BETA Version 9.6.] I'm going to leave MySQL selected, and I'll click Next. [In the next page, there are two types of Cloud SQL MySQL instances. There is Learn more hyperlinked text and: MySQL Second Generation (Recommended) and MySQL First Generation (Legacy)] And I'm going to click Choose Second Generation, [In the Create a MySQL Second Generation instance page, there is an Instance ID field, Root password field, or a checkbox for No password, Location: Region, and Zone dropdowns and a Create button and an option to Show configuration options.] and now we'll enter the instance information. So we'll call this annmj. [

You can leave the rest of the configuration at the defaults. But you can also click to show more configuration options. And you can drill right down into labels, maintenance schedule, database flags, authorize networks, and so on. But we'll hide those configuration options, we don't need those. And we'll click on Create. Now it will return us to the Instances page, and you'll see now that it's initializing the instance. Okay, so our instance has now initialized and started. And we can connect to it using the MySQL client in Cloud Shell.

We have two choices. We can either start or activate a Cloud Shell manually. [# you can either manually activate Google Cloud Shell by clicking # the icon in the upper right corner of the 'tool bar' ] And optionally set some default configurations. [# OPTIONAL: to set any defaults use 'getcloud config set' gcloud config set project gcde-2019-197823 gcloud config set compute/zone us-central1-a] Or we can connect to it using the Instance details page. 

To do that, click on the instance, and on the instance details page, scroll down. And we'll click Connect using Cloud Shell. [This is under the Connect to this instance tile which displays the instance IPv4 address and the Instance connection name. CloudShell displays at the bottom of the screen.]

Okay, so the connection string was populated in Cloud Shell automatically. so, we'll just press Enter to connect as root. [The message displays: Whitelisting your IP for incoming connection for 5 minutes.] And this is where we're at, we're going to enter the root password. Now you just have to wait a couple of minutes until it's done white listing the IP for this connection. And you saw that notification saying done. So it made a modification to the instance, white listing the IP for this connection, and now we enter the password. So click into Shell, and enter the password that you used. And press Enter. [When he enters the password, a message displays:

Welcome to the MariaDB monitor. Commands end with ; or /g.] So now we're at the MySQL prompt. So, first what we'll do is we'll create a database called blog. So I'll highlight this command. 

CREATE DATABASE blog;

And we can see that it was successful. Now, we'll insert some sample blog data into our new database. So first, what we have to do, so we have to specify to use blog. Then we create the table blogs and it's got a couple of columns. Blogger name, content, and an entryID, and that's the primary key,

Use blog; CREATE TABLE blogs (bloggerName VARCHAR (255), content VARCHAR (255), entryID INT NOT NULL AUTO_INCREMENT, PRIMARY KEY (entryID));

INSERT INTO blogs (bloggerName, content) values ("Leicester", "this is MY first blog");

INSERT INTO blogs (bloggerName, content) values ("London", "this is MY first blog");

INSERT INTO blogs (bloggerName, content) values ("Scotland", "this is MY first blog");

Okay, so we should have some data. So let's type select * from blogs; don't forget the semi-colon. Press Enter, and there you see is our data. 

Now to exit you just type exit, press Enter, and it's always a good idea to clean up after yourself. So what I'm going to do is I'm going to close the Cloud Shell. I'm going to go back to our Instance. Now we'll scroll up to the top of the Instance details page. Click the ellipsis, and we'll select, Delete. Now we have to type the instance name. 

Creating a PostgreSQL Database with Cloud SQL

 Okay, our PostgreSQL instance has initialize and started. And now at this point, we can connect to it using the PSQL Client in Cloud Shell.  We have a couple of options. We can either manually activate a Google Cloud Shell, and we can optionally specify some configuration settings. Or we can connect directly to it by clicking Connect using Cloud Shell from the Instance details page. This is the easiest way, so let's go ahead and do this. 

When you see your instance name and mypsqlinstance --user=postgres --quiet click enter and enter the password.

CREATE DATABASE blog;

\connect blog;  - Enter password

CREATE TABLE blogs (bloggerName VARCHAR (255), content VARCHAR (255), entryID SERIAL PRIMARY KEY) ;

\d+ table_name to find the information on columns of a table.

