分析系統可透過查詢 API 或存取資料庫來從交易系統取得資料。在大多數企業中，分析與交易系統往往是彼此隔開並以鬆散的方式組合在一起。「分析混合/多雲端」模式的目標是在兩種不同的運算環境中執行交易和分析工作負載，以善加利用這個現有的分組。系統會先從私人運算環境中執行的工作負載擷取原始資料，然後將原始資料載入至Google Cloud，用於分析處理。接著，某些結果可能會傳回交易系統中。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2024-11-27 (世界標準時間)。"],[[["\u003cp\u003eThe analytics hybrid and multicloud pattern leverages the separation of transactional and analytics workloads, running them in distinct computing environments.\u003c/p\u003e\n"],["\u003cp\u003eRaw data is extracted from transactional systems in a private computing environment and loaded into Google Cloud for analytical processing, with some results potentially feeding back into transactional systems.\u003c/p\u003e\n"],["\u003cp\u003eGoogle Cloud offers numerous advantages for running analytics workloads, including cost-effective inbound data transfer, dynamic scalability for processing large datasets, and a comprehensive suite of data management services.\u003c/p\u003e\n"],["\u003cp\u003eImplementing this architecture involves best practices such as using handover networking, employing Pub/Sub or Cloud Storage for data transfer, and utilizing Cloud Data Fusion or Dataflow for building data pipelines.\u003c/p\u003e\n"],["\u003cp\u003eCross-Cloud Interconnect can be utilized to facilitate long-term, high-volume data transfer between Google Cloud and other cloud providers.\u003c/p\u003e\n"]]],[],null,["# Analytics hybrid and multicloud pattern\n\nThis document discusses that the objective of the analytics hybrid and multicloud pattern is to capitalize on the split between transactional and analytics workloads.\n\nIn enterprise systems, most workloads fall into these categories:\n\n- *Transactional* workloads include interactive applications like sales, financial processing, enterprise resource planning, or communication.\n- *Analytics* workloads include applications that transform, analyze, refine, or visualize data to aid decision-making processes.\n\nAnalytics systems obtain their data from transactional systems by either\nquerying APIs or accessing databases. In most enterprises, analytics and\ntransactional systems tend to be separate and loosely coupled. The objective of\nthe *analytics hybrid and multicloud* pattern is to capitalize on this\npre-existing split by running transactional and analytics workloads in two\ndifferent computing environments. Raw data is first extracted from workloads\nthat are running in the private computing environment and then loaded into\nGoogle Cloud, where it's used for analytical processing. Some of the results\nmight then be fed back to transactional systems.\n\nThe following diagram illustrates conceptually possible architectures by showing\npotential data pipelines. Each path/arrow represents a possible data movement\nand transformation pipeline option that can be based on\n[ETL](/learn/what-is-etl)\nor ELT, depending on the available\n[data quality](/dataplex/docs/auto-data-quality-overview)\nand targeted use case.\n\nTo move your data into Google Cloud and unlock value from it, use\n[data movement](/data-movement)\nservices, a complete suite of data ingestion, integration, and replication\nservices.\n\nAs shown in the preceding diagram, connecting Google Cloud with\non-premises environments and other cloud environments can enable various data\nanalytics use cases, such as data streaming and database backups. To power the\nfoundational transport of a hybrid and multicloud analytics pattern that\nrequires a high volume of data transfer, Cloud Interconnect and\n[Cross-Cloud Interconnect](/network-connectivity/docs/interconnect/concepts/cci-overview)\nprovide dedicated connectivity to on-premises and other cloud providers.\n\nAdvantages\n----------\n\nRunning analytics workloads in the cloud has several key advantages:\n\n- Inbound traffic---moving data from your private computing environment or other clouds to Google Cloud---[might be free of charge](/vpc/network-pricing#general).\n- Analytics workloads often need to process substantial amounts of data and can be bursty, so they're especially well suited to being deployed in a public cloud environment. By dynamically scaling compute resources, you can quickly process large datasets while avoiding upfront investments or having to overprovision computing equipment.