Callie  Maggio

Callie Maggio


How to Calculate Google Data Studio Comparative Metrics

Google Data Studio Comparison Calculation calculated fields Tutorial Course for beginners to calculate % to total, % difference, % difference from max automatically.


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How to Calculate Google Data Studio Comparative Metrics
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Sid  Schuppe

Sid Schuppe


How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Tia  Gottlieb

Tia Gottlieb


Google Data Studio in 6 steps — Beginners Guide

“Whatever your message is, data visualization can increase its reach a thousand-fold” — Randy Olson

Google Data Studio is a data visualization, or a reporting tool aimed at converting data into stories, generate insights and curate reports

Out of the many data visualization or reporting tools, Google Data Studio stands out for its FREE and astute service offerings, and while that might be enough to attract storytelling enthusiasts, it has a multitude of other characteristics to offer –

  • Ease of Use — since most users are well versed with google suite’s user interface, deciphering and navigating data studio becomes natural and intuitive
  • 200+ data connectors
  • Drag and Drop functionality
  • Customizable Charts and Visuals
  • Effortless sharing and collaboration options

Step 1 — Getting Started

To use Google data studio, you can use your existing Google account or create a new one, there is no installation of any kind required.

Next up, once you open Google Data Studio, your window would look as below.

This window provides you with some predesigned reports and templates as your guide or reference.

The Blank Report tile with a ‘+’ sign or the ‘+ Create’ option on the left pane, would direct us to a blank canvas to create our reports.

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The Data Sources tab provides a view on all the data sources used in creating the reports.

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You can click on the ‘+ Create’ option to add a data source

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Step 2: Data Preparation

You have an option to pull data from multiple platforms. Scroll down or use the search option in Google Data Studio to find the platform you wish to connect to.

To know more about the data connectors.

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Select on the platform or connector of your choice, browse the data set you wish to connect to, and hit the ‘Connect’ button.

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Once you connect to the data set, your window would be divided into

  • Field Names
  • Field Types
  • Field Default Aggregation type
  • Description (if any)

The Blue colored fields are metric based — fields that can be aggregated (sum, average, count, etc.) or indicate quantitative values.

The Green colored fields are dimensional fields — these fields are categorical in nature (Name, Country, etc.)

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#data-science #google-data-studio #data #data-visualization #data analysis

Google Data Studio for Beginners

As every writer knows, a picture is worth a thousand words; and in the field of data science, a good visualization is worth its weight in. As someone still incredibly new to this industry and skillset, I can’t speak from a place of expertise, but as a neophyte data engineer, I arrived at my new company with some pandas in my pocket and the bright city lights in my eyes. My team had been collecting a wealth of fantastic, mostly clean data…but nobody had the time or expertise to transform it into something actionable. What they had discovered in their quest for understanding was perhaps the greatest tool for fledgling data scientists: Google Data Studio.

When I first began playing with its interface, I scoffed and huffed. It wasn’t Pandas, it wasn’t Seaborn, and I’d just spent hundreds of hours learning how to use my fancy new Python skills in the work place so I was determined to do so. That said, the more I worked with it, the more I came to understand the truly powerful nature of Google Data Studio, or GDS, as the cool kids call it.

#data #data-science #data-visualization #google-data-studio