\d+ blogs

INSERT INTO blogs (bloggername, content) values ('Joe', 'this is my first blog');

INSERT INTO blogs (bloggername, content) values ('Wade', 'this is my first blog');

INSERT INTO blogs (bloggername, content) values ('Bill', 'this is my first blog');

select * from blogs;

Now to quit from the PSQL prompt, just press \q, press Enter.

I was new to PostgreSQL so visited below site for basics.

http://www.postgresqltutorial.com/postgresql-describe-table/

Happy New Year 2019

”We will open the book. Its pages are blank. We are going to put words on them ourselves. The book is called Opportunity and its first chapter is New Year’s Day.” – Edith Lovejoy Pierce

”It is never too late to be what you might have been.” —George Eliot

”Your success and happiness lies in you. Resolve to keep happy, and your joy and you shall form an invincible host against difficulties.” – Helen Keller

Christmassy words

There are some wonderful old wintery words that have worked their way out of our vocabulary – and we think it’s about time we brought them back into circulation. Here are a few of our favourite old-fashioned festive vocables,

1. Waissail

A spiced mulled cider, Waissail was traditionally drunk on Twelfth Night. Revellers would serenade or “waissail” the trees in the orchard, in a custom intended to ensure a fruitful harvest.

In a sentence: “Pour me a glass of waissail, I’m off to waissail at neighbours.”The same word can also be used to mean drinking large amounts of alcohol and enjoying oneself with others in a noisy, lively way, or to go from house to house at Christmas singing carols. Cider is of course helpful for all the above.

Recipe https://www.tasteofhome.com/recipes/holiday-wassail/  

2. Kissing bough

A precursor to mistletoe, the kissing bough was a popular Christmas decoration in Tudor homes. A round ball of twigs and foliage, decorated with seasonal fruit such as apples, it was often hung on walls or over doorways as a gesture of goodwill, and to welcome guests into the home. The boughs gave birth to the ritual of kissing under the mistletoe, first documented in the 16th century.

In a sentence: “Pop under the kissing bough, Janet, and let’s have a snog.”

3. Bubbly-jock

Bubbly-jock, or bublie-cock, is an old Scots word for a male Turkey. Treasure Island author and famous Scot, Robert Louis Stevenson, once wrote in a letter: “ At table I was exceedingly funny, and entertained the company with tales of geese and bubbly-jocks”.

In a sentence: “That bubbly-jock is never going to fit in the oven, we’ll have to barbeque it.”

4. Crawmassing

Not happy with your prezzies this year? Why not do a spot of crawmassing, which means to go around begging gifts at Christmas. It also means to gather up, or go through the remnants of a festive feast. No point wasting those last few sprouts.

In a sentence: “Oh dear, your Auntie Barbara is crawmassing again.”

5. Snow-broth

A 16th century word for melted snow, icy water or slush. It features in Shakespeare’s play Measure for Measure: the character Lucio describes Lord Angelo as “a man whose blood is very snow-broth”, implying that he has no sexual appetite.

In a sentence: “No chance of going sledging I’m afraid – it’s just snow-broth out there.”

6. Bummock

Having a party? Then it’s time to brew a bummock. It’s an old Scots word for a large quantity of drink, purposely prepared for a festival or special occasion – in particular Christmas. In the 18th century a bummack, or bummock, was also the name given to a Christmas party given by landlords to their tenants.

In a sentence: “Can you make it to my bummack? I’m brewing a bummock!”

7. Waits

From as early as the Middle Ages, almost every town and city in the UK had a band of musicians and singers called waits. Their duties included waking the townspeople on dark winter days by playing in the streets, and welcoming Royal visitors by performing at the town gates.

Waits were abolished in 1835, but the term became associated with any group of singers or musicians who sang and played carols around their town or village at night, over the Christmas period, whilst collecting money. In other words, carol singers.

In a sentence: “Bob, come to the door – the Christmas waits are here!”

8. Blenky

Forget a smattering of snow – what about a blenky? The long-forgotten word means “to snow sparingly” and is probably derived from blenks, an 18th century word for ashes or cinders. The light snow resembles the blenks or ashes that can fly out of a chimney and settle on the ground.

In a sentence: “It’s forecast to blenky tonight. Don’t forget your scarf.”

9. Rumball

Rumball or Rumball Night is an old 18th century name for Christmas Eve. A rumball is also a feast served up on December 24th, rather than Christmas Day itself.

