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class=group><div class="container pt-14"><div style=position:relative;--progress-count:0%><div class="swiper-button-prev invisible group-hover:visible" style="position:absolute;top:calc(50% - 2rem);left:-3rem;z-index:99"></div><div class="swiper firstPageSwiper"><div class=swiper-wrapper><div class=swiper-slide><div class="row flex justify-center xl:justify-start flex-start pb-8 lg:pb-16"><div class="w-full lg:w-auto flex justify-center ml-4"><img width=424 src=/assets/images/compute-storage-decoupled-banner-59a38b98d30546418373b553045f8300.jpg alt="Slash your cost by 90% with Apache Doris Compute-Storage Decoupled Mode"></div><div class="lg:w-[48rem] px-6 lg:px-0 lg:ml-12 mt-4 lg:mt-0 flex flex-col"><div class="flex gap-1 mb-1"><div class="color-[#4c576c] font-medium text-xs leading-5">Tech Sharing</div></div><h3 class="leading-[38px] text-2xl font-semibold line-clamp-1">Slash your cost by 90% with Apache Doris Compute-Storage Decoupled Mode</h3><p class="pt-3 line-clamp-2 text-lg leading-8">Apache Doris compute-storage decoupled mode achieves 90% cost reduction and provides elasticity and workload isolation, while maintaining high performance in data ingestion and queries.</p><a class="flex group text-primary items-center cursor-pointer hover:no-underline pt-6" href=/blog/doris-compute-storage-decoupled><span class=mr-2>Read more</span><span class=transition-slide><svg class=transition-slide width=16 height=14 viewBox="0 0 16 14" fill=none><path d="M9.37549 12.3542L14.8755 6.85419L9.37549 1.35419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /><path d="M1.12549 6.85419L14.8755 6.85419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /></svg></span></a></div></div></div><div class=swiper-slide><div class="row flex justify-center xl:justify-start flex-start pb-8 lg:pb-16"><div class="w-full lg:w-auto flex justify-center ml-4"><img width=424 src=/assets/images/3.0.4-59ce56dd36630b3791025b270d8d7993.jpg alt="Apache Doris 3.0.4 Released"></div><div class="lg:w-[48rem] px-6 lg:px-0 lg:ml-12 mt-4 lg:mt-0 flex flex-col"><div class="flex gap-1 mb-1"><div class="color-[#4c576c] font-medium text-xs leading-5">Release Note</div></div><h3 class="leading-[38px] text-2xl font-semibold line-clamp-1">Apache Doris 3.0.4 Released</h3><p class="pt-3 line-clamp-2 text-lg leading-8">This version introduces continuous upgrades and enhancements in several key areas, including Lakehouse, Storage Efficiency, Compute-Storage Separation, Query Optimizer and Asynchronous Materialized Views, and more.</p><a class="flex group text-primary items-center cursor-pointer hover:no-underline pt-6"><span class=mr-2>Read more</span><span class=transition-slide><svg class=transition-slide width=16 height=14 viewBox="0 0 16 14" fill=none><path d="M9.37549 12.3542L14.8755 6.85419L9.37549 1.35419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /><path d="M1.12549 6.85419L14.8755 6.85419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /></svg></span></a></div></div></div><div class=swiper-slide><div class="row flex justify-center xl:justify-start flex-start pb-8 lg:pb-16"><div class="w-full lg:w-auto flex justify-center ml-4"><img width=424 src=/assets/images/ortege-1-08ee769ada597251fd61ae632666250e.jpg alt="Scaling Bitcoin data to billions of records with Apache Doris: our journey to auto-partitioning"></div><div class="lg:w-[48rem] px-6 lg:px-0 lg:ml-12 