📢We are thrilled to announce the release of Apache Doris 2.1.0! For our long-term supportive users, allow me to re-introduce Apache Doris with its amazing new features and substantially improved data writing and query performance! For those who are new to Apache Doris, this is great timing for a proof of concept to see how it performs in your use case! Fasten up and be ready for: 🚶♂️ 100% faster out-of-the-box performance proven by TPC-DS benchmark tests 🚶♀️ Improved data lake analytics capabilities: 4~6 times faster than Trino and Spark 🏃♂️ Solid support for semi-structured data analysis 🏃♀️ Materialized view across multiple tables to accelerate multi-table joins 💃 Enhanced real-time writing efficiency powered by AUTO_INCREMENT column, AUTO PARTITION, forward placement of MemTable, and Group Commit. 🕺 Better workload management for higher performance stability https://lnkd.in/gjVXD6gQ #database #dataengineering #analytics #bigdata #opensource
Apache Doris
Software Development
San Francisco, California 2,516 followers
Apache Doris is an open-source real-time data warehouse based on MPP architecture.
About us
Apache Doris is an open-source real-time data warehouse based on MPP architecture, known for its fast speed and ease of use. It supports real-time data ingestion and real-time query response in both high-concurrency point query and high-throughput analysis scenarios. With it, users can process and analyze large datasets in the blink of an eye. In June 2022, Apache Doris became a full-fledged, top-level project incubated by ASF. It accumulated nearly 600 contributors and more than 20,000 developers are using Apache Doris today. Doris is also used in production within over 2000 companies around the world, trusted by business giants such as AWS, Fuse, JD.com, Lenovo, OPPO, Shoppe, TikTok, Tencent, Vivo, Xiaomi and etc. We welcome more open source technology enthusiasts to join the Apache Doris community and together discover infinite possibilities! Learn more about Apache Doris on Github: https://github.com/apache/doris Join the Apache Doris community on Slack: https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2gmq5o30h-455W226d79zP3L96ZhXIoQ
- Website
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https://doris.apache.org/
External link for Apache Doris
- Industry
- Software Development
- Company size
- 201-500 employees
- Headquarters
- San Francisco, California
- Type
- Nonprofit
- Founded
- 2018
Locations
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Primary
San Francisco, California 94102, US
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Beijing, Beijing 100086, CN
Employees at Apache Doris
Updates
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Thanks to Riccardo Bernardi, Eng. for introducing Routine Load, one of our most frequently used data ingestion methods, in a succinct way. It's always valuable to receive user feedback on our technologies and hear how they describe them in their own words. https://lnkd.in/gfB7RW4k #Kafka #database #dataengineer #OLAP #opensource #dataplatform
What are routine loads in Apache Doris? And why are they advantageous…
medium.com
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We've been thrilled to witness the incredible formation of #Lakehouse V2 at Ortege AI in the past months. It's truly an honor to see how Apache Doris is fueling the growth and innovation of a #blockchain and #AI #startup. A huge thank you to Justin Trollip for sharing this exciting journey with us. We look forward to the great products you make. https://lnkd.in/g3HwRjjT #database #analytics #Databricks #dataengineering
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Quick start guide on building a data lakehouse solution with Apache Doris and Apache #Iceberg. It allows you to: 1️⃣ Build a unified platform for federated data analysis, leveraging the high-performance query engine of Doris to associate data from Iceberg and other data sources. 2️⃣ Manage and build Iceberg tables directly through Doris, performing data cleaning and processing in Doris and writeback to Iceberg. 3️⃣ Build an open data storage platform that shares Doris data with upstream and downstream systems for further processing through the Iceberg table engine. https://lnkd.in/djrBNGXA #DataLakehouse #dataengineering #opensource #dataanalytics
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Anticipating Mingyu's talk in November with excitement 🙌 Also, we'd like to take this chance to reintroduce ourselves: Apache Doris is an OLAP database for fast data analytics. It provides self-managed table format for high-concurrency and low-latency queries, semi-structured data analytics and complex ad-hoc queries, all by using standard SQL. It can also query data from various lake formation such as Apache #Hudi, Apache #Iceberg, and Apache #Paimon. In Mingyu's session, you will learn what Doris can do for real-time analytics and how to build a fast data analysis engine on data lake.
Don't miss 陈明雨 (Chen) Mingyu session on "Apache Doris: an alternative lakehouse solution for real-time analytics"! Discover how #ApacheDoris combines the best of data warehouses and lakes for rapid and efficient data analysis🔋 Learn about its core features and how to build a high-performance data analysis engine on a data lake. 💻 Register for the conference: osacon.io PS: Be one of the first 1,000 to register and get the chance to win 1 of 10 amazing prizes! 🎉
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Use case: the Apache Doris-based logging system for an AI unicorn 🦄 As one of the most data-intensive industries, #AI use cases are characterized by long processing pipelines, abundant contextual data, and large per-request data volumes. The gigantic log size requires their #logging system to be high-performance, flexible, and low-cost. In this blog, you will learn: 🌟 Why the user migrated from #Grafana #Loki 🌟 How they benefit from the new Apache Doris-based system 🌟 Their best practices in building an efficient logging system https://lnkd.in/gmhJntuj
How AI unicorn MiniMax migrated from Loki and built a PB-scale logging system with Apache Doris - Apache Doris
doris.apache.org
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Apache Doris reposted this
VeloDB VS HBase: When to use them? VeloDB is a real-time data warehouse, while HBase is a distributed key-value store. 🏠 Use case: OLAP analytical workloads combined with KV point lookups VeloDB is a great fit while HBase lacks relevant analytical capabilities. 🏠 Use case: small to medium-scale KV point lookups Both VeloDB and HBase are good options, but VeloDB boasts a simpler and more maintainable architecture. 🏠 Use case: only PB-scale online KV point lookups HBase is a better solution. #database #dataengineering #HBase #BigData #analytics
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In addition to the performance and capabilities of Apache Doris itself, we also provide you a rich, vibrant, and continuously expanding ecosystem, which include: 🌴 Cluster management Cluster Manager Kubernetes Operator 🌴 Connectors Flink-Doris-Connector Spark-Doris-Connector Kafka-Doris-Connector DBT-Doris-Adapter 🌴 Data loading Doris Streamloader 🌴 Data migration SQL Convertor https://lnkd.in/gwjk6rm7
Apache Doris - Ecosystem | Tools for quickly big data analytics - Apache Doris
doris.apache.org
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A #log system typically contains the following key components: 1️⃣ Collection agent: collecting logs from service standard outputs and pushing the data into a central message queue. 2️⃣ Message queue: decoupling upstream and downstream components, absorbing spikes, and ensuring system stability even when downstream components are unavailable. 3️⃣ Storage and query middleware: storing and querying the log data. In a log system, this middleware should be capable of inverted indexing to support efficient log searches. A typical choice for the middleware will be #Elasticsearch, and here's why you should also take Apache Doris into consideration.
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What bothers users before they migrate to Apache Doris? 1️⃣ Complex data architecture Multiple OLAP components Tedious maintenance 2️⃣ Data governance challenges Multi-source metadata Unmanageable data quality issues 3️⃣ Resource underutilization High data redundancy (bringing high storage costs) Lack of a unified data query entry, adding to scheduling difficulties 4️⃣ Inconsistency issues Each data warehouse calculates its data separately, making it hard to compare apples to apples. What draws them to Apache Doris? ⬇️
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