In a sentence: “Remember to put mince pies out for Father Christmas on Rumball.”

Priorities

In today's era both men and women start working full time at 16 and change lots of job by the time we reach our retirement age of 65. 

This is also the decade of failure. And that's okay - it's probably the first time in your life you've had to contend with most of the things you're taking on. 

Things like supporting yourself, managing your time/stress/relationships, figuring out what role you want work/study/spirituality/etc. to play in your life. 

 As you get a bit older, and you start to meet people in their 30s, 40s, and 50s who kept up that lifestyle, and believe me, you don't want to be one of them. Have your fun, but remember: they call it excess for a reason. 

Eat well (whatever that means to you), get exercise, get enough sleep, and try to have a steady life if you possibly can.

You will fail in life, over and over and over. It won't feel fair. Maybe for decades. You've got to keep moving forward. Keep going. Keep going!

Make a list of all the macro “areas” in your life – This might include: Work, Home, School, Social, Church, Self, Community, Sleep (don’t forget that one!) etc

Take each item on the list and break it down into “projects”. I’ll use Home as an example:
Home:  Spouse, Kids, Yard, Groceries, Dinner, Lunches, Laundry, etc

If possible, break it down even further.
    Kids: Homework, School project, T-ball, Ballet
    Yard: Mow lawn, Weed beds, Prune shrubs, plant garden

Look at each project independently and answer this question – How much time a week do I need/want to spend on this?

It is going to take some time to drill down each macro category. I suggest my clients use index cards. That way we can spread them out like a flow chart. The thing that is most surprising to everyone I do this project with is how much the total time ends up being. I can guarantee you will have more hours than there are in a week.

Now you have a basic understanding of what is going on in your life and how much time it takes, Where are your priorities? How many of them are there? How many hours of your week are dedicated to them? What about the other stuff? Is it okay to let it fall by the wayside?

So you tell me – How many priorities are too many?
Finally, if you could only do one thing – Which card would you pick up? As always, I wish you the MOST from your potential,

I think few priority's will be make your life and others easy, 

To your boss: 
Your number one priority is to make your boss look good. I am not joking.

To your company: 
Your Number One priority is to increase revenue. 
Number Two is to improve profitability. 
These two priorities should guide all your thinking and actions.

To yourself: 
Your number one priority is to always, without exception, maintain a positive attitude, positive self confidence and the appearance of success.

The third item is for your career and your life and no single thing you can do, no amount of education, no amount of expertise or knowledge is more important.

 An employee with average skills and a good attitude is worth more than 5 dour experts.

 As the pilots say, 'your attitude determines your altitude' and you must maintain a positive attitude at ALL COSTS and you WILL be successful, as day follows the night. You cannot fail.

That ugly person you may think you are inside is actually going to be the most compelling, most interesting, and most powerful manifestation of yourself.

Oh, and don't forget to have fun along the way! Enjoy every moment you possibly can.

Creating Success Out Of Chaos 

https://youtu.be/k7HKJn7NDZs 

Thanks 

Ann

Google Cloud Concepts Part I

The Google Cloud Platform provides a comprehensive big data solution in a single platform.

The Google Cloud Platform is a full service platform, and it's set up so that you can utilize not only cloud native services, but also open-source tools. It also supports both batch and stream data processing modes.

Google Cloud Platform resources consist of physical resources, like computers and hard disk drives, as well as virtual resources, for example virtual machines. It's global in scope, with resources located around the world in Google data centers. And global distribution has a number of positive implications, including redundancy in the event of failure. The vast reach of the global data centers offered by Google Cloud Platform mean that you can pretty much deploy whatever number of resources you need to without worry. It also means reduced latency, since you can locate your services at a data center close to your end users.

Resources reside in regions or zones. A region is a particular geographical location where resources run. Each region contains one or more zones. For example, the us-central1 region specifies a region in central US that has zones in us-central1-a, us-central1-b, -c, and -f. Resources that reside in a zone, for example resources like virtual machine instances or persistent disks, are called zonal resources. Other resources, like static external IP addresses, are regional. Regional resources can be consumed by any resources within that region. And that includes any zone within that region, while zonal resources are only used by other resources within the same zone.

