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Unified Lakehouse with Apache Doris and Paimon: Xiaomi Achieves 6× Faster Performance
Best Practice

Unified Lakehouse with Apache Doris and Paimon: Xiaomi Achieves 6× Faster Performance

Xiaomi is a leading global player in consumer electronics. Best known for its smartphones and smart home devices, Xiaomi is among the top global three smartphone makers and continues to expand into new offerings like electric vehicles. With a global operation, Xiaomi requires an analytical data architecture that can support its growth and increasing demand. Their solution: Apache Doris and Apache Paimon.

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Apache Doris Empowers Real-time Lakehouse in Large-Scale Business Scenarios of Cainiao
Best Practice

Apache Doris Empowers Real-time Lakehouse in Large-Scale Business Scenarios of Cainiao

Cainiao, the world of e-commerce logistics giant, chose Apache Doris to upgrade its data platform. This step-by-step migration started in 2023, including validating Doris in a mission-critical scenario, expanding Doris's application scenarios, and executing full-scale deployment. the cost efficiency, stability, and operational efficiency of Doris have been powerfully proven. Currently, Doris powers over 25 clusters (10,000+ CPUs) across 3 regions without any failure.

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Leading Cloud Computing Service Provider Chose Apache Doris + Iceberg for Hyperscale Data Lakehouse
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Leading Cloud Computing Service Provider Chose Apache Doris + Iceberg for Hyperscale Data Lakehouse

The world's cloud computing service giant chose Apache Doris + Apache Iceberg to upgrade its data platform into a flexible, efficient data lakehouse with low costs. This solution handles reporting and BI, federated analysis, log storage and analysis, and high-concurrency analysis. With Apache Doris, this company has successfully launched 20+ projects with 50+ clusters, 3000+ nodes, and over 15 petabytes of data.

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Real-time Data Analytics at Scale: Integrating Apache Flink and Doris
Tech Sharing

Real-time Data Analytics at Scale: Integrating Apache Flink and Doris

In this article, we'll discuss the main technical use cases of Flink Doris Connector and Flink CDC, showing a complete playbook to combine Flink's real-time processing with Doris's fast analytics.

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What is Apache Doris

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Core capabilities

Real-time ingestion and storage
Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation.
Lightning-fast query
Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine.
Federated querying
Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL.
Semi-structured data
Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches.
Elastic architecture
Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.
Open ecosystem
Compatible with MySQL protocol and ANSI SQL, easily integrated with BI tools. Provide open data API to be accessible for external compute engines like Spark, Flink and ML/AI.

Unified data warehouse

for various analytics use cases

From traditional batch reporting to real-time reporting and dashboards. From internal-facing analytics like traditional BI to customer-facing analytics. From decision support analytics to algorithm-driven real-time decision-making.
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