spelling fixes

Author: gingerwizard

docs/en/about-us/adopters.md

@@ -132,7 +132,7 @@ The following list of companies using ClickHouse and their success stories is as
 | [DNSMonster](https://dnsmonster.dev/) | Software & Technology | DNS Monitoring | — | — | [GitHub Repository](https://github.com/mosajjal/dnsmonster) |
 | [Darwinium](https://www.darwinium.com/) |  Software & Technology |  Security and Fraud Analytics | — | — | [Blog Post, July 2022](https://clickhouse.com/blog/fast-feature-rich-and-mutable-clickhouse-powers-darwiniums-security-and-fraud-analytics-use-cases) |
 | [Dash0](https://www.dash0.com/) | APM Platform | Main product | — | — | [Careers page](https://careers.dash0.com/senior-product-engineer-backend/en) |
-| [Dashdive](https://www.dashdive.com/) | Infrastructure management | Analytics | — | — | [Hackernews, 2024](https://news.ycombinator.com/item?id=39178753) |
+| [Dashdive](https://www.dashdive.com/) | Infrastructure management | Analytics | — | — | [Hacker News, 2024](https://news.ycombinator.com/item?id=39178753) |
 | [Dassana](https://lake.dassana.io/) | Cloud data platform | Main product | - | - | [Blog Post, Jan 2023](https://clickhouse.com/blog/clickhouse-powers-dassanas-security-data-lake) [Direct reference, April 2022](https://news.ycombinator.com/item?id=31111432) |
 | [Datafold](https://www.datafold.com/) | Data Reliability Platform | — | — | — | [Job advertisement, April 2022](https://www.datafold.com/careers) |
 | [Dataliance for China Telecom](https://www.chinatelecomglobal.com/) | Telecom | Analytics | — | — | [Slides in Chinese, January 2018](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup12/telecom.pdf) |
@@ -146,7 +146,7 @@ The following list of companies using ClickHouse and their success stories is as
 | [Didi](https://web.didiglobal.com/) | Transportation & Ride Sharing | Observability | 400+ logging, 40 tracing | PBs/day / 40GB/s write throughput, 15M queries/day, 200 QPS peak | [Blog, Apr 2024](https://clickhouse.com/blog/didi-migrates-from-elasticsearch-to-clickHouse-for-a-new-generation-log-storage-system) |
 | [DigiCert](https://www.digicert.com) | Network Security | DNS Platform | — | over 35 billion events per day | [Job posting, Aug 2022](https://www.indeed.com/viewjob?t=Senior+Principal+Software+Engineer+Architect&c=DigiCert&l=Lehi,+UT&jk=403c35f96c46cf37&rtk=1g9mnof7qk7dv800) |
 | [Disney+](https://www.disneyplus.com/) | Video Streaming | Analytics | — | 395 TiB | [Meetup Video, December 2022](https://www.youtube.com/watch?v=CVVp6N8Xeoc&list=PL0Z2YDlm0b3iNDUzpY1S3L_iV4nARda_U&index=8) [Slides, December 2022](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup67/Disney%20plus%20ClickHouse.pdf) |
-| [Dittofeed](https://dittofeed.com/) | Software & Technology | Open Source Customer Engagement | — | — | [Hackernews, June 2023](https://news.ycombinator.com/item?id=36061344) |
+| [Dittofeed](https://dittofeed.com/) | Software & Technology | Open Source Customer Engagement | — | — | [Hacker News, June 2023](https://news.ycombinator.com/item?id=36061344) |
 | [Diva-e](https://www.diva-e.com) | Digital consulting | Main Product | — | — | [Slides in English, September 2019](https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup29/ClickHouse-MeetUp-Unusual-Applications-sd-2019-09-17.pdf) |
 | [Dolphin Emulator](https://dolphin-emu.org/) | Games | Analytics | — | — | [Twitter, September 2022](https://twitter.com/delroth_/status/1567300096160665601) |
 | [DoorDash](https://www.doordash.com/home) | E-commerce | Monitoring | — | — | [Meetup, December 2024](https://github.com/ClickHouse/clickhouse-presentations/blob/master/2024-meetup-san-francisco/Clickhouse%20Meetup%20Slides%20(1).pdf) |

docs/en/chdb/guides/jupysql.md

@@ -6,7 +6,7 @@ description: How to install chDB for Bun
 keywords: [chdb, jupysql]
 ---
 
-[JupySQL](https://jupysql.ploomber.io/en/latest/quick-start.html) is a Python library that lets you run SQL in Jupyter notebooks and the iPython shell.
+[JupySQL](https://jupysql.ploomber.io/en/latest/quick-start.html) is a Python library that lets you run SQL in Jupyter notebooks and the IPython shell.
 In this guide, we're going to learn how to query data using chDB and JupySQL.
 
 <div class='vimeo-container'>
@@ -22,13 +22,13 @@ python -m venv .venv
 source .venv/bin/activate
 ```
 
-And then, we'll install JupySQL, iPython, and Jupyter Lab:
+And then, we'll install JupySQL, IPython, and Jupyter Lab:
 
 ```bash
 pip install jupysql ipython jupyterlab
 ```
 
-We can use JupySQL in iPython, which we can launch by running:
+We can use JupySQL in IPython, which we can launch by running:
 
 ```bash
 ipython

docs/en/chdb/guides/query-remote-clickhouse.md

@@ -41,7 +41,7 @@ You can also use the code in a Python script or in your favorite notebook.
 ## An intro to ClickPy
 
 The remote ClickHouse server that we're going to query is [ClickPy](https://clickpy.clickhouse.com).
-ClickPy keeps track of all the downloads of PyPi packages and lets you explore the stats of packages via a UI.
+ClickPy keeps track of all the downloads of PyPI packages and lets you explore the stats of packages via a UI.
 The underlying database is available to query using the `play` user.
 
 You can learn more about ClickPy in [its GitHub repository](https://github.com/ClickHouse/clickpy).

docs/en/chdb/guides/querying-apache-arrow.md

@@ -3,7 +3,7 @@ title: How to query Apache Arrow with chDB
 sidebar_label: Querying Apache Arrow
 slug: /en/chdb/guides/apache-arrow
 description: In this guide, we'll learn how to query Apache Arrow tables with chDB
-keywords: [chdb, apache-arrow]
+keywords: [chdb, Apache Arrow]
 ---
 
 [Apache Arrow](https://arrow.apache.org/) is a standardized column-oriented memory format that's gained popularity in the data community.
@@ -25,7 +25,7 @@ Make sure you have version 2.0.2 or higher:
 pip install "chdb>=2.0.2"
 ```
 
-And now we're going to install pyarrow, pandas, and ipython:
+And now we're going to install PyArrow, pandas, and ipython:
 
 ```bash
 pip install pyarrow pandas ipython
@@ -55,7 +55,7 @@ If you want to download more files, use `aws s3 ls` to get a list of all the fil
 
 
 
-Next, we'll import the Parquet module from the pyarrow package:
+Next, we'll import the Parquet module from the `pyarrow` package:
 
 ```python
 import pyarrow.parquet as pq

docs/en/chdb/guides/querying-parquet.md

@@ -25,7 +25,7 @@ Make sure you have version 2.0.2 or higher:
 pip install "chdb>=2.0.2"
 ```
 
-And now we're going to install iPython:
+And now we're going to install IPython:
 
 ```bash
 pip install ipython

docs/en/chdb/guides/querying-s3-bucket.md

@@ -25,7 +25,7 @@ Make sure you have version 2.0.2 or higher:
 pip install "chdb>=2.0.2"
 ```
 
