As usage grew, new challenges started to appear, both technical and organisational. Athena query fails with GENERIC_INTERNAL_ERROR: Missing variable ... Let's say we want to update the data in our Athena table. eg: INSERT INTO staging.destination_table. The parquet files happily live in a S3 bucket, and I can query the data with Athena using the name of the Glue table, like this: select * from {table}_ {latest_refresh_date} Now let's say that I get new data. Connect and share knowledge within a single location that is structured and easy to search. Let's try an alternative choice of partitioning columns. Execute the following query (we require to run the complete query along with defining a variable, its value): 1. This made it possible to use OSS Delta Lake files in S3 with Amazon Redshift Spectrum or Amazon Athena. Starting off, let's inspect our table in Athena to check how many rows there are . Athena Columns Repeated Partitioning Column In [2I7PUG] I also checked the underlying data. Add Newly Created Partitions Programmatically into AWS Athena ... - Medium Athena query fails with GENERIC_INTERNAL_ERROR: Missing variable ... The view itself is queried OK with simple queries like select * from <view_name> where <conditions> 2. Once I wrap it into bigger select like select count (*) from (select * from <view_name> where <conditions>) I get the following strange error: GENERIC_INTERNAL_ERROR: Missing variable: <column_name> where <column_name> is absolutely live and valid column that could be queried and returned as usual. Hence it can't parse data (int, string) as header (string). Partition your data. この質問は、趣旨が明確でわかりやすい・実用的である・建設的である. dataset (bool) - If True store a parquet dataset instead of a ordinary file(s) If True, enable all follow arguments: partition_cols, mode, database, table, description, parameters, columns_comments, concurrent_partitioning, catalog_versioning, projection_enabled, projection_types, projection_ranges, projection_values, projection_intervals . Athena is a service that lets you query data in S3 using SQL without having to provision servers and move data around—that is, it is "serverless". So try changing it to ("skip.header.line.count"="1"); Hope this helps. If the source data is JSON, manually recreate the table and add partitions in Athena, using the mapping function, instead of using an . Cooling down hot data: From Kafka to Athena - Medium ParseException missing EOF error message with Hive Query - Cloudera