\n- Google Cloud provides a rich set of services to manage data throughout its entire lifecycle, ranging from initial acquisition through processing and analyzing to final visualization.\n - Data movement services on Google Cloud provide a complete suite of products to move, integrate, and transform data seamlessly in different ways.\n - Cloud Storage is well suited for [building a data lake](https://cloud.google.com/blog/topics/developers-practitioners/architect-your-data-lake-google-cloud-data-fusion-and-composer).\n- Google Cloud helps you to modernize and optimize your data\n platform to break down data silos. Using a\n [data lakehouse](/discover/what-is-a-data-lakehouse#section-3)\n helps to standardize across different storage formats. It can also provide\n the flexibility, scalability, and agility needed to help ensure that your\n data generates value for your business, rather than inefficiencies. For\n more information, see\n [BigLake](/biglake).\n\n- [BigQuery Omni,](/bigquery/docs/omni-introduction)\n provides compute power that runs locally to the storage on AWS or Azure. It\n also helps you query your own data stored in Amazon Simple Storage Service\n (Amazon S3) or Azure Blob Storage. This multicloud analytics capability\n lets data teams break down data silos. For more information about querying\n data stored outside of BigQuery, see\n [Introduction to external data sources](/bigquery/docs/external-data-sources).\n\nBest practices\n--------------\n\nTo implement the *analytics hybrid and multicloud* architecture pattern,\nconsider the following general best practices:\n\n- Use the [handover networking pattern](/architecture/hybrid-multicloud-secure-networking-patterns/handover-pattern) to enable the ingestion of data. If analytical results need to be fed back to transactional systems, you might combine both the handover and the [*gated egress*](/architecture/hybrid-multicloud-secure-networking-patterns/gated-egress) pattern.\n- Use [Pub/Sub](/pubsub) queues or [Cloud Storage](/storage) buckets to hand over data to Google Cloud from transactional systems that are running in your private computing environment. These queues or buckets can then serve as sources for data-processing pipelines and workloads.\n- To deploy ETL and ELT data pipelines, consider using [Cloud Data Fusion](/data-fusion) or [Dataflow](/dataflow) depending on your specific use case requirements. Both are fully managed, cloud-first data processing services for building and managing data pipelines.\n- To discover, classify, and protect your valuable data assets, consider using Google Cloud [Sensitive Data Protection](/sensitive-data-protection) capabilities, like [de-identification techniques](/sensitive-data-protection/docs/deidentify-sensitive-data). These techniques let you mask, encrypt, and replace sensitive data---like personally identifiable information (PII)---using a randomly generated or pre-determined key, where applicable and compliant.\n- When you're performing an initial data transfer from your private\n computing environment to Google Cloud, choose the transfer approach\n that is best suited for your dataset size and available bandwidth. For more\n information, see\n [Migration to Google Cloud: Transferring your large datasets](/architecture/migration-to-google-cloud-transferring-your-large-datasets).\n\n- If data transfer or exchange between Google Cloud and other clouds\n is required for the long term with high traffic volume, you should evaluate\n using Google Cloud\n [Cross-Cloud Interconnect](/network-connectivity/docs/interconnect/concepts/cci-overview)\n to help you establish high-bandwidth dedicated connectivity between\n Google Cloud and other cloud service providers (available in certain\n [locations](/network-connectivity/docs/interconnect/concepts/cci-overview#locations)).\n\n- If encryption is required at the connectivity layer, various options are\n available based on the selected hybrid connectivity solution. These options\n include VPN tunnels, HA VPN over Cloud Interconnect, and\n [MACsec for Cross-Cloud Interconnect](/network-connectivity/docs/interconnect/concepts/cci-overview#encryption).\n\n- Use consistent tooling and processes across environments. In an\n analytics hybrid scenario, this practice can help increase operational\n efficiency, although it's not a prerequisite."]]
