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Visualizing insights with Data Studio

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Google Cloud Platform Big Data and Machine Learning Fundamentals

4.6 (7,205 ratings) | 76K Students Enrolled

Course 1 of 6 in the Data Engineering with GCP Specialization

This 2-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • Google services are currently unavailable in China.

Skills You'll Learn

Tensorflow, Bigquery, Google Cloud Platform, Cloud Computing

Reviews

4.6 (7,205 ratings)

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1 star
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DK

Apr 08, 2019

This course provides the basics of Google Cloud Platform and makes us understand Google Products and services on Cloud. I would love to explore GCP more and will opt for the remaining GCP courses.

AR

May 27, 2019

This course gives you a pretty good fundamental idea on what you are diving into. Google cloud platform has bucket of data engineering features. This course sets you on the starting line for them.

From the lesson

Create Streaming Data Pipelines with Cloud Pub/sub and Cloud Dataflow

In this module you will engineer and build an auto-scaling streaming data pipeline to ingest, process, and visualize data on a dashboard. Before you build your pipeline you'll learn the foundations of message-oriented architecture and pitfalls to avoid when designing and implementing modern data pipelines.

Visualizing insights with Data Studio3:23

Creating charts with Data Studio2:42

Demo: Data Studio walkthrough7:09

Taught By

Google Cloud Training

Transcript

All data sitting in BigQuery, is great if you're an analyst who's exploring for insights with SQL. But it's often not enough for your stakeholders. They want to see and interact with your data in a more intuitive and visual way. Data visualization is a powerful tool to let you explore your data also before presenting your results. Speaking of exploration, a new and popular way to explore your datasets is available right within BigQuery. After you execute a query, you could explore in Data Studio to immediately start creating visuals or explore as part of building a dashboard. Now, here's an example of an actual Data Studio report. They don't have to understand the numbers. But just on the surface, Now what do you think this report is trying to do? In this case, it was the ad performance for the 2016 Olympic games that were broadcasted on NBC. The role of your report should be to highlight the key insights that your audience cares about. Don't show them everything, focus and filter their attention to just what those data points or those insights that they need to be seeing right then. Telling a good story with your data on your dashboard is critical because your users are likely not going to care about that amazing pipeline you just built, if the output of the dashboard is ugly and hard to use. Take it from experience. Data Studio comes with pre-built templates like this one that monitors your GCP Billing in a dashboard. You can even connect this one to your own GCP account to monitor your own spending and do some fun analysis like track incoming BigQuery jobs and the amount of data that those queries are processing. Here is a quick walk-through of the Data Studio UI. Now keep in mind that the team is continuously updating the product, so refer to their documentation as a link for those best examples in additional practices. The Data Studio homepage shows the dashboards and the data sources that you have access to. It's an important distinction between the two. Connected data sources can feed into dashboards but just because somebody has access to your dashboard, doesn't mean they automatically are granted permission to view the underlying data that's presented, because that could be controlled in BigQuery or elsewhere in your GCP project. Anyway, there are two ways to create a new report from scratch: from the templates panel up at the top, or from the button in the lower right-hand corner. The first thing you need to do is tell Data Studio where your data is coming from. That again is known as the data source. A Data Studio report can have any number of data sources but we'll just start with one. The data source picker shows all the data sources that you have access to. Note that you can have any or all of these data sources in a single Data Studio report. Since Data Studio reports and datasets can be shared, you should be aware of the ramifications of adding a new data source. When you add a data source to a report, other people who can view the report, can potentially see all the data in that data source if you share that data source with them. Anyone who can edit the report can also use all the fields from any added data sources to create new charts with them. You can click "Add to Report" to connect the data source and then you're ready to start visualizing.

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