mt-4 lg:mt-0 flex flex-col"><div class="flex gap-1 mb-1"><div class="color-[#4c576c] font-medium text-xs leading-5">Best Practice</div></div><h3 class="leading-[38px] text-2xl font-semibold line-clamp-1">Scaling Bitcoin data to billions of records with Apache Doris: our journey to auto-partitioning</h3><p class="pt-3 line-clamp-2 text-lg leading-8">To power lightning-fast queries and ensure the Deep Dive dashboards deliver real-time insights, Ortege relies on Apache Doris. A crucial feature they embrace is Auto Partition.</p><a class="flex group text-primary items-center cursor-pointer hover:no-underline pt-6" href=/blog/ortege-studio-1-scaling-bitcoin-data-to-billions-of-records><span class=mr-2>Read more</span><span class=transition-slide><svg class=transition-slide width=16 height=14 viewBox="0 0 16 14" fill=none><path d="M9.37549 12.3542L14.8755 6.85419L9.37549 1.35419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /><path d="M1.12549 6.85419L14.8755 6.85419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /></svg></span></a></div></div></div><div class=swiper-slide><div class="row flex justify-center xl:justify-start flex-start pb-8 lg:pb-16"><div class="w-full lg:w-auto flex justify-center ml-4"><img width=424 src=/assets/images/ortege-2-e552980a5ac398a44b4ba300b79f7add.jpg alt="Fine-tuning Apache Doris for maximum performance and resilience: a deep dive into fe.conf"></div><div class="lg:w-[48rem] px-6 lg:px-0 lg:ml-12 mt-4 lg:mt-0 flex flex-col"><div class="flex gap-1 mb-1"><div class="color-[#4c576c] font-medium text-xs leading-5">Best Practice</div></div><h3 class="leading-[38px] text-2xl font-semibold line-clamp-1">Fine-tuning Apache Doris for maximum performance and resilience: a deep dive into fe.conf</h3><p class="pt-3 line-clamp-2 text-lg leading-8">Ortege handles massive volumes of blockchain data to power its analytics platform, Ortege Studio. Apache Doris forms the backbone of its Lakehouse v2, enabling it to process billions of records and deliver real-time insights.</p><a class="flex group text-primary items-center cursor-pointer hover:no-underline pt-6" href=/blog/ortege-studio-2-fine-tuning-apache-doris-for-maximum-performance-and-resilience><span class=mr-2>Read more</span><span class=transition-slide><svg class=transition-slide width=16 height=14 viewBox="0 0 16 14" fill=none><path d="M9.37549 12.3542L14.8755 6.85419L9.37549 1.35419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /><path d="M1.12549 6.85419L14.8755 6.85419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /></svg></span></a></div></div></div></div><div class=swiper-pagination></div></div><div class="swiper-button-next invisible group-hover:visible" style="position:absolute;top:calc(50% - 2rem);right:-3rem;z-index:99"></div></div></div></section><section class=apache-doris><div class="page-column lg:py-[6.25rem] py-16 lg:pb-[7.5rem] py-16 center has-footer"><h1 class=page-column-title><div class=container>What is Apache Doris</div></h1><div class=page-column-container><div class=container><img style=max-width:85%;margin:auto src=/assets/images/what-is-doris-new-eee52d097d0131115c46330ce57131b2.png alt=""></div></div><div class=page-column-footer><div class="justify-center flex mt-14"><a class="flex group text-primary items-center text-base cursor-pointer hover:no-underline undefined" href=/docs/gettingStarted/what-is-apache-doris><span class=mr-2>Learn more</span><span