Fix grammar in /docs/dlt-ecosystem/destinations/ (#1070)

docs/website/docs/dlt-ecosystem/destinations/athena.md

@@ -6,7 +6,7 @@ keywords: [aws, athena, glue catalog]
 
 # AWS Athena / Glue Catalog
 
-The athena destination stores data as parquet files in s3 buckets and creates [external tables in aws athena](https://docs.aws.amazon.com/athena/latest/ug/creating-tables.html). You can then query those tables with athena sql commands which will then scan the whole folder of parquet files and return the results. This destination works very similar to other sql based destinations, with the exception of the merge write disposition not being supported at this time. dlt metadata will be stored in the same bucket as the parquet files, but as iceberg tables. Athena additionally supports writing individual data tables as iceberg tables, so the may be manipulated later, a common use-case would be to strip gdpr data from them.
+The Athena destination stores data as Parquet files in S3 buckets and creates [external tables in AWS Athena](https://docs.aws.amazon.com/athena/latest/ug/creating-tables.html). You can then query those tables with Athena SQL commands, which will scan the entire folder of Parquet files and return the results. This destination works very similarly to other SQL-based destinations, with the exception that the merge write disposition is not supported at this time. The `dlt` metadata will be stored in the same bucket as the Parquet files, but as iceberg tables. Athena also supports writing individual data tables as Iceberg tables, so they may be manipulated later. A common use case would be to strip GDPR data from them.
 
 ## Install dlt with Athena
 **To install the DLT library with Athena dependencies:**
@@ -17,35 +17,34 @@ pip install dlt[athena]
 ## Setup Guide
 ### 1. Initialize the dlt project
 
-Let's start by initializing a new dlt project as follows:
+Let's start by initializing a new `dlt` project as follows:
    ```bash
    dlt init chess athena
    ```
-   > 💡 This command will initialise your pipeline with chess as the source and aws athena as the destination using the filesystem staging destination
+   > 💡 This command will initialize your pipeline with chess as the source and AWS Athena as the destination using the filesystem staging destination.
 
 
-### 2. Setup bucket storage and athena credentials
+### 2. Setup bucket storage and Athena credentials
 
-First install dependencies by running:
+First, install dependencies by running:
 ```
 pip install -r requirements.txt
 ```
-or with `pip install dlt[athena]` which will install `s3fs`, `pyarrow`, `pyathena` and `botocore` packages.
+or with `pip install dlt[athena]`, which will install `s3fs`, `pyarrow`, `pyathena`, and `botocore` packages.
 
 :::caution
 
-You may also install the dependencies independently
-try
+You may also install the dependencies independently. Try
 ```sh
 pip install dlt
 pip install s3fs
 pip install pyarrow
 pip install pyathena
 ```
-so pip does not fail on backtracking
+so pip does not fail on backtracking.
 :::
 
-To edit the `dlt` credentials file with your secret info, open `.dlt/secrets.toml`. You will need to provide a `bucket_url` which holds the uploaded parquet files, a `query_result_bucket` which athena uses to write query results too, and credentials that have write and read access to these two buckets as well as the full athena access aws role.
+To edit the `dlt` credentials file with your secret info, open `.dlt/secrets.toml`. You will need to provide a `bucket_url`, which holds the uploaded parquet files, a `query_result_bucket`, which Athena uses to write query results to, and credentials that have write and read access to these two buckets as well as the full Athena access AWS role.
 
 The toml file looks like this:
 
@@ -63,10 +62,10 @@ query_result_bucket="s3://[results_bucket_name]" # replace with your query resul
 [destination.athena.credentials]
 aws_access_key_id="please set me up!" # same as credentials for filesystem
 aws_secret_access_key="please set me up!" # same as credentials for filesystem
-region_name="please set me up!" # set your aws region, for example "eu-central-1" for frankfurt
+region_name="please set me up!" # set your AWS region, for example "eu-central-1" for Frankfurt
 ```
 
-if you have your credentials stored in `~/.aws/credentials` just remove the **[destination.filesystem.credentials]** and **[destination.athena.credentials]** section above and `dlt` will fall back to your **default** profile in local credentials. If you want to switch the  profile, pass the profile name as follows (here: `dlt-ci-user`):
+If you have your credentials stored in `~/.aws/credentials`, just remove the **[destination.filesystem.credentials]** and **[destination.athena.credentials]** section above and `dlt` will fall back to your **default** profile in local credentials. If you want to switch the profile, pass the profile name as follows (here: `dlt-ci-user`):
 ```toml
 [destination.filesystem.credentials]
 profile_name="dlt-ci-user"
@@ -77,83 +76,78 @@ profile_name="dlt-ci-user"
 
 ## Additional Destination Configuration
 
-You can provide an athena workgroup like so:
+You can provide an Athena workgroup like so:
 ```toml
 [destination.athena]
 athena_work_group="my_workgroup"
 ```
 
 ## Write disposition
 
-`athena` destination handles the write dispositions as follows:
-- `append` - files belonging to such tables are added to dataset folder
-- `replace` - all files that belong to such tables are deleted from dataset folder and then current set of files is added.
-- `merge` - falls back to `append`
+The `athena` destination handles the write dispositions as follows:
+- `append` - files belonging to such tables are added to the dataset folder.
+- `replace` - all files that belong to such tables are deleted from the dataset folder, and then the current set of files is added.
+- `merge` - falls back to `append`.
 
 ## Data loading
 
-Data loading happens by storing parquet files in an s3 bucket and defining a schema on athena. If you query data via SQL queries on athena, the returned data is read by
-scanning your bucket and reading all relevant parquet files in there.
+Data loading happens by storing parquet files in an S3 bucket and defining a schema on Athena. If you query data via SQL queries on Athena, the returned data is read by scanning your bucket and reading all relevant parquet files in there.
 
 `dlt` internal tables are saved as Iceberg tables.
 
 ### Data types
-Athena tables store timestamps with millisecond precision and with that precision we generate parquet files. Mind that Iceberg tables have microsecond precision.
+Athena tables store timestamps with millisecond precision, and with that precision, we generate parquet files. Keep in mind that Iceberg tables have microsecond precision.
 
-Athena does not support JSON fields so JSON is stored as string.
+Athena does not support JSON fields, so JSON is stored as a string.
 
 > ❗**Athena does not support TIME columns in parquet files**. `dlt` will fail such jobs permanently. Convert `datetime.time` objects to `str` or `datetime.datetime` to load them.
 
 ### Naming Convention
-We follow our snake_case name convention. Mind the following:
-* DDL use HIVE escaping with ``````
+We follow our snake_case name convention. Keep the following in mind:
+* DDL uses HIVE escaping with ``````
 * Other queries use PRESTO and regular SQL escaping.
 
 ## Staging support
 
-Using a staging destination is mandatory when using the athena destination. If you do not set staging to `filesystem`, dlt will automatically do this for you.
+Using a staging destination is mandatory when using the Athena destination. If you do not set staging to `filesystem`, `dlt` will automatically do this for you.
 
 If you decide to change the [filename layout](./filesystem#data-loading) from the default value, keep the following in mind so that Athena can reliably build your tables:
- - You need to provide the `{table_name}` placeholder and this placeholder needs to be followed by a forward slash
- - You need to provide the `{file_id}` placeholder and it needs to be somewhere after the `{table_name}` placeholder.
- - {table_name} must be the first placeholder in the layout.
+ - You need to provide the `{table_name}` placeholder, and this placeholder needs to be followed by a forward slash.
+ - You need to provide the `{file_id}` placeholder, and it needs to be somewhere after the `{table_name}` placeholder.
+ - `{table_name}` must be the first placeholder in the layout.
 
 
 ## Additional destination options
 
-### iceberg data tables
-You can save your tables as iceberg tables to athena. This will enable you to for example delete data from them later if you need to. To switch a resouce to the iceberg table-format,
-supply the table_format argument like this:
+### Iceberg data tables
+You can save your tables as Iceberg tables to Athena. This will enable you, for example, to delete data from them later if you need to. To switch a resource to the iceberg table format, supply the table_format argument like this:
 
 ```python
 @dlt.resource(table_format="iceberg")
 def data() -> Iterable[TDataItem]:
     ...
 ```
 
-Alternatively you can set all tables to use the iceberg format with a config variable:
+Alternatively, you can set all tables to use the iceberg format with a config variable:
 
 ```toml
 [destination.athena]
 force_iceberg = "True"
 ```
 
-For every table created as an iceberg table, the athena destination will create a regular athena table in the staging dataset of both the filesystem as well as the athena glue catalog and then
-copy all data into the final iceberg table that lives with the non-iceberg tables in the same dataset on both filesystem and the glue catalog. Switching from iceberg to regular table or vice versa
-is not supported.
+For every table created as an iceberg table, the Athena destination will create a regular Athena table in the staging dataset of both the filesystem and the Athena glue catalog, and then copy all data into the final iceberg table that lives with the non-iceberg tables in the same dataset on both the filesystem and the glue catalog. Switching from iceberg to regular table or vice versa is not supported.
 
 ### dbt support
 
-Athena is supported via `dbt-athena-community`. Credentials are passed into `aws_access_key_id` and `aws_secret_access_key` of generated dbt profile. Iceberg tables are supported but you need to make sure that you materialize your models as iceberg tables if your source table is iceberg. We encountered problems with materializing
-date time columns due to different precision on iceberg (nanosecond) and regular Athena tables (millisecond).
-The Athena adapter requires that you setup **region_name** in Athena configuration below. You can also setup table catalog name to change the default: **awsdatacatalog**
+Athena is supported via `dbt-athena-community`. Credentials are passed into `aws_access_key_id` and `aws_secret_access_key` of the generated dbt profile. Iceberg tables are supported, but you need to make sure that you materialize your models as iceberg tables if your source table is iceberg. We encountered problems with materializing date time columns due to different precision on iceberg (nanosecond) and regular Athena tables (millisecond).
+The Athena adapter requires that you set up **region_name** in the Athena configuration below. You can also set up the table catalog name to change the default: **awsdatacatalog**
 ```toml
 [destination.athena]
 aws_data_catalog="awsdatacatalog"
 ```
 
 ### Syncing of `dlt` state
-- This destination fully supports [dlt state sync.](../../general-usage/state#syncing-state-with-destination). The state is saved in athena iceberg tables in your s3 bucket.
+- This destination fully supports [dlt state sync.](../../general-usage/state#syncing-state-with-destination). The state is saved in Athena iceberg tables in your S3 bucket.
 
 
 ## Supported file formats