 

Google Cloud Platform resources are hosted across multiple locations globally. Placing resources in different zones within a region provides isolation from several common types of infrastructure, software, and hardware failures. Placing resources in different regions provides an even higher level of protection against failure. The bottom line is that you can design robust systems using resources that are spread across different failure domains. Compute Engine resources are either global, regional, or zonal. As an example, images themselves are global resources, while disks are zonal. Global resources are accessible by resources regardless of region or zone. So virtual machine instances from different zones can apply the same global image. The scope of a resource indicates how accessible it is to other resources. Though all resources, regardless of whether global, zonal, or regional, must be uniquely named within a project. What this means is that you can't, for example, name a virtual machine instance demo instance in one zone and then try to name another VM within the same project with that same name.

Google Cloud Platform Services

Google Cloud Platform provides a huge number of services.

Some of the more common services include computing and hosting, storage, networking as well as big data.Let's look at computing and hosting first. First, managed application platform. This is offered as Google App Engine and it's a platform as a service offering. It's a somewhat hands off approach in which you allow Google to manage hosting, scaling, and monitoring services. Well, for example, if traffic to your e-commerce website is a dramatic upturn, Google will automatically scale the system for you.

 And container based computing. This is focused on application code rather than deployment and hosting. Google Kubernetes Engine is referred to as containers as a service, and is very mature, and one of the most powerful container orchestration platform. Virtual machines are offered as a service called Google Compute Engine, and this is considered to be a type of infrastructure as a service.

With this type of service, you are responsible for configuration, administration, and monitoring tasks. In other words, Google will make sure that reliable resources are always available and up to date, but it's on you to manage and provision them.

 Now, let's look at storage services. Cloud SQL, which is a database service based on Structured Query Language or SQL, and it offers either MySQL or PostgreSQL databases. Google Cloud Platform offers two types of NoSQL data storage. One is Cloud Datastore and Cloud Bigtable is the other.

Cloud Spanner is a fully managed, highly available, relational database service for mission critical application. Cloud Storage offers large capacity, consistent and scalable data storage. And Compute Engine offers persistent disks. And this is available as the primary storage for your instances with both standard persistent disks, as well as solid state drives.

Now, let's look at networking services. Compute Engine provides networking services for virtual machine instances to use. You can load balance traffic across multiple instances. And there's Cloud DNS as well, which allows you to create and manage domain name system records. And Google Cloud Interconnect is an advanced connectivity service which allows you to connect your existing network to Google Cloud Platform networking resources.

And finally, big data services. First, BigQuery, this is a data analysis service. The data analysis services include custom schema creation, so you can organize your data as you wish. For example, you may have a schema structure in mind using specific datasets and tables.It offers the convenience of large dataset querying using SQL-like commands, so the learning curve is more manageable. It provides for loading, querying, and other operations via jobs. And supports managing and protecting data with controllable and manageable permission.

Cloud Dataflow is a managed service that includes software development kits or SDKs for batch and streaming data processing modes. And Cloud Dataflow is also applicable for extract, transform, load, or ETL operation. Then there's Cloud Pub/Sub. This is an asynchronous messaging service. It allows an application to send messages as JSON structures. And on the receiving end is a publishing unit referred to as a topic. These topics are global resources and what means is that other applications and projects owned by your organization can subscribe to the topic. Thereby receiving those messages in the body of HTTP requests or responses.

Benefits of Google Cloud Platform

Some of the main benefits of Google Cloud Platform include ,

Let's have a closer look at these. Future-proof infrastructure includes factors like live migration. And that means you can move Google Compute Engines instances to nearby hosts, even while they are active and under high load. Google Cloud Platform offers pricing innovations like per second billing and discounts for sustained use. The platform allows you to configure a wide combination of memory and virtual CPU, helping to avoid over-provisioning when sizing hardware for a particular workload. Fast archive restore provides a high throughput for right now restoration of data. Google's load balancer is the same system that supplies load balancing to Google products like Gmail and Google Maps over a global distributive platform. It's super fast and capable of tolerating extreme bites of traffic. You can take advantage of the Google security model, built and maintained by some of the top, application, information and network security experts. This is the same infrastructure that secures Google applications like Gmail and G Suite.

Google maintains a global network footprint, boasting over 100 points of presence, banning over 30 countries. Now let's look at powerful data and analytics as a benefit. You can build distributed services or fast results on the platform. BigQuery, Cloud Datalab and Cloud Dataproc. These are the same services that Google uses. So queries that traditionally take hours or days, can now be performed in a fraction of the time. Google Cloud Platform offers powerful applications and tools for working with big data, with data processing tools like Cloud Dataflow, Cloud Pub/Sub, BigQuery, and Cloud Datalab. Making it easier to use extreme volumes of data to deliver results.

Again, these are the same products that Google itself uses. And Google Cloud Machine Learning provides access to powerful deep learning system that google uses for services like Google Translate and Google Photos, as well as voice search. With respect to serverless computing, there are no upfront provisioning costs. So resources are allocated dynamically as needed. You simply bring your code and data. It's full management of servers and eliminates the repetitive tasks and potential errors that are inherent in tasks like scaling clusters and applying security badges. With automatic scaling and dynamic provisioning of resources, you pay only for what you use.

Let's consider a couple of use cases for a serverless computing. Take for example, web backend. You employ Google's App Engine with the highly scalable NoSQL Cloud Datastore database for a full scale, powerful backend infrastructure. Or Internet of Things or IoT device messaging, combined the real time geo-redundent Cloud Pub/Sub messaging service, with Cloud Dataflow serverless stream and batch data processing. When considering extract transform and load or ETL, we could combine Cloud Dataflow again for stream and batch data processing, with BigQuery for serverless data warehousing. [Other examples of Serverless Use Cases are shown.

 

Now one of the other benefits, customer-friendly pricing. As pointed out earlier, you do not have to commit to a specific deployment size, so no upfront costs are involved. You pay as you go and with per second pricing, that means that you pay for services as you require them. So you don't have to maintain a mountain of hardware and have that hardware sitting there idle. And you stop paying when you stop using a service, with no termination fees. Google Cloud Platform offers, as another benefit, data center innovation. For example, high performance virtual machines for fast and consistent performance.

Google's global network provides fast networking, performance, strong redundancy and high availability. Live migration technology means that maintenance of virtual machines is transparent, never requiring down time for scheduled maintenance. Google maintains very high security compliance and standards, providing some of the most secure infrastructure on earth. And Google builds its data centers with energy efficiency top of mind. In fact, Google is the first major Internet services organisation to obtain ISO 50,000 I certification. And Google has reportedly been carbon-neutral for over a decade. Now, consider security.

Google security model is an end-to-end process. Google uses practices and controls to secure data access and when retired, hard disks that contain customer information undergo a data destruction process. With only a few exceptions, customer data stored at rest is always encrypted on Google Cloud Platform. The encryption is automatic and transparent, so no customer intervention or action is required. Google's secure global network helps to improve security of in transit data. And the combination of cloud interconnect and managed VPN, means that you can create encrypted channels from an on-prem private IP environment, to Google's network. In addition to that, Cloud Security Scanner assists app engine developers to identify common vulnerabilities.

Google Cloud Platform also allows the configuration of user permissions at the project level, for full control of who has access to what resources and at what level. Using tools like Google Cloud logging and Google Cloud monitoring, simplifies the collection and analysis of request logs, as well as the monitoring of infrastructure services availability.

Comparing GCP and Other Models

When you're talking about cloud services suppliers, there are really three main suppliers. That's Amazon Web Services, Google Cloud Platform, and Microsoft Azure. The major differences from platform to platform include pricing. And one thing that you should keep in mind when considering pricing is this. How to calculate cost? For example, the pricing for Amazon's EC2 and Azure's Virtual Machines scalable computing service can get pretty complicated. While Google's scalable computing service is perhaps a little less flexible, but the pricing, way more straightforward. Another major difference lies in how these vendors name and group the services that they offer.

  

Compute offerings

With respect to scalable computing on demand, Amazon Web Services has its Elastic Compute Cloud, or EC2. While Google Cloud Platform offers Compute Engine, and Azure has Virtual Machines and Virtual Machine Scale Set. For web and mobile apps, we have AWS Elastic Beanstalk and GCP, and I'll refer to Google Cloud Platform hereafter as GCP. GCP's App Engine, and Azure offers Webs Apps and Cloud Services. For software container management, Amazon Web Services has ECS. And ECS is Amazon's EC2 container service, while EKS is Amazon Elastic Container Service for Kubernetes. Google Cloud Platform provides the Google Kubernetes Engine. And I'm quite familiar with that having a lot of personal experience with GKE, and I can tell you it is very powerful, very flexible, and fantastic. Azure offers AKS, which is Azure Container Service, and Azure Container Instances. 

