![]() We next need to create the table to hold the weather data. This is also true on Microsoft Access and Postgres (also known as PostgreSQL) where you connect to a specific database when you log in. However, on DB/2, you typically connect log into a database directly, so you would use a command such as “connect to Weather_DB” instead. If the database in which you want to store the data has a different name, simply replace “Weather_DB” with that alternate name.) (Note that the database name “Weather_DB” matches the name that we created in this article above. On most databases, this is done with the “use” command. If you are using a command line tool, before working with a database, you typically need to set that database as the current default target for your actions. In many cases you will want to load periodic weather data updates as part of an ETL process. ![]() This is extremely useful since SQL and other scripts can be easily automated for future and periodic use. However, in this article we’ll discuss how to load the file via a scriptable command. If you are familiar with your database’s GUI tools, you can typically load a CSV file easily via their UI. Now that we have a CSV file containing weather data, we are ready to load it as a table into the database. Following these steps will get you a weather data CSV file for your exact location and date range in just a couple of minutes. If you would like to follow a step-by-step guide for downloading a CSV result set based on your own weather query, please follow the CSV tutorial or watch the matching guide video. You can then use this same query as the foundation for a recurring bulk data query, if want to retrieve updated data over time. The web-based query interface lets you specify the detailed weather query that we want to run and then allows you to instantly download the results as a CSV file. A weather data service such as Visual Crossing Weather can provide bulk weather data in various convenient formats such as CSV that be directly imported into nearly any database. Now that we have a database in which to store weather data, we need to find a source for that data. Otherwise, the universal create database command above should be fine to get you started. If you are working in an environment managed by others, then you will want to check with your local DBA to determine any appropriate, additional options. For example, in MySQL you can specify a character set and collation type, in DB/2 you can specify security settings and storage options, and in Oracle and SQL Server you can set a host of options including languages, logfiles, and date formats. In most databases there are optional parameters that can be added to tune the database or the storage associated with it. Just make sure to use the same name consistently throughout all of the steps in the article.) Feel free to choose something else that better meets your own naming convention. (Note that this example names the database “Weather_DB”. In nearly all databases this can be done simply by logging in with administrator access and executing a “create database” command like this. If you have just installed a fresh database server or want to keep your weather data entirely separate from your existing data, then you will want to create a new database instance. If you are one of those, you can safely skip this step and jump to the next header below. However, many readers may already have an existing database into which they want to add weather data. This database creation step is required here for completeness so that readers starting from scratch can follow along easily. Would you like us to add an entry for your favorite database? Email and we’ll do our best to help. To make your project easier, we have noted specific commands for most popular database platforms below. While the flow is the same for any database, the exact command syntax and SQL varies somewhat for different database platforms. In this article, we will walk through the steps of importing historical weather data into a database. For this reason, using a database as a storage and analysis platform is the best option for many projects. Weather data is typically quite large in volume, and its analysis often requires processing thousands or even millions of detailed records in order to find valuable patterns and apply them to the task at hand. Many business intelligence, data science, and academic research projects need high quality historical weather data to be available in a database.
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