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docs/en/guides/00-products/00-dce.md

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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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Databend Community Edition is the open-source version of Databend, released under the **Apache 2.0 license** for free commercial and non-commercial use. It provides the same core analytics engine as [Databend Enterprise](/guides/products/dee/) and [Databend Cloud](/guides/products/dc/), but without enterprise-specific features.
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Databend is an open-source, elastic, and workload-aware cloud data warehouse built in Rust, offering a cost-effective alternative to Snowflake. It's designed for complex analysis of the world's largest datasets.
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## Core Features
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<Tabs groupId="whydatabend">
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<TabItem value="Performance" label="Performance">
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- **High-performance SQL processing** with vectorized execution
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- **Multiple data types**: JSON, ARRAY, MAP, VARIANT
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- **Standard SQL operations**: SELECT, INSERT, DELETE, UPDATE, REPLACE, COPY, MERGE
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- **Multiple data formats**: CSV, JSON, Parquet, ORC, and more
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- **Self-hosted deployment** on your infrastructure
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- **Object storage support** for major cloud platforms
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- Blazing-fast data analytics on object storage.
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- Leverages data-level parallelism and instruction-level parallelism technologies for [optimal performance](https://benchmark.clickhouse.com/).
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- No indexes to build, no manual tuning, and no need to figure out partitions or shard data.
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## Enterprise Features
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</TabItem>
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For advanced capabilities, consider upgrading to [Databend Enterprise](/guides/products/dee/) or [Databend Cloud](/guides/products/dc/), which include:
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- Advanced authentication and security
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- Priority technical support
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- Enterprise connectors and integrations
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- Advanced monitoring and compliance features
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<TabItem value="Data Manipulation" label="Data Manipulation">
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See the complete list in [Enterprise Features](/guides/products/dee/enterprise-features).
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- Supports atomic operations such as `SELECT`, `INSERT`, `DELETE`, `UPDATE`, `REPLACE`, `COPY`, and `MERGE`.
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- Provides advanced features such as Time Travel and Multi Catalog (Apache Hive / Apache Iceberg).
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- Supports [ingestion of semi-structured data](/guides/load-data/load) in various formats like CSV, JSON, and Parquet.
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- Supports semi-structured data types such as [ARRAY, MAP, and JSON](/sql/sql-reference/data-types/).
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- Supports Git-like MVCC storage for easy querying, cloning, and restoration of historical data.
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## Getting Started
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</TabItem>
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- **[Quick Start Guide](/guides/deploy/QuickStart/)**: Get up and running in 5 minutes
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- **[Download & Install](/guides/deploy/deploy/download)**: Self-hosted deployment options
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- **[GitHub Repository](https://github.com/databendlabs/databend)**: Source code and community
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<TabItem value="Object Storage" label="Object Storage">
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- Supports various object storage platforms. Click [here](../10-deploy/01-deploy/00-understanding-deployment-modes.md#supported-object-storage) to see a full list of supported platforms.
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- Allows instant elasticity, enabling users to scale up or down based on their application needs.
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</TabItem>
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</Tabs>
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Databend's high-level architecture is composed of a `meta-service layer`, a `query layer`, and a `storage layer`.
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![Databend Architecture](https://github.com/databendlabs/databend/assets/172204/68b1adc6-0ec1-41d4-9e1d-37b80ce0e5ef)
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<Tabs groupId="databendlay">
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<TabItem value="Meta-Service Layer" label="Meta-Service Layer">
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Databend efficiently supports multiple tenants through its meta-service layer, which plays a crucial role in the system:
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- **Metadata Management**: Handles metadata for databases, tables, clusters, transactions, and more.
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- **Security**: Manages user authentication and authorization for a secure environment.
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Discover more about the meta-service layer in the [meta](https://github.com/databendlabs/databend/tree/main/src/meta) on GitHub.
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</TabItem>
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<TabItem value="Query Layer" label="Query Layer">
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The query layer in Databend handles query computations and is composed of multiple clusters, each containing several nodes.
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Each node, a core unit in the query layer, consists of:
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- **Planner**: Develops execution plans for SQL statements using elements from [relational algebra](https://en.wikipedia.org/wiki/Relational_algebra), incorporating operators like Projection, Filter, and Limit.
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- **Optimizer**: A rule-based optimizer applies predefined rules, such as "predicate pushdown" and "pruning of unused columns", for optimal query execution.
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- **Processors**: Constructs a query execution pipeline based on planner instructions, following a Pull&Push approach. Processors are interconnected, forming a pipeline that can be distributed across nodes for enhanced performance.
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Discover more about the query layer in the [query](https://github.com/databendlabs/databend/tree/main/src/query) directory on GitHub.
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</TabItem>
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<TabItem value="Storage Layer" label="Storage Layer">
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Databend employs Parquet, an open-source columnar format, and introduces its own table format to boost query performance. Key features include:
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- **Secondary Indexes**: Speeds up data location and access across various analysis dimensions.
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- **Complex Data Type Indexes**: Aimed at accelerating data processing and analysis for intricate types such as semi-structured data.
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- **Segments**: Databend effectively organizes data into segments, enhancing data management and retrieval efficiency.
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- **Clustering**: Employs user-defined clustering keys within segments to streamline data scanning.
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Discover more about the storage layer in the [storage](https://github.com/databendlabs/databend/tree/main/src/query/storages) on GitHub.
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</TabItem>
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</Tabs>

