First of all, a wait step pauses the execution, then another lambda function queries the state of the query execution. For the LAMBDA data catalog type, use one of the following sets of required parameters, but not both. Lambda(Python3.6)からAthenaを実行する機会がありましたのでサンプルコードをご紹介します。 Overview. If the query fails, the manifest file also tracks files that the query intended to write. In this case, we'll need to manually define the … 2020-08-31T14:52:42.473Z e5434651-d36e-48f0-8f27-0290 Task timed out after 30.03 seconds To me this looks like the timeout of the Lambda Function is set to 30 seconds. Hence i am going the LAMBDA way to run a query on the ATHENA created table and store the result back to S3 which i can use to create visualizations in AWS quicksight. The manifest file is saved to the Athena query results location in Amazon S3. To use it you simply define a table that points to your S3 data file and fire SQL queries away! The last line in your log shows. Vertica processes the SQL query and writes the result set to the S3 bucket specified in the EXPORT command. The next step is to query the data in Athena. A lambda function starts the long running Athena query, then we enter a kind of loop. The manifest file tracks files that the query wrote to Amazon S3. Using AWS PrivateLink, the Lambda function communicates with the external Hive metastore in your VPC and receives responses to metadata requests. Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. ; Athena calls a Lambda function to scan the S3 bucket in order to determine the number of files to read for the result set. The following diagram depicts how Athena federation works by using Lambda to integrate with a federated data source. First, you need to enable Athena to recognize the data. This is pretty painless to setup in a Lambda function. In this diagram, Athena is scanning data from S3 and executing the Lambda-based connectors to read data from HBase on EMR, Dynamo DB, MySQL, RedShift, ElastiCache (Redis) and Amazon Aurora. If you followed the post Extracting and joining data from multiple data sources with Athena Federated Query when configuring your Athena federated connectors, you can select dynamo , hbase , mysql , and redis . Amazon Athena is a serverless, SQL-based query service for objects stored in S3. Athena's documentation focuses on how you can manually define the schema for your JSON files. athena-express simplifies integrating Amazon Athena with any Node.JS application - running as a standalone application or as a Lambda function. AWS does offer a service, called AWS Glue, designed to auto-discover the schema of your export, but it doesn't do this very well for Athena. As a wrapper on AWS SDK, Athena-Express bundles the following steps listed on the official AWS Documentation: Initiates a query execution; Keeps checking until the query has finished executing When you run an Athena DML or DDL query that uses the catalog name, the Athena query engine calls the Lambda function name that you associated with the catalog name. On the Lambda tab, select the Lambda functions corresponding to the Athena federated connectors that Athena federated queries use. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Diagram 2 shows Athena invoking Lambda-based connectors to connect with data sources that are on On Premises and in Cloud in the same query. Vertica parallelizes the write to S3 bucket based on the fileSizeMB parameter into as many partitions as needed for the result set.