Merge pull request #298 from cyyeh/feature/update-doc

refine README and docs

Author: kokokuo

README.md

@@ -23,24 +23,28 @@
   </a>
 </p>
 
-## What is VulcanSQL
+## What is VulcanSQL?
 
-**[VulcanSQL](https://vulcansql.com/) is a Data API Framework for data applications** that helps data folks create and share data APIs faster. It turns your SQL templates into data APIs. No backend skills required.
+**[VulcanSQL](https://vulcansql.com/) is an Analytical Data API Framework for data apps**. It aims to help data professionals deliver RESTful APIs from databases, data warehouses or data lakes much easier and faster. It turns your SQL queries into REST APIs in no time!
 
 ![overview of VulcanSQL](https://i.imgur.com/JvCIZQ1.png)
 
-## Online Playground
+## What Problems does VulcanSQL aim to solve?
 
-Use [Online Playground](https://codesandbox.io/p/sandbox/vulcansql-demo-wfd834) to get a taste of VulcanSQL!
+Given the vast amount of analytical data in databases, data warehouses, and data lakes, there is currently no easy method for data professionals to share data with relevant stakeholders for operational business use cases.
 
-## Examples
+## Online Playground
 
-Need Inspiration?! Discover a [selected compilation of examples](https://github.com/Canner/vulcan-sql-examples) showcasing the use of VulcanSQL!
+Use [Online Playground](https://codesandbox.io/p/sandbox/vulcansql-demo-wfd834) to get a taste of VulcanSQL!
 
 ## Installation
 
 Please visit [the installation guide](https://vulcansql.com/docs/get-started/installation).
 
+## Examples
+
+Need inspiration? Here are a [selected compilation of examples](https://github.com/Canner/vulcan-sql-examples) showcasing how you can use VulcanSQL!
+
 ## How VulcanSQL works?
 
  💻 **Build**
@@ -49,7 +53,7 @@ VulcanSQL offers a development experience similar to dbt. Just insert variables
 
 🚀 **Accelerate**
 
-VulcanSQL uses DuckDB as a caching layer, boosting your query speed and API response time . This means faster, smoother data APIs for you and less strain on your data sources.
+VulcanSQL uses DuckDB as a caching layer, boosting your query speed and reducing API response time. This means faster, smoother data APIs for you and less strain on your data sources.
 
 🔥 **Deploy**
 
@@ -82,7 +86,7 @@ Below are some common scenarios that you may be interested:
 
 👏 **Data sharing**: Sharing data with partners, vendors, or customers, which requires a secure and scalable way to expose data.
 
-⚙️ **Internal tools**: Integration with internal tools like AppSmith and Retools, etc.
+⚙️ **Internal tools**: Integration with internal tools like Zapier, AppSmith and Retools, etc.
 
 ## Community
 

packages/doc/docs/intro.mdx

@@ -31,6 +31,16 @@ Discover how to validate and sanitize API parameters in VulcanSQL. This feature
 1. [Handling Data Privacy](./data-privacy/overview):
 Explore the mechanisms and practices for handling data privacy in VulcanSQL. It encompasses practices and measures implemented to safeguard personal, confidential, or regulated information from unauthorized access, misuse, or disclosure.
 
+### Extensions
+
+VulcanSQL allows you to extend its core functionalities through extensions.
+
+1. [dbt](./extensions/dbt): VulcanSQL supports queries from dbt's SQL models directly. 
+If you use dbt to create some models, you can make APIs for them instantly.
+2. [Hugging Face](./extensions/huggingface/overview): With this plugin, you can leverage 
+the power of language models to generate SQL quries using natural language.
+3. [API](./extensions/api): You can access data from 3rd parties through calling REST APIs with this extension.
+
 ### API Catalog & Documentation
 1. [API Catalog](catalog/intro):
    Learn how to create an API catalog with VulcanSQL, providing a centralized repository for all your Data APIs. This catalog enables easy discovery, management, and sharing of APIs within your organization.

packages/doc/docs/references/faq.mdx

@@ -0,0 +1,27 @@
+# FAQs
+
+## How do you deal with SQL injection attacks?
+
+Because VulcanSQL supports multiple connectors (e.g.: Snowflake, BigQuery, etc.), so we delegate our connectors to handle the SQL injection 
+by their prepared statement through parameterized queries from the connectors' client:
+
+BigQuery: https://cloud.google.com/bigquery/docs/parameterized-queries
+```sql
+SELECT word, word_count FROM `bigquery-public-data.samples.shakespeare`
+WHERE corpus = @corpus AND word_count >= @min_word_count
+ORDER BY word_count DESC
+```
+
+Snowflake: https://docs.snowflake.com/en/developer-guide/node-js/nodejs-driver-execute#binding-statement-parameters
+```sql
+SELECT word, word_count FROM `bigquery-public-data.samples.shakespeare`
+WHERE corpus = :1 AND word_count >= :2
+ORDER BY word_count DESC
+```
+
+Then, we replace the input parameters with parameterized values like $1, $2 ...etc, and record the input values in the 
+`Parameterizer` when sending a query from an API request with query arguments. Finally we organize the SQL statement with 
+parameters query in the `DataQueryBuilder` and send it to the connector to delegate the connector client to handle SQL 
+injection and execute the SQL query.
+
+You could see https://github.com/Canner/vulcan-sql/pull/40 to read it more.

packages/doc/sidebars.js

@@ -431,6 +431,10 @@ const sidebars = {
       type: 'doc',
       id: 'references/data-source-profile',
     },
+    {
+      type: 'doc',
+      id: 'references/faq',
+    },
     {
       type: 'html',
       value: '<br/><br/>',