Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Working with tables on the Amazon Glue console

A table in the Amazon Glue Data Catalog is the metadata definition that represents the data in a data store. You create tables when you run a crawler, or you can create a table manually in the Amazon Glue console. The Tables list in the Amazon Glue console displays values of your table's metadata. You use table definitions to specify sources and targets when you create ETL (extract, transform, and load) jobs.

To get started, sign in to the Amazon Web Services Management Console and open the Amazon Glue console at https://console.amazonaws.cn/glue/. Choose the Tables tab, and use the Add tables button to create tables either with a crawler or by manually typing attributes.

Adding tables on the console

To use a crawler to add tables, choose Add tables, Add tables using a crawler. Then follow the instructions in the Add crawler wizard. When the crawler runs, tables are added to the Amazon Glue Data Catalog. For more information, see Using crawlers to populate the Data Catalog .

If you know the attributes that are required to create an Amazon Simple Storage Service (Amazon S3) table definition in your Data Catalog, you can create it with the table wizard. Choose Add tables, Add table manually, and follow the instructions in the Add table wizard.

When adding a table manually through the console, consider the following:

Table attributes

The following are some important attributes of your table:

Viewing and editing table details

To see the details of an existing table, choose the table name in the list, and then choose Action, View details.

The table details include properties of your table and its schema. This view displays the schema of the table, including column names in the order defined for the table, data types, and key columns for partitions. If a column is a complex type, you can choose View properties to display details of the structure of that field, as shown in the following example:

{
"StorageDescriptor": 
    {
      "cols": {
         "FieldSchema": [
           {
             "name": "primary-1",
             "type": "CHAR",
             "comment": ""
           },
           {
             "name": "second ",
             "type": "STRING",
             "comment": ""
           }
         ]
      },
      "location": "s3://aws-logs-111122223333-us-east-1",
      "inputFormat": "",
      "outputFormat": "org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat",
      "compressed": "false", 
      "numBuckets": "0",
      "SerDeInfo": {
           "name": "",
           "serializationLib": "org.apache.hadoop.hive.serde2.OpenCSVSerde",
           "parameters": {
               "separatorChar": "|"
            }
      },
      "bucketCols": [],
      "sortCols": [],
      "parameters": {},
      "SkewedInfo": {},
      "storedAsSubDirectories": "false"
    },
    "parameters": {
       "classification": "csv"
    }
}

For more information about the properties of a table, such as StorageDescriptor, see StorageDescriptor structure.

To change the schema of a table, choose Edit schema to add and remove columns, change column names, and change data types.

To compare different versions of a table, including its schema, choose Compare versions to see a side-by-side comparison of two versions of the schema for a table. For more information, see Compare table schema versions .

To display the files that make up an Amazon S3 partition, choose View partition. For Amazon S3 tables, the Key column displays the partition keys that are used to partition the table in the source data store. Partitioning is a way to divide a table into related parts based on the values of a key column, such as date, location, or department. For more information about partitions, search the internet for information about "hive partitioning."

Compare table schema versions

When you compare two versions of table schemas, you can compare nested row changes by expanding and collapsing nested rows, compare schemas of two versions side-by-side, and view table properties side-by-side.

To compare versions