-And now we're going to install iPython:
+And now we're going to install IPython:
 
 ```bash
 pip install ipython

docs/en/chdb/install/python.md

@@ -207,7 +207,7 @@ chdb.query(
 
 1. You must inherit from chdb.PyReader class and implement the `read` method.
 2. The `read` method should:
-    1. return a list of lists, the first demension is the column, the second dimension is the row, the columns order should be the same as the first arg `col_names` of `read`.
+    1. return a list of lists, the first dimension is the column, the second dimension is the row, the columns order should be the same as the first arg `col_names` of `read`.
     1. return an empty list when there is no more data to read.
     1. be stateful, the cursor should be updated in the `read` method.
 3. An optional `get_schema` method can be implemented to return the schema of the table. The prototype is `def get_schema(self) -> List[Tuple[str, str]]:`, the return value is a list of tuples, each tuple contains the column name and the column type. The column type should be one of [the following](/en/sql-reference/data-types).
@@ -247,7 +247,7 @@ See also: [test_query_py.py](https://github.com/chdb-io/chdb/blob/main/tests/tes
 
 ## Limitations
 
-1. Column types supported: pandas.Series, pyarrow.array, chdb.PyReader
+1. Column types supported: `pandas.Series`, `pyarrow.array`,`chdb.PyReader`
 1. Data types supported: Int, UInt, Float, String, Date, DateTime, Decimal
 1. Python Object type will be converted to String
 1. Pandas DataFrame performance is all of the best, Arrow Table is better than PyReader

docs/en/cloud/bestpractices/asyncinserts.md

@@ -4,7 +4,7 @@ sidebar_label: Asynchronous Inserts
 title: Asynchronous Inserts (async_insert)
 ---
 
-Inserting data into ClickHouse in large batches is a best practice.  It saves compute cycles and disk I/O, and therefore it saves money.  If your usecase allows you to batch your inserts external to ClickHouse, then that is one option.  If you would like ClickHouse to create the batches, then you can use the asynchronous INSERT mode described here.
+Inserting data into ClickHouse in large batches is a best practice.  It saves compute cycles and disk I/O, and therefore it saves money.  If your use case allows you to batch your inserts external to ClickHouse, then that is one option.  If you would like ClickHouse to create the batches, then you can use the asynchronous INSERT mode described here.
 
 Use asynchronous inserts as an alternative to both batching data on the client-side and keeping the insert rate at around one insert query per second by enabling the [async_insert](/docs/en/operations/settings/settings.md/#async_insert) setting. This causes ClickHouse to handle the batching on the server-side.
 

docs/en/cloud/bestpractices/usagelimits.md

@@ -9,7 +9,7 @@ title: Usage Limits
 Clickhouse is very fast and reliable, but any database has its limits. For example, having too many tables or databases could negatively affect performance. To avoid that, Clickhouse Cloud has guardrails for several types of items.
 
 :::tip
-If you've reached one of those limits, it may mean that you are implementing your use case in an unoptimised way. You can contact our support so we can help you refine your use case to avoid going through the limits or to increase the limits in a guided way. 
+If you've reached one of those limits, it may mean that you are implementing your use case in an unoptimized way. You can contact our support so we can help you refine your use case to avoid going through the limits or to increase the limits in a guided way. 
 :::
 
 # Tables

docs/en/cloud/changelogs/changelog-24-10.md

@@ -2,7 +2,7 @@
 slug: /en/changelogs/24.10
 title: v24.10 Changelog for Cloud
 description: Fast release changelog for v24.10
-keywords: [chaneglog, cloud]
+keywords: [changelog, cloud]
 ---
 
 Relevant changes for ClickHouse Cloud services based on the v24.10 release.
@@ -39,7 +39,7 @@ Relevant changes for ClickHouse Cloud services based on the v24.10 release.
 - Adding `system.projections` table to track available projections. [#68901](https://github.com/ClickHouse/ClickHouse/pull/68901) ([Jordi Villar](https://github.com/jrdi)).
 - Add support for `arrayUnion` function. [#68989](https://github.com/ClickHouse/ClickHouse/pull/68989) ([Peter Nguyen](https://github.com/petern48)).
 - Add new function `arrayZipUnaligned` for spark compatiablity(arrays_zip), which allowed unaligned arrays based on original `arrayZip`. ``` sql SELECT arrayZipUnaligned([1], [1, 2, 3]). [#69030](https://github.com/ClickHouse/ClickHouse/pull/69030) ([李扬](https://github.com/taiyang-li)).
-- Support aggreate function `quantileExactWeightedInterpolated`, which is a interpolated version based on quantileExactWeighted. Some people may wonder why we need a new `quantileExactWeightedInterpolated` since we already have `quantileExactInterpolatedWeighted`. The reason is the new one is more accurate than the old one. BTW, it is for spark compatiability in Apache Gluten. [#69619](https://github.com/ClickHouse/ClickHouse/pull/69619) ([李扬](https://github.com/taiyang-li)).
+- Support aggregate function `quantileExactWeightedInterpolated`, which is a interpolated version based on quantileExactWeighted. Some people may wonder why we need a new `quantileExactWeightedInterpolated` since we already have `quantileExactInterpolatedWeighted`. The reason is the new one is more accurate than the old one. BTW, it is for spark compatibility in Apache Gluten. [#69619](https://github.com/ClickHouse/ClickHouse/pull/69619) ([李扬](https://github.com/taiyang-li)).
 - Support function arrayElementOrNull. It returns null if array index is out of range or map key not found. [#69646](https://github.com/ClickHouse/ClickHouse/pull/69646) ([李扬](https://github.com/taiyang-li)).
 - Support Dynamic type in most functions by executing them on internal types inside Dynamic. [#69691](https://github.com/ClickHouse/ClickHouse/pull/69691) ([Pavel Kruglov](https://github.com/Avogar)).
 - Adds argument `scale` (default: `true`) to function `arrayAUC` which allows to skip the normalization step (issue [#69609](https://github.com/ClickHouse/ClickHouse/issues/69609)). [#69717](https://github.com/ClickHouse/ClickHouse/pull/69717) ([gabrielmcg44](https://github.com/gabrielmcg44)).