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The following commit(s) were added to refs/heads/kylin5 by this push:
new 96293c4421 minor fix readme
96293c4421 is described below
commit 96293c4421bf3e76ef6313e1a9d2f777c5bdc3f0
Author: lionelcao <whuca...@gmail.com>
AuthorDate: Tue Oct 1 13:12:32 2024 +0800
minor fix readme
---
README.md | 14 +++++---------
1 file changed, 5 insertions(+), 9 deletions(-)
diff --git a/README.md b/README.md
index d20edc8712..e775fab6bb 100644
--- a/README.md
+++ b/README.md
@@ -31,8 +31,6 @@ Kylin has following key strengths:
## What's New in Kylin 5.0
----
-
### 📊 1. Internal Table
Kylin now support internal table, which is designed for flexible query and
lakehouse scenarios.
@@ -52,8 +50,6 @@ Kylin now support Apache Kafka as streaming data source of
model building. Users
## Significant Change
----
-
### 🤖1. Metadata Refactory
In Kylin 5.0, we have refactored the metadata storage structure and the
transaction process, removed the project lock and Epoch mechanism. This has
significantly improved transaction interface performance and system concurrency
capabilities.
@@ -61,13 +57,11 @@ To upgrade from 5.0 alpha, beta, follow the [Metadata
Migration Guide](https://k
The metadata migration tool for upgrading from Kylin 4.0 is not tested, please
contact kylin user or dev mailing list for help.
-## Other Optimizations and Improvements
+### Other Optimizations and Improvements
Please refer to [Release Notes](https://kylin.apache.org/docs/release_notes/)
for more details.
## Quick Start
----
-
### 🐳 Play Kylin in Docker
To explore new features in Kylin 5 on a laptop, we recommend pulling the
Docker image and checking the [Apache Kylin Standalone Image on Docker
Hub](https://hub.docker.com/r/apachekylin/apache-kylin-standalone) (For amd64
platform).
@@ -114,6 +108,8 @@ Kylin utilizes multidimensional modeling theory to build
star or snowflake schem
- **Table Index**: A multilevel index in a wide table and can be used to
answer detailed queries such as the last 100 transactions of a certain user.
+---
+
### Why Use Kylin
+ **Low Query Latency vs. Large Volume**
@@ -133,11 +129,11 @@ Kylin utilizes multidimensional modeling theory to build
star or snowflake schem
+ **Manual Modeling vs. Recommendation**
- Before Kylin 5.0, model design had to be done manually, which was a tedious
process requiring extensive knowledge of multidimensional modeling. However,
this changed with the introduction of Kylin 5.0. We now offer a new approach to
model design, called **recommendation**, which allows models to be created by
importing SQL, along with an automatic way to remove unnecessary indexes.
Additionally, the system can leverage query history to generate index
recommendations, further optimizing [...]
+ Before Kylin 5.0, model design had to be done manually, which was a tedious
process requiring extensive knowledge of multidimensional modeling. However,
this changed with the introduction of Kylin 5.0. We now offer a new approach to
model design, called **recommendation**, which allows models to be created by
importing SQL, along with an automatic way to remove unnecessary indexes.
Additionally, the system can leverage query history to generate index
recommendations, further optimizing [...]
+ **Batch Data vs. Streaming Data**
In the OLAP field, data has traditionally been processed in batches.
However, this is changing as more companies are now required to handle both
batch and streaming data to meet their business objectives. The ability to
process data in real-time has become increasingly critical for applications
such as real-time analytics, monitoring, and event-driven decision-making.
- To address these evolving needs, we have introduced support for streaming
data in the new version. This allows users to efficiently process and analyze
data as it is generated, complementing the traditional batch processing
capabilities. For more details, please refer to [Streaming](streaming/intro.md).
+ To address these evolving needs, we have introduced support for streaming
data in the new version. This allows users to efficiently process and analyze
data as it is generated, complementing the traditional batch processing
capabilities. For more details, please refer to
[Streaming](https://kylin.apache.org/docs/model/streaming/intro).