class=transition-slide><svg class=transition-slide width=1em height=1em viewBox="0 0 16 14" fill=none><path d="M9.37549 12.3542L14.8755 6.85419L9.37549 1.35419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /><path d="M1.12549 6.85419L14.8755 6.85419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /></svg></span></a></div></div></div></section><div style=background-color:#F7F9FE class="page-column lg:py-[6.25rem] py-16 lg:py-[7.5rem] center has-footer"><h1 class=page-column-title><div class=container>Core capabilities</div></h1><div class=page-column-container><div class=container><div class="block lg:grid core-capabilities-group grid-cols-1 lg:grid-cols-3"><div><div class=core-capabilities-icon><svg width=48 height=48 viewBox="0 0 48 48" fill=none><g clip-path=url(#clip0_1665_614)><path d="M26.9393 20.9393C27.5251 20.3536 28.4748 20.3536 29.0606 20.9393C29.6464 21.5251 29.6464 22.4749 29.0606 23.0607L25.0606 27.0607C24.4748 27.6464 23.5251 27.6464 22.9393 27.0607C22.3535 26.4749 22.3535 25.5251 22.9393 24.9393L26.9393 20.9393Z" fill=#444FD9 /><path d="M0 24C0 10.7452 10.7452 0 24 0C37.2548 0 48 10.7452 48 24C48 37.2548 37.2548 48 24 48C10.7452 48 0 37.2548 0 24ZM23.9999 15C17.6487 15 12.4999 20.1487 12.4999 26.5C12.4999 32.8513 17.6487 38 23.9999 38C30.3512 38 35.4999 32.8513 35.4999 26.5C35.4999 20.1487 30.3512 15 23.9999 15Z" fill=#444FD9 /><path d="M20 9.5C19.1716 9.5 18.5 10.1716 18.5 11C18.5 11.8284 19.1716 12.5 20 12.5H22.5001V15.0969C22.991 15.033 23.4916 15 24 15C24.5084 15 25.0091 15.033 25.5001 15.0969V12.5H28C28.8284 12.5 29.5 11.8284 29.5 11C29.5 10.1716 28.8284 9.5 28 9.5H20Z" fill=#C0C3F1 /><path d="M35.1546 14.0334C34.5649 13.4561 33.6195 13.4636 33.039 14.0502C32.4585 14.6369 32.461 15.5823 33.0446 16.1659L34.9393 18.0607C35.5251 18.6464 36.4749 18.6464 37.0607 18.0607C37.6464 17.4749 37.6464 16.5251 37.0607 15.9393L36.2598 15.1385C36.2154 15.0799 36.1662 15.0238 36.112 14.9708L35.1546 14.0334Z" fill=#C0C3F1 /></g><defs><clipPath id=clip0_1665_614><rect width=48 height=48 fill=white /></clipPath></defs></svg></div><div class=core-capabilities-card><div class=title>Real-time ingestion and storage</div><div class=content>Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation.</div></div></div><div><div class=core-capabilities-icon><svg width=48 height=48 viewBox="0 0 48 48" fill=none><path d="M24 48C37.2548 48 48 37.2548 48 24C48 10.7452 37.2548 0 24 0C10.7452 0 0 10.7452 0 24C0 37.2548 10.7452 48 24 48ZM22.6666 29.3337L18.6666 25.3337L11.9999 25.3337C11.9999 25.3337 12.7333 21.2937 14.6666 20.0004C16.8266 18.5604 21.3333 20.0004 21.3333 20.0004L21.1748 20.3173C21.2272 20.2336 21.28 20.1502 21.3333 20.067C22.8868 17.583 25.0501 15.5377 27.6173 14.1258C30.1844 12.7138 33.0702 11.9822 35.9999 12.0003C35.9999 15.627 34.9599 22.0003 27.9999 26.667C27.9999 26.667 29.4399 31.1737 27.9999 33.3337C26.7066 35.267 22.6666 36.0003 22.6666 36.0003L22.6666 29.3337Z" fill=#444FD9 /><path d="M14.9332 29.2177C12.9332 30.8977 12.2665 35.8844 12.2665 35.8844C12.2665 35.8844 17.2532 35.2177 18.9332 33.2177C19.8799 32.0977 19.8665 30.3777 18.8132 29.3377C18.2949 28.8431 17.6123 28.5572 16.8962 28.5351C16.1801 28.5129 15.481 28.756 14.9332 29.2177Z" fill=#DFE0F7 /></svg></div><div class=core-capabilities-card><div class=title>Lightning-fast query</div><div class=content>Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine.</div></div></div><div><div class=core-capabilities-icon><svg width=48 height=48 viewBox="0 0 48 48" fill=none><path d="M24 48C37.2548 48 48 37.2548 48 24C48 10.7452 37.2548 0 24 0C10.7452 0 0 10.7452 0 24C0 37.2548 10.7452 48 24 48ZM14.9999 13H28.9999C30.1045 13 30.9999 13.8954 30.9999 15V29C30.9999 30.1046 30.1045 31 28.9999 31H14.9999C13.8954 31 12.9999 30.1046 12.9999 29V15C12.9999 13.8954 13.8954 13 14.9999 13Z" fill=#444FD9 /><path d="M16.9999 31V33C16.9999 34.1046 17.8954 35 18.9999 35H32.9999C34.1045 35 34.9999 34.1046 34.9999 33V19C34.9999 17.8954 34.1045 17 32.9999 17H30.9999V29C30.9999 30.1046 30.1045 31 28.9999 31H16.9999Z" fill=#C0C3F1 /></svg></div><div class=core-capabilities-card><div class=title>Federated querying</div><div class=content>Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL.</div></div></div><div><div class=core-capabilities-icon><svg width=48 height=48 viewBox="0 0 48 48" fill=none><path fill-rule=evenodd clip-rule=evenodd d="M48 24C48 37.2548 37.2548 48 24 48C10.7452 48 0 37.2548 0 24C0 10.7452 10.7452 0 24 0C37.2548 0 48 10.7452 48 24ZM9.99994 15C9.99994 13.8954 10.8954 13 11.9999 13H35.9999C37.1045 13 37.9999 13.8954 37.9999 15V24H36.7852V19.4688H35.1562V24H34.7232L32.4551 19.4688H30.8203V24H29.5723V23.0723C29.5723 22.4551 29.498 21.916 29.3496 21.4551C29.2051 20.9902 28.9961 20.6035 28.7227 20.2949C28.4492 19.9824 28.125 19.748 27.75 19.5918C27.375 19.4316 26.959 19.3516 26.502 19.3516C26.0449 19.3516 25.6289 19.4316 25.2539 19.5918C24.8789 19.748 24.5547 19.9824 24.2812 20.2949C24.0078 20.6035 23.7988 20.9902 23.6543 21.4551C23.5098 21.916 23.4375 22.4551 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23.9985H34.7232L35.1563 24.8638V23.9985ZM25.1426 23.9985H27.8672V24.4946C27.8672 24.8853 27.838 25.2192 27.7794 25.4966C27.7208 25.7739 27.6348 26.0005 27.5215 26.1763C27.4083 26.3521 27.2657 26.4829 27.0938 26.5688C26.9258 26.6509 26.7325 26.6919 26.5137 26.6919C26.2911 26.6919 26.0938 26.6509 25.9219 26.5688C25.754 26.4829 25.6114 26.3521 25.4942 26.1763C25.377 26.0005 25.2891 25.7739 25.2305 25.4966C25.1719 25.2192 25.1426 24.8853 25.1426 24.4946V23.9985Z" fill=#DFE0F7 /></svg></div><div class=core-capabilities-card><div class=title>Semi-structured data</div><div class=content>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.</div></div></div><div><div class=core-capabilities-icon><svg width=48 height=48 viewBox="0 0 48 48" fill=none><path d="M24 48C37.2548 48 48 37.2548 48 24C48 10.7452 37.2548 0 24 0C10.7452 0 0 10.7452 0 24C0 37.2548 10.7452 48 24 48ZM12.8707 14.7307C14.6213 15.8848 18.5703 18 23.9999 18C29.4296 18 33.3786 15.8848 35.1292 14.7307C35.7319 14.3334 36.5033 14.3838 36.805 15.0396C37.3401 16.2026 37.9999 18.5408 37.9999 23C37.9999 27.4592 37.3401 29.7974 36.805 30.9604C36.5033 31.6162 35.7319 31.6666 35.1292 31.2693C33.3786 30.1152 29.4296 28 23.9999 28C18.5703 28 14.6213 30.1152 12.8707 31.2693C12.268 31.6666 11.4966 31.6162 11.1949 30.9604C10.6598 29.7974 9.99994 27.4592 9.99994 23C9.99994 18.5408 10.6598 16.2026 11.1949 15.0396C11.4966 14.3838 12.268 14.3334 12.8707 14.7307Z" fill=#444FD9 /><path d="M12.8708 13.7308C14.6214 14.8849 18.5704 17.0001 24 17.0001C29.4296 17.0001 33.3786 14.8849 35.1292 13.7308C35.7319 13.3335 36.5033 13.384 36.8051 14.0397C37.3401 15.2027 38 17.5409 38 22.0001C38 26.4593 37.3401 28.7975 36.8051 29.9605C36.5033 30.6163 35.7319 30.6667 35.1292 30.2694C33.3786 29.1153 29.4296 27.0001 24 27.0001C18.5704 27.0001 14.6214 29.1153 12.8708 