Last Thursday's Apache Doris Meetup in Singapore was a success!
On October 24 ("1024"), a day that resonates with tech enthusiasts, the Apache Doris community came together for an engaging meetup in Singapore. It was a great opportunity for data enthusiasts and professionals to connect, share insights, and explore the latest developments in the world of big data and analytics.
Highlights of the event
Keynote: Apache Doris
The meetup kicked off with a presentation by the Apache Doris PMC Chair, who introduced the technical features of Doris, highlighting its fast performance, user friendliness, and capabilities to handle different types of analytics workloads:
“Apache Doris is friendly for first-time users, with a low operational cost as a distributed system and flexible deployment operation for various environments. Besides reporting and ad-hoc analysis, Doris also supports semi-structured data analysis and can act like a lakehouse query engine. ”
"For both single-table query and complex multi-table joins, Apache Doris can give better performance than other alternatives."
"Tencent Music, a leading online provider, replaced Elasticsearch with Doris so they can unify their online data analysis and their user content retrieval service. It reduced their storage usage by 80% and improved their data freshness by reducing the data import time from 10 hours to 3 hours."
User story: Douyin Group
Boyang Chen, a database development engineer and an Apache Doris Contributor, introduced the usage of Doris in Douyin Group and discussed multi-stream data analysis as a common use case based on Doris:
"I would use three keywords to describe the Doris usage inside Douyin Group."
"The first keyword is real-time data warehouse. As Doris provides powerful ELT ability and efficient query performance, we've been building a real-time data warehouse based on Doris. It mainly consists of two platforms: a data integration platform that controls the data imports and exports of Doris and a data production platform which provides task scheduling."
"The second keyword is data serving. As Doris can provide high-QPS and low-latency data analysis, we can use it as a computing engine for multiple business lines, like live streaming, e-commerce, and advertisement."
"The last keyword is data lake. As Doris provides powerful data lake ability, we've been using Doris-on-Elasticsearch and Doris-on-Hive. In short, we've been using Doris extensively inside our Group."
User story: Footprint Analytics
Wade Deng, Co-Founder & CTO at Footprint Analytics and XCelsior AI, talked about their blockchain analytics solution using Apache Doris. He started with the introduction to the data platform and architecture of Footprint Analytics, and explained why they chose Apache Doris among options like Apache Druid, ClickHouse, and TiDB. Then he provided lots of hands-on experience and advice on the usage of Apache Doris, including the choice of data models for the crypto domain, materialized view, data migration, and data compaction.
"Two years ago, we were using Google Cloud and BigQuery for the major data warehouse. Then we had a few clients who required low latency, especially for our pricing and trading tables. We had a few options. In summary, Doris stood out for its high concurrency and SQL support. This lowers the barrier for data scientists and analysts to use our platform."
Community partner: RisingWave
The session continued with a presentation from Liu Zhi, Product Manager of RisingWave. He discussed real-time data enrichment and analytics using RisingWave and Apache Doris:
"Streaming process and OLAP are not like a one-to-replace-the-other relationship. It's more like: Each of the processing paradigm tries to solve a different use case. So when people first come up with stream processing, they ask the question like: In real world, data is generated continuously, so why don't we process data continuously so that we can acquire insights continuously? The idea is that you are facing an infinite stream of data where you have a fixed query, so whenever there are new data coming to the system, the query reacts to the data."
"The idea of OLAP is more like: You have a finite set of data, and business analysts will try to explore what's contained in that data set, so they will come up with different queries. Based on the insights, the modify the queries and try to get more different insights."
Networking and engagement
One of the most rewarding aspects of the meetup was the opportunity for attendees to engage in lively discussions, where participants exchanged ideas and built networks that opened up endless opportunities.
A big thank you
We extend our heartfelt thanks to everyone who contributed to making the Apache Doris Meetup in Singapore a success. Your participation and enthusiasm truly made the event special. We are excited to continue building our community and supporting each other in our data journeys.
Looking ahead
We look forward to future meetups and opportunities to connect with more users and data enthusiasts. Stay tuned for updates, as we will upload recordings of the speakers' presentations to YouTube soon!
Thank you once again for joining us, and we can't wait to see you at our next event.
Download the slides from the event to dive deeper into the insights and innovations shared by the speakers.
If you're interested in speaking at future Apache Doris meetups (in-person or virtual), simply fill out this short form and we'll be happy to talk further.