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Top commercial bank migrated from Elasticsearch to Apache Doris for PB-scale log storage and analytics
Best Practice

Top commercial bank migrated from Elasticsearch to Apache Doris for PB-scale log storage and analytics

By replacing Elasticsearch with Apache Doris, they have saved 50% of resources while improving query speed by 2-4× and enjoying much simpler operations and maintenance.

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Elasticsearch vs ClickHouse vs Apache Doris — which powers observability better?
Tech Sharing

Elasticsearch vs ClickHouse vs Apache Doris — which powers observability better?

A side-by-side comparison of observability solutions in terms of performance, cost, usability, and ecosystem openness. This article also introduces Apache Doris as a powerful observability solution, supported by live demos and real-world user success stories.

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Community Voice | Elasticsearch vs. Apache Doris
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Community Voice | Elasticsearch vs. Apache Doris

Listen to what users say! Rahul Kolluri has provided a detailed technical comparison between Elasticsearch and Apache Doris based on real-world billing analytics scenarios. Based on his experience building dashboards and backend APIs around AWS usage and billing data, he saw Elasticsearch hit major limits when used for analytics.

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Community Voice | Apache Doris and DeepSeek: Redefining Intelligent Data Analytics
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Community Voice | Apache Doris and DeepSeek: Redefining Intelligent Data Analytics

This article will focus on the in — depth integration of Apache Doris and DeepSeek, and will analyze in detail its technical implementation, optimization strategies, application scenarios, and future trends.

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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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