Redshift’s column-oriented database is designed to connect to SQL-based clients and business intelligence tools, making data available to users in real time. It is also used to perform large scale database migrations. What is Amazon Redshift?Īmazon Redshift (also known as AWS Redshift) is a fully-managed petabyte-scale cloud based data warehouse product designed for large scale data set storage and analysis. With the rise of cloud computing, the need for warehousing solutions that can scale up for the increasing demands of data storage and analysis has been apparent, resulting in organizations looking for alternatives to traditional on-premise warehousing.ĪWS’s Amazon Redshift is a direct response to this demand. On traditional database warehouses, queries will start taking more time, making data difficult to manage. When an organization gains traction, the size of data that needs to be stored, monitored, and analyzed expands exponentially. In this first post, we will discuss how Amazon Redshift works and why it is the fastest growing cloud data warehouse in the market, used by over 15,000 customers around the world. To learn more about using Redshift, you can visit Amazon’s overview documentation.In this blog series, we will cover how Amazon Redshift and Sumo Logic deliver best-in-class data storage, processing, analytics, and monitoring. You can learn more about the Redshift plugin for Grafana from the project’s plugin page. For example, we can create annotations based on some weather data in our database: Learn more about the Redshift plugin It’s also possible to automatically annotate your panels based on the result of a Redshift query. ![]() There are multiple template variable types (i.e., text values, data source lists), but you can also use Redshift to write queries and use the results as variables. In order to make your dashboard dynamic, template variables are really helpful. It’s also possible to modify how the data frames are formatted, which is ideal for panel representations like time series. Check out the documentation for all the available macros that you can use. The Schema, Table, and Column dropdown menus allow you to explore your data and automatically inject them in your query using macros. When we edit a panel from our performance dashboard, we see the query editor for the plugin comes with SQL syntax and autocompletion. The default dashboard gives you insight into the performance and health of your Redshift clusters. We will use that dashboard to demonstrate the rest of the plugin features! Once you have saved your changes, you can find a curated dashboard that is set up to monitor your Redshift cluster in the Dashboards tab. If you’re using Amazon Managed Grafana, you can use the AWS data source configuration to automatically create service-managed role permissions. Here you can choose to either use Temporary Credentials or use an AWS Managed Secret. When you install the Redshift data source plugin for Grafana, you’ll be prompted to introduce some credentials in order to access your Redshift cluster. ![]() It uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes by using AWS-designed hardware and machine learning.Īnd now, with this new plugin, users can visualize their Redshift queries in Grafana, taking full advantage of the wide range of visualization options that Grafana offers and combining them with other data from multiple sources in a single Grafana dashboard. In collaboration with the AWS team, we have recently released the new Redshift data source plugin for Grafana.Īmazon Redshift is the fastest and most widely used cloud data warehouse. This blog post was co-authored by Grafana Labs Software Engineer Andres Martinez, who works on the cloud data sources team, and Robbie Rolin, a Software Development Engineer on the Amazon Managed Grafana team.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |