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Data Storage

Addressing storage issues in different storage scenarios and data formats

Data Management

Provide efficient, intelligent, and reliable data management solutions

iDataFusion

Data collection

Migration and replication

Conversion processing

iDataExplorer

Efficient retrieval

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CMS

Unified monitoring

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Data Application

Quick retrieval and invocation of business, enabling data to unleash greater value

IDM

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Format conversion

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SandStone data product collection

Fully meet the needs of customers for massive data storage, management and application

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SandStone Inspection Data Management System IDM

SandStone IDM can provide a number of functional modules such as quality traceability, data regularization, data classification, data analysis, visual business kanban, value mining, and data hierarchical management for enterprises to build an efficient, agile and flexible manufacturing support base for enterprises.

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Financial Imaging Financial Unstructured Data Financial Lake Warehouse Integration

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Enterprise content management Cloud native data warehouse Machine Learning Private cloud storage Container cloud storage

Case Collection of Production Line Inspection Data Management

How can advanced manufacturing companies such as BYD, BOE, and Funeng Technology control data storage and management costs to achieve synchronous improvement in product quality and production efficiency?

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Inspection data management system

SandStone IDM provides multiple functional modules for massive detection data in enterprises, including quality traceability, data organization, data classification, data analysis, visualization of business dashboards, value mining, and data hierarchical management. It builds an efficient, agile, and flexible manufacturing support base for enterprises.

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SandStone Data Financial Industry Solution

To build a solid storage foundation for the financial industry, providing various scenario storage solutions such as financial imaging, unstructured data, integrated warehousing, and information and innovation storage.

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Company Profile

Meet SandStone here, we sincerely hope to work with you hand in hand

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Introduction of SandStone

A comprehensive understanding of SandStone's brand strength, product business and development

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Cloud native data warehouse

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Background of the solution

客户需求

解决方案

客户价值

Background of the solution

传统的数据仓库效率比较低、灵活性和可扩展性不足

传统的数据仓库基于HDFS存储数据，这种架构可以处理大规模的数据，缺点是效率比较低，而且灵活性和可扩展性不足，无法处理相对复杂的SQL查询。

存算分离架构的云原生数据仓库让用户可以独立规划存储和计算资源，存储资源池提供S3对象和原生HDFS接口，与MPP架构的数据仓库平台一起，为企业提供从数据到知识的综合分析能力，实现多源数据的集成、海量数据的挖掘、多类型知识的建模和多业务场景的分析。

客户需求

IT设施架构升级，存算分离架构成为趋势

IT基础设施需要支持快速增长的数据规模

数据应用增多、新业务拓展、数据挖掘深度提高等都导致大量数据要存储，数据也在企业经营中正在发挥着更大的作用。随着不断膨胀扩大的数据规模，对数据的存储计算也提出了更高的要求。对需要存储大量小文件的应用场景，HDFS存储层无法高效支持。

数据处理实时性增强

每时每刻都有大量数据因为无法被及时处理而被迫丢弃，在某些场合，数据价值随着时间流逝而不断衰减，新鲜、活跃的数据能为用户带来更大的价值。对数据实时处理的要求应该更好地得到满足，越来越多的业务会基于实时数据去构建。

IT设施架构升级

从IT技术的演进趋势可以发现，计算层正在逐渐轻量化，逐步与数据解耦，存储和技术分离可以简化IT基础设施的维护，根据实际需要独立扩展，避免资源浪费。存储层正在逐渐分级化和专业化，计算层无需继续依赖开源HDFS存储，可以转向具有更好的可靠性和利用率，具备更丰富企业级特性的专业存储。

解决方案

存储、计算、管理分离，实现海量数据的低延迟处理

杉岩数据围绕着对象存储、数据管理和计算技术构建的云原生数据仓库，是服务于企业和机构的大数据分析解决方案。该方案以对象存储为基础，汇聚所有类型的数据为多业务平台提供数据服务，联合下一代高性能可扩展的数据仓库平台，为用户提供标准SQL支持和超高的查询性能。云原生数据仓库解决方案融合了MPP数据仓库技术，支持大数据量处理，具有优秀的压缩技术，可以对数据库的常规数据、索引数据、临时表空间数据进行全面压缩。该方案还具备大数据分析能力，支持大量用户并发访问、并行挖掘算法、分析函数等常用功能，具有处理TB级或PB级数据容量的能力。该方案通过存储、计算、管理分离的系统架构，实现了海量数据的低延迟处理，提升了系统的并发处理能力和扩展性，能够取得近乎无限扩展的并发处理能力。

客户价值

存储、计算、管理分离架构，秒级扩容、超高性能

低成本实现数据湖存储

云原生架构实现了存算分离，存储和计算资源可以独立配置和升级扩容，对象存储比HDFS存储成本更低，相对传统方案降低90%，独立的元数据服务可以实现秒级扩容。

高并发、高性能的大数据分析能力

通过集群加负载均衡的方式实现高并发，通过无共享大规模并行处理（MPP）、流水式执行引擎、列式存储和大表分区等技术对数据仓库应用进行了优化，提供超高性能。

良好的技术生态兼容

分析接口完全开放，100%兼容开源的PostgreSQL和Greenplum，同时具备良好的兼容性和开放性，可无缝集成市场上主流ETL及BI工具。

Assist in the digital upgrade of intelligent manufacturing production lines

The storage and management of production line inspection data

Free to download

Assist in the digital upgrade of intelligent manufacturing production lines

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Enterprise content management

Help enterprise customers achieve unified storage, unified management and value mining of unstructured data across business systems, and improve the level of enterprise business management.

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Container cloud storage

Efficiently connect various cloud computing resources and reduce infrastructure management and operation costs.

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Machine Learning

Accelerate the deep integration of artificial intelligence and the real economy

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