![]() Maybe you can create this query manually next time instead of going through three to four steps in the console.DeltaTable = DeltaTable.forPath(spark, target_s3_path)įullHistoryDF = deltaTable.history() # get the full history of the table This query is displayed here only for your reference. You don’t have to run this query, as the table is already created and is listed in the left pane. Here, you’ll get the CREATE TABLE query with the query used to create the table we just configured. So ignore this step, and confirm the rest of the configuration. Since our data is pretty small, and also because it is kind of out of the scope of this particular post, we’ll skip this step for now. This step is a bit advanced, which deals with partitions. Column definitions are delimited using a comma. ![]() You specify the name of the column, followed by a space, followed by the type of data in that column. For example, the bulk configuration for our example looks like this: _id string, string1 string, string2 string, double1 double, double2 doubleĪs you can see, the format is pretty simple. You’ll find the option for that at the bottom of the page. In case your data set has too many columns, and it becomes tedious to configure each of them individually, you can add columns in bulk as well. So make sure you configure the columns properly. Any field or column which is not defined here, or has a typo in the name, i.e., misconfigured, will be ignored and replaced with empty values. This is required so that Athena knows the schema of the data we’re working with. In this third step, we define the “columns” or the fields in each document / record in our data set. Every query is run against the original data set. Let’s also note here that Athena does not copy over any data from these source files to another location, memory or storage. We’ll ignore the encryption option in this post. Since we only have one file, our data will be limited to that. So all the files in that folder with the matching file format will be used as the data source. Note that you can’t provide the file path, you can only provide the folder path. Next, you have to provide the path of the folder in S3 where you have the file stored. For this example, I’ve named the table sampleData, just to keep it same as the CSV file I’m using. If not, you have the option of creating a database right from this screen. If you already have a database, you can select it from the drop down, like what I’ve done. For this post, we’ll stick with the basics and select the “Create table from S3 bucket data” option.Īs you can see from the screen above, in this step, we define the database, the table name, and the S3 folder from where the data for this table will be sourced. As you can see from the screenshot, you have multiple options to create a table. Mine looks something similar to the screenshot below, because I already have a few tables. You’ll get an option to create a table on the Athena home page. For this post, we’ll stick with the basics and select the “Create table from S3 bucket data” option.So, now that you have the file in S3, open up Amazon Athena. So, now that you have the file in S3, open up Amazon Athena. But you can use any existing bucket as well. I suggest creating a new bucket so that you can use that bucket exclusively for trying out Athena. Once you have the file downloaded, create a new bucket in AWS S3. But for this, we first need that sample CSV file. In this post, we’ll see how we can setup a table in Athena using a sample data set stored in S3 as a.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |