ChatGPT using "text-davinci-003"

here is text:

Ask: I want to use AWS Quicksight
Computer: AWS QuickSight is an Amazon Web Services (AWS) analytical service that enables you to visually analyze data and quickly create visualizations for business intelligence
With QuickSight, you can easily build interactive dashboards, share insights and collaborate with colleagues, and create powerful visualizations with just a few clicks.
Ask: can I import data from raw log files
Computer: Yes, you can import data from raw log files into AWS QuickSight
You can use AWS Glue to ingest and transform data from various data sources, including raw log files, into a format that is optimized for use in QuickSight
Additionally, you can use Amazon Athena to query data stored in raw log files in S3 buckets.
Ask: I have raw log files that contain FIX4.4 messages
Computer: Yes, you can import raw log files that contain FIX4.4 messages into AWS QuickSight
To do this, you can use AWS Glue to ingest and transform the data from the raw log files into a format that is optimized for use in QuickSight
Additionally, you can use Amazon Athena to query data stored in raw log files in S3 buckets.
Ask: what file format should I use
Computer: If you are using AWS Glue to ingest and transform data from raw log files for use in QuickSight, the recommended file format is Apache Parquet
Parquet is an efficient columnar storage format for large datasets, and is ideal for querying and processing data stored in S3 buckets.
Ask: do I put all raw logs into single Parquet file?
Computer: No, it is not recommended to put all raw logs into a single Parquet file
It is better to create a separate Parquet file for each raw log file in order to ensure that the data is stored in an optimal format
Additionally, putting all of the raw logs into a single Parquet file can lead to performance issues.
Ask: does Parquet has SQL table structure?
Computer: Yes, Parquet does have a SQL table structure
It is a columnar storage format that is optimized for large datasets, and it is designed to store data in a way that allows for efficient querying and processing
The data can be organized into tables that have columns and rows, which can then be used to create SQL queries.
Ask: does it supports data parttions
Computer: Yes, Parquet supports data partitions
Data partitions are used to divide data into different groups based on certain criteria, such as date or location
This can be useful for optimizing queries and improving performance
Data partitions can be specified when creating the Parquet file, and the data can then be queried using partitions.
Ask: show me example
Computer: One example of using Parquet partitions is to separate data into different timeframes
For example, if you have data spanning multiple years, you could create a partition for each year
This would allow you to query data within a specific time period more quickly and efficiently.
Ask: I want to use year, month and day
Computer: Yes, you can use year, month, and day as data partitions in Parquet
This would allow you to easily query data within a specific time period, such as a month or a day
You can specify the partitions when creating the Parquet file, and then use the partitions to query the data.
Ask: show me link to docs
Computer: Here is a link to the AWS documentation on using Parquet partitions: Amazon Athena