wiktor smith


A basic analytics and guide of Google Tag Manager Button Click Tracking

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What is Google Tag Manager button click tracking and why do you guys need it?
Google Tag Manager button click tracking is a button where with one click we can get into Google Analytics without any need for the code of your website.
Click on tracking here to verify before we start the Google Tag Manager button:
• If you are a beginner to Google Tag Manager then just check out this complete guide to Google Tag Manager as we are going to use some advanced steps in GTM.
• Google Analytics pageview tag should be linked with the Google Tag Manager.
• We need to check the source code as we are going to show you source code images in this blog from Google Chrome, so if you are using other browsers then steps to view source code may vary.
A short overwise of making it to the track button clicks:
• Find the GTM tracking button
• Get into the source code page
• Enable the built-in variable
• Create a tag
• Create a trigger
• Preview the container
• Check Google Analytics
• Publish the container

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A basic analytics and guide of Google Tag Manager Button Click Tracking
Ian  Robinson

Ian Robinson


Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics

Jon  Gislason

Jon Gislason


Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Fannie  Zemlak

Fannie Zemlak


Leveraging BigQuery with Google Analytics Data

Google Analytics is an amazing tool. It enables you to make the data work for you, get a broader picture, and understand your visitors better.

The problem comes when you expect more from this ‘reporting’ tool. I will tell you why you should consider leveraging BigQuery (or a similar tool) along with your Google Analytics data.

Table of Content:

A. Why Leverage BigQuery with Google Analytics Data?

B. Query Google Analytics Data with BigQuery

I. How does it work?

II. Pr-requisites

III. Let’s Query

A. Why Leverage BigQuery with Google Analytics Data?

3 Reasons Why:

1. Handling Changes over time:

Every website gets experiential changes over time. Along with this, the way you store data will also change. Hence, for an apple to apple comparison, you will need to transform the data into a common view.

2. Data Sampling

Ifyou are dependent on Google Analytics, you would know that GA samples its data often. As long as you are not molding the data enough or you are using the out of the box reports, GA will give you 100% accurate data. It’s only when you start filtering your data or increase its cardinality, Google Analytics will start sampling the data proportionally to maintain its speed.

*Sampling: During Sampling, GA returns you the metrics based on a smaller sample space instead of the whole pool of data.

#web-analytics #bigquery #google-analytics #google-tag-manager #digital-marketing

How to use Google Tag Manager and Google Analytics Without Cookies


_“Web analytics is the measurement, collection, analysis, and reporting of web data for purposes of understanding and optimizing web usage. However, Web analytics is not just a process for measuring web traffic but can be used as a tool for business and market research, and to assess and improve the effectiveness of a website.” _Source

so web analytics enables you to:

  • connect your user behavior with technical insights.
  • improve your customer experience, by understanding your users and where they might get stuck.
  • track the value of expenses through user conversions.
  • learn to know your target group and how you can reach them.

Due to these facts, more than 50 million websites/web apps around the world use analytics tools like Google Analytics. Most of these tools use cookies to track user behaviors. If you live in Europe you probably have heard of the GDPR, the regulation in EU law on data protection and privacy. Due to the GDPR, it is no longer easy to use cookies for web analytics. I am neither a lawyer nor I want to go into detail here. If you want to know more about it and be secure you have to talk to a lawyer. Google also provides a website with information about it.

But I can help you learn how to use Google Tag Manager and Google Analytics without cookies.

Google Tag Manager

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Google Tag Manager is a free tool, which allows you to manage and deploy marketing/analytics tags (snippets of code) on your website/web app. These Tags can be used to share information from one data source (e.g. your website) to another data source (e.g. Google Analytics).

The key components of Google Tag Manager are TagsTriggers, and Variables.

Tags are snippets of code, which tell Google Tag Manager what to do. Examples of Tags are Google Analytics, Google Adwords, Facebook Pixel.

Triggers are the way events are handled. They tell Google Tag Manager what to do and when to do it. Examples of Triggers are page viewwindow.loadedclicks, Javascript errors, or custom events (Javascript functions).

Variables are additional information for your Tags and Triggers to work. Examples are DOM elements, click classes, click text.

Google even has a video series on how to get started with Google Tag Manager.

#google #startup #marketing #web-development #analytics #data analytic

Marcelle  Smith

Marcelle Smith


How to Export a List of Google Tag Manager Users Via the APIs

It’s simple enough (with the right admin credentials) to get an overview of which users can access a particular account/container, as well as what level of access they have. However, there’s no way of getting a simple view of access levels for all users across all accounts & containers, which means it’s not that easy to monitor who has access to what.

Fortunately, all of the information that’s required to build this view is available via the GTM APIs. The steps below explain how to it’s possible to generate a user access table, which can then be exported in CSV format and uploaded into a tool such as BigQuery. In my example, the table is designed to show which containers each user has “publish” access to but it could be easily extended to show all access levels.

1 Design Overview

The UI element in this project is extremely basic, as the tasks that are carried out don’t require any user involvement beyond Google authentication. Consequently, it would make a lot of sense to run this in App Engine or via Cloud Triggers but for the initial build I am running a simple Node.js app locally on my machine.

The requirements for the project are very simple:

i) the user can authenticate with an account of their choice

ii) a table of GTM users with publish access is generated for all accounts that the user making the request has access to

iii) the table is exported in CSV format for use in other tools, so it can be ingested elsewhere (e.g. BigQuery)

At risk of stating the obvious, it’s important that you have access to all of the containers that you need to evaluate user permissions for, otherwise that information won’t be returned when you make the API requests.

2 API setup

The first step is to go to console.developers.google.com, at which point you can choose from an existing GCP project or create a new one. Once you have an active project, click on +Enable APIs and Services:

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Search for the Tag Manager API and click the blue Enable button, you should then see a green tick and confirmation that the API is enabled:

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As the API requests will involve an OAuth client, you also need to set up an OAuth consent screen, which is what will be presented to the user when they first authenticate. There are a couple of additional options available such as “Authorized domains” but the main requirement at this point is to provide an application name:

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With the OAuth consent screen saved, the next step is to create the OAuth client. Click on +Create Credentials in the Credentials section and select OAuth Client ID. Choose the application type (in this case Web application) and provide a name for the client:

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The next step is to specify which URI(s) the requests can be made from; in my case I’m running the application locally but if you wanted to set this up to run in App Engine, for example, then you’d put your App Engine domain here:

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The final step is to provide a redirect URI, which is where users will be redirected to after they have authenticated with Google:

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As far as authentication/API setup goes, that’s all there is to it. It’s worth clicking the “Download JSON” option and storing the credentials somewhere you can easily access because you’ll need the client_id and client_secret properties when you get to the point of building the API requests.

#google-cloud-platform #google-tag-manager #nodejs #google #apis