feat:add discord links and restore slack links (#3542)

Author: yang1666204

blog/Auto-Synchronization-of-an-Entire-MySQL-Database-for-Data-Analysis.md

@@ -191,5 +191,5 @@ CREATE TABLE doris_sink (
 );
 ```
 
-If you've got any questions, find Apache Doris developers on [Discord](/discord).
+If you've got any questions, find Apache Doris developers on [Slack](https://doris.apache.org/slack).
 

blog/Building-A-Log-Analytics-Solution-10-Times-More-Cost-Effective-Than-Elasticsearch.md

@@ -247,4 +247,4 @@ For more feature introduction and usage guide, see documentation: [Inverted Inde
 
 In a word, what contributes to Apache Doris' 10-time higher cost-effectiveness than Elasticsearch is its OLAP-tailored optimizations for inverted indexing, supported by the columnar storage engine, massively parallel processing framework, vectorized query engine, and cost-based optimizer of Apache Doris. 
 
-As proud as we are about our own inverted indexing solution, we understand that self-published benchmarks can be controversial, so we are open to [feedback](/discord) from any third-party users and see how [Apache Doris](https://github.com/apache/doris) works in real-world cases.
+As proud as we are about our own inverted indexing solution, we understand that self-published benchmarks can be controversial, so we are open to [feedback](https://doris.apache.org/slack) from any third-party users and see how [Apache Doris](https://github.com/apache/doris) works in real-world cases.

blog/Building-a-Data-Warehouse-for-Traditional-Industry.md

@@ -172,4 +172,4 @@ Actually, before we evolved into our current data architecture, we tried Hive, S
 
 On the other hand, to smoothen our  big data transition, we need to make our data platform as simple as possible in terms of usage and maintenance. That's why we landed on Apache Doris. It is compatible with MySQL protocol and provides a rich collection of functions so we don't have to develop our own UDFs. Also, it is composed of only two types of processes: frontends and backends, so it is easy to scale and track.
 
-Find Apache Doris developers on [Discord](/discord).
+Find Apache Doris developers on [Slack](https://doris.apache.org/slack).

blog/Building-the-Next-Generation-Data-Lakehouse-10X-Performance.md

@@ -249,5 +249,5 @@ http://doris.apache.org
 
 https://github.com/apache/doris
 
-Find Apache Doris developers on [Discord](/discord).
+Find Apache Doris developers on [Slack](https://doris.apache.org/slack).
 

blog/Database-Dissection-How-Fast-Data-Queries-Are-Implemented.md

@@ -134,7 +134,7 @@ The results are as below:
 In short, what contributed to the fast data loading and data queries in this case?
 
 - The Colocate mechanism that's designed for distributed computing
-- Collaboration between database users and [developers](/discord) that enables the operator merging
+- Collaboration between database users and [developers](https://doris.apache.org/slack) that enables the operator merging
 - Support for a wide range of data loading methods to choose from
 - A vectorized engine that brings overall performance increase
 

blog/Database-in-Fintech-How-to-Support-ten-thousand-Dashboards-Without-Causing-a-Mess.md

@@ -73,7 +73,7 @@ Analysts often check data reports of the same metrics on a regular basis. These
 
 The complexity of data analysis in the financial industry lies in the data itself other than the engineering side. Thus, the underlying data architecture should focus on facilitating the unified and efficient management of data. Apache Doris provides the flexibility of simple metric registration and the ability of fast and resource-efficient metric computation. In this case, the user is able to handle 10,000 active financial metrics in 10,000 dashboards with 30% less ETL efforts.
 
-Find Apache Doris developers on [Discord](/discord).
+Find Apache Doris developers on [Slack](https://doris.apache.org/slack).
 
 
 

blog/For-Entry-Level-Data-Engineers-How-to-Build-a-Simple-but-Solid-Data-Architecture.md

@@ -100,4 +100,4 @@ Firstly, Apache Doris is famously fast in Join queries. So if you need to extrac
 
 ## Conclusion
 
-This is the overview of a simple data architecture and how it can provide the data services you need. It ensures data ingestion stability and quality with Flink CDC, and quick data analysis with Apache Doris. The deployment of this architecture is simple, too. If you plan for a data analytic upgrade for your business, you might refer to this case. If you need advice and help, you may join our [community here](/discord).
+This is the overview of a simple data architecture and how it can provide the data services you need. It ensures data ingestion stability and quality with Flink CDC, and quick data analysis with Apache Doris. The deployment of this architecture is simple, too. If you plan for a data analytic upgrade for your business, you might refer to this case. If you need advice and help, you may join our [community here](https://doris.apache.org/slack).

blog/Is-Your-Latest-Data-Really-the-Latest-Check-the-Data-Update-Mechanism-of-Your-Database.md

@@ -223,5 +223,5 @@ mysql> select * from test_table;
 
 ## Conclusion
 
-Congratulations. Now you've gained an overview of how data updates are implemented in Apache Doris. With this knowledge, you can basically guarantee efficiency and accuracy of data updating. But wait, there is so much more about that. As Apache Doris 2.0 is going to provide more powerful Partial Column Update capabilities, with improved execution of the Update statement and the support for more complicated multi-table Join queries, I will show you how to take advantage of them in details in my follow-up writings. [We](/discord) are constantly updating our data updates!
+Congratulations. Now you've gained an overview of how data updates are implemented in Apache Doris. With this knowledge, you can basically guarantee efficiency and accuracy of data updating. But wait, there is so much more about that. As Apache Doris 2.0 is going to provide more powerful Partial Column Update capabilities, with improved execution of the Update statement and the support for more complicated multi-table Join queries, I will show you how to take advantage of them in details in my follow-up writings. [We](https://doris.apache.org/slack) are constantly updating our data updates!
 

blog/Listen-to-That-Poor-BI-Engineer-We-Need-Fast-Joins.md

@@ -102,4 +102,4 @@ We believe self-service BI is the future in the BI landscape, just like AGI is t
 
 
 
-Find the Apache Doris developers on [Discord](/discord)
+Find the Apache Doris developers on [Slack](https://doris.apache.org/slack)

blog/Log-Analysis-How-to-Digest-15-Billion-Logs-Per-Day-and-Keep-Big-Queries-Within-1-Second.md

@@ -88,7 +88,7 @@ These strategies have shortened the response time of queries. For example, a que
 
 ## Ongoing Plans
 
-The user is now testing with the newly added [inverted index](https://doris.apache.org/docs/table-design/index/inverted-index) in Apache Doris. It is designed to speed up full-text search of strings as well as equivalence and range queries of numerics and datetime. They have also provided their valuable feedback about the auto-bucketing logic in Doris: Currently, Doris decides the number of buckets for a partition  based on the data size of the previous partition. The problem for the user is, most of their new data comes in during daytime, but little at nights. So in their case, Doris creates too many buckets for night data but too few in daylight, which is the opposite of what they need. They hope to add a new auto-bucketing logic, where the reference for Doris to decide the number of buckets is the data size and distribution of the previous day. They've come to the [Apache Doris community](/discord) and we are now working on this optimization. 
+The user is now testing with the newly added [inverted index](https://doris.apache.org/docs/table-design/index/inverted-index) in Apache Doris. It is designed to speed up full-text search of strings as well as equivalence and range queries of numerics and datetime. They have also provided their valuable feedback about the auto-bucketing logic in Doris: Currently, Doris decides the number of buckets for a partition  based on the data size of the previous partition. The problem for the user is, most of their new data comes in during daytime, but little at nights. So in their case, Doris creates too many buckets for night data but too few in daylight, which is the opposite of what they need. They hope to add a new auto-bucketing logic, where the reference for Doris to decide the number of buckets is the data size and distribution of the previous day. They've come to the [Apache Doris community](https://doris.apache.org/slack) and we are now working on this optimization.