  

 

For storage offerings, we have object storage. So Amazon Web Services offers Simple Storage Service, or S3. GCP has Cloud Storage, while Azure has Blob Storage. As far as archiving, or as it's known, cold storage, is concerned, AWS offers Glacier and Data Archive. GCP has Cloud Storage Nearline. And Azure offers Backup and Archive. With respect to content delivery networks, AWS offers CloudFront. GCP offers Cloud CDN, and Azure has its Content Delivery Network.

Now let's look at analytics offerings. For big data, AWS offers EMR and Athena. While GCP offers BigQuery, Cloud Dataflow, Dataproc, Datalab, and Pub/Sub. Azure has HDInsight and Data Lake Analytics. For BI, or business intelligence, AWS offers QuickSight. GCP offers Data Studio, while Azure has Power BI. Now, you might be thinking, well, Power BI, isn't that an installable application? Yes, it is an installable application. There are applications that are installable on desktop, or on servers even. But they connect to cloud resources very readily and easily. That's why I'm including those here. With respect to machine learning, AWS has Amazon Machine Learning, or AML. Google has Cloud Machine Learning, and Azure offers Azure Machine Learning.

Now let's consider briefly locations offerings. So really you should try to choose your data center close to users, because it reduces latency and provides better user experience. I mean, that goes without saying, if you're involved in any capacity in networking, in administration, you already know that. So here's the thing, AWS has the most global coverage, but unfortunately there's no coverage in Africa at this point. Google Cloud Platform has good coverage in the US, but not so good in Europe and Asia, and there's none in South America or Africa at this time. But knowing Google, I'm pretty sure that they are in the process of planning those right now. And as far as Azure goes, really they have the second best global coverage behind AWS, but again, there is no coverage in Africa.

GCP Locations https://cloud.google.com/about/locations/#global-products

AWS Locations https://aws.amazon.com/about-aws/global-infrastructure/

Azure Locations https://azure.microsoft.com/en-gb/global-infrastructure/regions/

Login https://cloud.google.com/

Developed by: Google Platform: Google App Engine, Google Compute Engine, Google Cloud Datastore, Google Cloud Storage, Google BigQuery, Google Cloud SQL

Initial release: April 7, 2008; 10 years ago

License: Proprietary Written in: Java, C++, Python, Go, Ruby

In the beginning, we need to apply a Google Cloud Platform (GCP) account and the new client would have $300 credit to spend in the first 12 months.

Once we logged in to the GCP console, we need to create a new project in order to use the GCP services. Simply click the New Project button and type the project name. At the same time, it would assign a unique project ID for your project which we need to use to access the GCP in terminal later.

Defensive Pessimism

Growing up I heard again and again in school especially on Oct 2^nd the below words

"Keep your thoughts positive because your thoughts become your words," 

But the school didn’t teach me what to do if I have a failure in life even though I have been there Mon-Friday for 12 years of my life …

The fact is human body especially human brain is developed enough to help us with cope with loss of some one dear or a sudden illness for you or in the family.

Defensive Pessimism is a strategic approach to life that help bolsters your physical, emotional and mental health

People who tend to be anxious can benefit from this approach. Defensive pessimism is the process that allows anxious people to do good planning.

The obesity in the UK is at record levels among men, women and children, and that many conditions, like heart disease and diabetes, are linked to being overweight. "Defensive pessimism can help with eating well and exercising regularly,"

A defensive pessimist may realize that high blood pressure and diabetes runs in his or her family, and in turn exercise and eat a healthy diet to try to avoid those diseases.

Some times taking that umbrella with you is defensive pessimism.

Think about the below things and keep a journal of your answers in your diary and see each day how decision's change

1.     What is your goal?

2.     What would be the most positive outcome?

3.     What action will I take to reach this goal?

4.     What is the biggest obstacle?

5.     When and where is the obstacle most likely to occur?

6.     What can I do to prevent the obstacle?

7.     What specific thing will I do to get back to my goal when this obstacle happens?

Optimism:

(Read from bottom to top)

Pessimism:

(Read top to bottom)

Follow Your Soul