Build a Google BigQuery report with Google Data Studio

Introduction

In the two-part SQL Shack article, Build a Google BigQuery Resource, I showed how to build a Google BigQuery resource, and then link it to an Azure SQL Server resource. This article will expand on that first part, showing how to build a BigQuery report with Google Data Studio.

Google Data Studio – an overview

With Google Data Studio, we can build reports and dashboards that visualize data. Data Studio can handle data from many different data platforms, including BigQuery. To build a Data Studio report, first, build the data source. Next, design, build, and configure the report over that data source. Finally, deploy the report.

The Google Data Studio tool

• For this article, we’ll build the BigQuery data source and the Data Studio report with the same Google account. First, create the BigQuery ZIP code data resource as explained in part one of the two-part article linked in the first paragraph above. After building the data source, open the Data Studio product and click Create, as shown in this screenshot:

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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

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

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.

Introduction

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).

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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

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.

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

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

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.

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

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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Automate Reports in Google Data Studio from Google BigQuery

About working with Google Data Studio
A couple of words about Data Studio for those who don’t know it yet:
First of all, Data Studio is convenient because it has many connectors for third-party services. They can easily connect to almost any data source. Both native connectors from Google and those developed by other companies are available, for example, for Yandex.Metrics, Yandex.Directory, Facebook, Twitter, etc. If necessary, you can create your own connector.
The service is easy to use and it’s easy to visualize data. Multiple sources can be connected to a single dashboard.
It’s easy to share reports in Data Studio with colleagues, providing them access to viewing or editing. There’s no need for a colleague to have authorization, all they have to do is open the dashboard by clicking the link.
Almost all the features of the tool are available in the free version.

#analytics #google-data-studio #reports-and-dashboards #google-big-query