30.2694C12.2681 30.6667 11.4967 30.6163 11.1949 29.9605C10.6599 28.7975 10 26.4593 10 22.0001C10 17.5409 10.6599 15.2027 11.1949 14.0397C11.4967 13.384 12.2681 13.3335 12.8708 13.7308Z" fill=white /><path d="M10.0007 22.25C10.0002 22.3327 10 22.416 10 22.5001C10 27.4153 11.2026 31.2568 12.0315 33.3208C12.3553 34.127 13.4296 34.2488 14.0796 33.6725C15.7504 32.1912 19.0572 30.0001 24 30.0001C28.9428 30.0001 32.2496 32.1912 33.9204 33.6725C34.5704 34.2488 35.6447 34.127 35.9685 33.3208C36.7974 31.2568 38 27.4153 38 22.5001C38 22.416 37.9998 22.3327 37.9993 22.25C37.9752 26.547 37.33 28.8194 36.8051 29.9605C36.5033 30.6163 35.7319 30.6667 35.1292 30.2694C33.3786 29.1153 29.4296 27.0001 24 27.0001C18.5704 27.0001 14.6214 29.1153 12.8708 30.2694C12.2681 30.6667 11.4967 30.6163 11.1949 29.9605C10.67 28.8194 10.0248 26.547 10.0007 22.25Z" fill=#C0C3F1 /></svg></div><div class=core-capabilities-card><div class=title>Elastic architecture</div><div class=content>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.</div></div></div><div><div class=core-capabilities-icon><svg width=48 height=48 viewBox="0 0 48 48" fill=none><path fill-rule=evenodd clip-rule=evenodd d="M48 24C48 37.2548 37.2548 48 24 48C10.7452 48 0 37.2548 0 24C0 10.7452 10.7452 0 24 0C37.2548 0 48 10.7452 48 24ZM24.2579 24.9953C24.1724 24.9984 24.0864 25 24.0001 25C20.1341 25 17.0001 21.866 17.0001 18C17.0001 14.134 20.1341 11 24.0001 11C27.8661 11 31.0001 14.134 31.0001 18C31.0001 19.4893 30.535 20.8699 29.7421 22.0047C29.8277 22.0016 29.9137 22 30 22C33.866 22 37 25.134 37 29C37 32.866 33.866 36 30 36C26.134 36 23 32.866 23 29C23 27.5107 23.4651 26.1301 24.2579 24.9953Z" fill=#444FD9 /><path fill-rule=evenodd clip-rule=evenodd d="M24 25.3924C23.3652 26.446 23 27.6803 23 29C23 30.3197 23.3652 31.554 24 32.6076C22.7751 34.6404 20.5463 36 18 36C14.134 36 11 32.866 11 29C11 25.134 14.134 22 18 22C20.5463 22 22.7751 23.3596 24 25.3924Z" fill=#C0C3F1 /></svg></div><div class=core-capabilities-card><div class=title>Open ecosystem</div><div class=content>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.</div></div></div></div></div></div><div class=page-column-footer></div></div><div class="page-column lg:py-[6.25rem] py-16 center"><h1 class=page-column-title><div class=container><div><p>Unified data warehouse<p>for various analytics use cases</div></div></h1><div class=page-column-container><div class=container><div class=cases-tabs><div class="ant-tabs ant-tabs-left css-1v613y0"><div role=tablist aria-orientation=vertical class=ant-tabs-nav><div class=ant-tabs-nav-wrap><div class=ant-tabs-nav-list style="transform:translate(0px, 0px)"><div data-node-key=0 class="ant-tabs-tab ant-tabs-tab-active"><div role=tab aria-selected=true class=ant-tabs-tab-btn tabindex=0><div class="font-misans text-start"><span>Real-time analytics</span><div class="absolute -bottom-0 w-full"><div class="h-0.5 w-[calc(100%-1.5rem)] bg-neutral-200 dark:bg-neutral-600 progress"><div class="h-0.5 bg-[#C0C3F1]" style=width:0%></div></div></div></div></div></div><div data-node-key=1 class=ant-tabs-tab><div role=tab aria-selected=false class=ant-tabs-tab-btn tabindex=-1><div class="font-misans text-start"><span>Ad-hoc analysis</span><div class="absolute -bottom-0 w-full"><div class="h-0.5 w-[calc(100%-1.5rem)] bg-neutral-200 dark:bg-neutral-600 progress"><div class="h-0.5 bg-[#C0C3F1]" style=width:0%></div></div></div></div></div></div><div data-node-key=2 