[KYUUBI #605] Improve the landscape for README

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### _Why are the changes needed?_
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Please clarify why the changes are needed. For instance,
  1. If you add a feature, you can talk about the use case of it.
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### _How was this patch tested?_
- [ ] Add some test cases that check the changes thoroughly including negative and positive cases if possible

- [ ] Add screenshots for manual tests if appropriate

- [x] [Run test](https://kyuubi.readthedocs.io/en/latest/tools/testing.html#running-tests) locally before make a pull request

Closes #605 from yaooqinn/readme.

Closes #605

63d8702 [Kent Yao] Update Readme
c48d8e8 [Kent Yao] Update Readme
9a2e2d6 [Kent Yao] Update Readme

Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>

README.md

@@ -9,26 +9,70 @@
 ![GitHub Workflow Status](https://img.shields.io/github/workflow/status/NetEase/kyuubi/Kyuubi/master?style=plastic)
 [![Documentation Status](https://readthedocs.org/projects/kyuubi/badge/?version=latest)](https://kyuubi.readthedocs.io/en/latest/?badge=latest)
 
-Kyuubi is a high-performance universal JDBC and SQL execution engine, built on top of [Apache Spark](http://spark.apache.org).
-The goal of Kyuubi is to facilitate users to handle big data like ordinary data.
+## What is Kyuubi?
 
-It provides a standardized JDBC interface with easy-to-use data access in big data scenarios.
-End-users can focus on developing their own business systems and mining data value without having to be aware of the underlying big data platform (compute engines, storage services, metadata management, etc.).
+Kyuubi is a distributed multi-tenant Thrift JDBC/ODBC server for large-scale data management, processing, and analytics, built on top of Apache Spark and designed to support more engines (i.e., Flink). It has been open-sourced by NetEase since 2018. We are aiming to make Kyuubi an "out-of-the-box" tool for data warehouses and data lakes.
+
+Kyuubi provides a pure SQL gateway through Thrift JDBC/ODBC interface for end-users to manipulate large-scale data with pre-programmed and extensible Spark SQL engines. This "out-of-the-box" model minimizes the barriers and costs for end-users to use Spark at the client side. At the server-side, Kyuubi server and engines' multi-tenant architecture provides the administrators a way to achieve computing resource isolation, data security, high availability, high client concurrency, etc.
+
+![](./docs/imgs/kyuubi_positioning.png)
+
+- [x] A HiveServer2-like API
+- [x] Multi-tenant Spark Support
+- [x] Running Spark in a serverless way
+
+
+### Target Users
+
+Kyuubi's goal is to make it easy and efficient for `anyone` to use Spark(maybe other engines soon) and facilitate users to handle big data like ordinary data. Here, `anyone` means that users do not need to have a Spark technical background but a human language, SQL only. Sometimes, SQL skills are unnecessary when integrating Kyuubi with Apache Superset, which supports rich visualizations and dashboards.
+
+
+In typical big data production environments with Kyuubi, there should be system administrators and end-users.
+
+- System administrators: A small group consists of Spark experts responsible for Kyuubi deployment, configuration, and tuning.
+- End-users: Focus on business data of their own, not where it stores, how it computes.
+
+Additionally, the Kyuubi community will continuously optimize the whole system with various features, such as History-Based Optimizer, Auto-tuning, Materialized View, SQL Dialects, Functions, e.t.c.
+
+
+### Usage scenarios
+
+#### Port workloads from HiveServer2 to Spark SQL
+
+In typical big data production environments, especially secured ones, all bundled services manage access control lists to restricting access to authorized users. For example, Hadoop YARN divides compute resources into queues. With Queue ACLs, it can identify and control which users/groups can take actions on particular queues. Similarly, HDFS ACLs control access of HDFS files by providing a way to set different permissions for specific users/groups.
+
+Apache Spark is a unified analytics engine for large-scale data processing. It provides a Distributed SQL Engine, a.k.a, the Spark Thrift Server(STS), designed to be seamlessly compatible with HiveServer2 and get even better performance.
+
+HiveServer2 can identify and authenticate a caller, and then if the caller also has permissions for the YARN queue and HDFS files, it succeeds. Otherwise, it fails. However, on the one hand, STS is a single Spark application. The user and queue to which STS belongs are uniquely determined at startup. Consequently, STS cannot leverage cluster managers such as YARN and Kubernetes for resource isolation and sharing or control the access for callers by the single user inside the whole system. On the other hand, the Thrift Server is coupled in the Spark driver's JVM process. This coupled architect puts a high risk on server stability and makes it unable to handle high client concurrency or apply high availability such as load balancing as it is stateful.
+
+Kyuubi extends the use of STS in a multi-tenant model based on a unified interface and relies on the concept of multi-tenancy to interact with cluster managers to finally gain the ability of resources sharing/isolation and data security. The loosely coupled architecture of the Kyuubi server and engine dramatically improves the client concurrency and service stability of the service itself.
+
+
+#### DataLake/LakeHouse Support
+
+The vision of Kyuubi is to unify the portal and become an easy-to-use data lake management platform. Different kinds of workloads, such as ETL processing and BI analytics, can be supported by one platform, using one copy of data, with one SQL interface.
+
+- Logical View support via Kyuubi DataLake Metadata APIs
+- Multiple Catalogs support
+- SQL Standard Authorization support for DataLake(coming)
+
+
+#### Cloud Native Support
+
+Kyuubi can deploy its engines on different kinds of Cluster Managers, such as, Hadoop YARN, Kubernetes, etc.
+
+
+![](./docs/imgs/kyuubi_migrating_yarn_to_k8s.png)
+
+
+### The Kyuubi Ecosystem(present and future)
+
+
+The figure below shows our vision for the Kyuubi Ecosystem. Some of them have been realized, some in development, and others would not be possible without your help.
+
+![](./docs/imgs/kyuubi_ecosystem.png)
 
-Kyuubi relies on Apache Spark to provide high-performance data query capabilities,
-and every improvement in the engine's capabilities can help Kyuubi's performance make a qualitative leap.
-In addition, Kyuubi improves ad-hoc responsiveness through the engine caching,
-and enhances concurrency through horizontal scaling and load balancing.
-It provides complete authentication and authentication services to ensure data and metadata security.
-It provides robust high availability and load balancing to help you guarantee the SLA commitments.
-It provides a two-level elastic resource management architecture to effectively improve resource utilization while covering the performance and response requirements of all scenarios including interactive,
-or batch processing and point queries, or full table scans.
-It embraces Spark and builds an ecosystem on top of it,
-which allows Kyuubi to quickly expand its existing ecosystem and introduce new features,
-such as cloud-native support and `Data Lake/Lake House` support.
 
-The vision of Kyuubi is to unify the portal and become an easy-to-use data lake management platform.
-Different kinds of workloads, such as ETL processing and BI analytics, can be supported by one platform, using one copy of data, with one SQL interface.
 
 ## Online Documentation