Kibana: build your own dashboard
With Kibana it is possible to build custom dashboards to display our data in an appropriate way. There are different types of visualization including pie charts, bar charts and geographic maps. In this tutorial we’ll discover how to build a custom dashboard starting with data entry.
Kibana: let’s explore data
Kibana, ELK Stack’s data visualization tool, offers several methodologies to graphically represent and explore data. Thanks to some example data, you can better understand the potential of this tool. In this tutorial we will discover some of them.
What is Kibana used for?
Data visualization plays a vital role in many activities. There are several tools, both open-source and paid, that allow us to create intuitive representations of our data. Among them we can’t mention Kibana, the data visualization tool by ELK Stack. Let’s find out what are its main features and not slo those dedicated to visualization.
ELK Stack: what it is and what it is used for
ELK Stack, an acronym for Elasticsearch – LogStash – Kibana, is a valuable tool for data ingest and analysis in various application contexts. Initially born to support the textual research, today its functionalities have increased considerably. In fact, it is possible to ingest data using Beats and LogStash and/or create interactive dashboards of analysis using Kibana. Let’s discover its main features.
Google Cloud Storage: solution for data lakes
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.
Data lakes: GCP solutions
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.
BigQuery: performance optimization
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.
BigQuery: WINDOWS analytics
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.
BigQuery: GIS functions and Geo Vis
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.
BigQuery: WITH clause
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.