docs/en/guides/00-products/index.md

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**Databend** 是一个开源的、Serverless 产品形态、存算分离架构、基于对象存储构建的云原生数据湖仓,提供极致的性能、快速的弹性扩展能力,致力于打造开源版的 **Snowflake**
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**Databend** 是新一代云原生 **[数据+AI] 分析平台**,支持结构化、半结构化和非结构化多模态数据。
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作为 **Snowflake 的开源替代方案**,具有**近 100% SQL 兼容性**和原生 AI 能力,受到世界级企业信赖,管理着 **800+ PB** 数据和**每日 1 亿+** 查询。
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**Databend** is an open-source, serverless, cloud-native data lakehouse built on object storage with a decoupled storage and compute architecture. It delivers exceptional performance and rapid elasticity, aiming to be the open-source alternative to **Snowflake**.
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**Databend** is the next-generation cloud **[Data+AI] Analytics** platform for structured, semi-structured & unstructured multimodal data.
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As the **open-source alternative to Snowflake** with **near 100% SQL compatibility** and native AI capabilities, trusted by world-class enterprises managing **800+ petabytes** and **100+ million queries daily**.
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<DocsOverview />
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**Here are some entries you might want to learn about**
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**Getting Started**
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- **[SQL Reference](/sql)**: Your swift-access guide for Databend essentials!
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- **[Connect to Databend](/guides/sql-clients)**: Connect with various SQL clients and programming languages.
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**Data Processing**
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- **[Data Loading](/guides/load-data)**: Import data from various sources into Databend.
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- **[Data Unloading](/guides/unload-data)**: Export data from Databend to different formats.
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- **[External Functions](/guides/query/external-function)**: Extend Databend's capabilities with custom functions.
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- **[Semi-Structured Data](/sql/sql-functions/semi-structured-functions)**: Process JSON, arrays, and nested data with VARIANT type.
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**AI & Advanced Analytics**
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- **[Databend AI and ML](/guides/ai-functions)**: Leverage AI capabilities in your data processing.
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- **[Data Management](/guides/data-management)**: Manage your data lifecycle effectively.
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- **[Data Lakehouse](/guides/access-data-lake)**: Seamless integration with Hive, Iceberg, and Delta Lake.
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- **[Vector Functions](/sql/sql-functions/vector-functions)**: Vector similarity and distance calculations for ML workloads.
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- **[Full-Text Search](/guides/performance/fulltext-index)**: Advanced text search and relevance scoring.
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**Performance & Scale**
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- **[Performance Optimization](/guides/performance)**: Enhance query performance with various strategies.
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- **[Benchmarks](/guides/benchmark)**: Compare Databend performance with other data warehouses.
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- **[Data Lakehouse](/guides/access-data-lake)**: Seamless integration with Hive, Iceberg, and Delta Lake.

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