One of the most exciting aspects about Google Data Studio is its flexibility to accept data from several distinct sources. During this tutorial, we will explore the MySQL connector provided by the platform to access your MySQL database in order to create an interesting sales report, highlighting significant trends with charts and tables.
Once you become familiar with the Google Data Studio platform, the next natural step is to play with the breadth of available charts. They can be especially useful for highlighting trends and analysis of various kinds, making the final report much more useful and satisfying. In this tutorial, we will explore the main elements to customize your project.
Creating proper dashboards for data visualisation has now a crucial role to highlight interesting insights and support companies. Google Data Studio is a user-friendly tool to design attractive reports based on different database connectors. This tutorial shows the basic functionalities useful for beginners to start creating their own personal dashboards.
Google offers several solutions to implement a data lake. Of these, the most popular is Cloud Storage because of its versatility in data management and low cost. However, configuring the service requires some considerations depending on its use. Let’s discover its features and how to optimize performance and costs.
In the world of Big Data, raw data management plays a vital role. In most cases, it is not possible to load the data provided by different applications into data warehouses in order to create Machine Learning models or dashboards. Data lakes, i.e. raw data staging areas, play a key role to perform the necessary transformation pipelines. Let’s find out what solutions are offered by Google Cloud to implement a data lake.
Although BigQuery is a very good tool for querying terabytes, best practices should be adopted to improve performance. Let’s discover tricks for writing queries that execute quickly and save on execution costs. We also look at how you can optimize table storage through partitioning and clustering.
In many application scenarios, the statistics you need to extract refer to different groupings on the source data. By defining aggregation windows, you can calculate statistics within the same query. Moreover, if necessary, you can also provide different levels of data granularity through the ARRAY data type. Let’s discover these advanced features through two real-world examples.
Geographic data plays a very important role in various analyses. BigQuery includes GIS functions in addition to the SQL standard to query, manipulate and analyze this kind of information. Let’s find out how to use and visualize them using Geo Vis.
Extracting data and analyzing it is a process that requires knowledge of data sources and the ability to write complex queries. BigQuery, Google’s database, makes it easy to access terabytes of data. Query writing, however, requires method. Let’s discover the WITH clause to increase the readability of our queries.
Developing classification models for unstructured data, such as images or text, is not an easy task. In many cases, very specific development skills are required. Let’s find out how it is possible, using AutoML Vision from Google Cloud, to create an image classification model without writing a line of code but only selecting images for our model.