class=ant-tabs-tab><div role=tab aria-selected=false class=ant-tabs-tab-btn tabindex=-1><div class="font-misans text-start"><span>Data lakehouse</span><div class="absolute -bottom-0 w-full"><div class="h-0.5 w-[calc(100%-1.5rem)] bg-neutral-200 dark:bg-neutral-600 progress"><div class="h-0.5 bg-[#C0C3F1]" style=width:0%></div></div></div></div></div></div><div data-node-key=3 class=ant-tabs-tab><div role=tab aria-selected=false class=ant-tabs-tab-btn tabindex=-1><div class="font-misans text-start"><span>ELT data processing</span><div class="absolute -bottom-0 w-full"><div class="h-0.5 w-[calc(100%-1.5rem)] bg-neutral-200 dark:bg-neutral-600 progress"><div class="h-0.5 bg-[#C0C3F1]" style=width:0%></div></div></div></div></div></div><div data-node-key=4 class=ant-tabs-tab><div role=tab aria-selected=false class=ant-tabs-tab-btn tabindex=-1><div class="font-misans text-start"><span>Log analytics</span><div class="absolute -bottom-0 w-full"><div class="h-0.5 w-[calc(100%-1.5rem)] bg-neutral-200 dark:bg-neutral-600 progress"><div class="h-0.5 bg-[#C0C3F1]" style=width:0%></div></div></div></div></div></div><div data-node-key=5 class=ant-tabs-tab><div role=tab aria-selected=false class=ant-tabs-tab-btn tabindex=-1><div class="font-misans text-start"><span>Customer data platform</span><div class="absolute -bottom-0 w-full"><div class="h-0.5 w-[calc(100%-1.5rem)] bg-neutral-200 dark:bg-neutral-600 progress"><div class="h-0.5 bg-[#C0C3F1]" style=width:0%></div></div></div></div></div></div><div class="ant-tabs-ink-bar ant-tabs-ink-bar-animated"></div></div></div><div class="ant-tabs-nav-operations ant-tabs-nav-operations-hidden"><button type=button class=ant-tabs-nav-more style=visibility:hidden;order:1 aria-haspopup=listbox aria-controls=null-more-popup id=null-more aria-expanded=false><span role=img aria-label=ellipsis class="anticon anticon-ellipsis"><svg viewBox="64 64 896 896" focusable=false data-icon=ellipsis width=1em height=1em fill=currentColor aria-hidden=true><path d="M176 511a56 56 0 10112 0 56 56 0 10-112 0zm280 0a56 56 0 10112 0 56 56 0 10-112 0zm280 0a56 56 0 10112 0 56 56 0 10-112 0z"/></svg></span></button></div></div><div class=ant-tabs-content-holder><div class="ant-tabs-content ant-tabs-content-left"><div role=tabpanel tabindex=0 aria-hidden=false class="ant-tabs-tabpane ant-tabs-tabpane-active"><div class="font-misans text-start h-full cases-bg-real-time py-14 pr-14 text-base leading-7"><div>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.</div><div class="flex mt-14 gap-10"><a class="flex group text-primary items-center text-base cursor-pointer hover:no-underline text-start" href=/blog/JD_OLAP><span class=mr-2>Read JD.com story </span><span class=transition-slide><svg class=transition-slide width=1em height=1em viewBox="0 0 16 14" fill=none><path d="M9.37549 12.3542L14.8755 6.85419L9.37549 1.35419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /><path d="M1.12549 6.85419L14.8755 6.85419" stroke=currentColor stroke-width=1.65 stroke-linecap=round stroke-linejoin=round /></svg></span></a></div></div></div></div></div></div></div></div></div></div><div class="page-column lg:py-[6.25rem] py-16 bg-[#F7F9FE] center"><h1 class=page-column-title><div class=container>Connect with community</div></h1><div class=page-column-container><div class=container><div class=communitys><div class=row><div class="col col--4"><a 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