Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Langkah 1: Buat beban kerja
Halaman ini memandu Anda melalui langkah awal penyiapan fondasi data,
inti dari Cortex Framework. Dibangun di atas penyimpanan BigQuery, fondasi data mengatur data masuk Anda dari berbagai sumber.
Data yang teratur ini menyederhanakan analisis dan penerapannya dalam pengembangan AI.
Menyiapkan integrasi data
Mulai dengan menentukan beberapa parameter utama yang akan berfungsi sebagai cetak biru untuk mengatur dan menggunakan data Anda secara efisien dalam Cortex Framework.
Ingat, parameter ini dapat bervariasi bergantung pada workload tertentu, alur data yang Anda pilih, dan mekanisme integrasi. Diagram berikut memberikan ringkasan integrasi data dalam Fondasi Data Cortex Framework:
Gambar 1. Cortex Framework Data Foundation: Ringkasan Integrasi Data.
Tentukan parameter berikut sebelum deployment untuk pemanfaatan data yang efisien dan efektif dalam Cortex Framework.
Project
Project sumber: Project tempat data mentah Anda berada. Anda memerlukan setidaknya satu project untuk menyimpan data dan menjalankan proses deployment. Google Cloud
Project target (opsional): Project tempat Cortex Framework Data Foundation
menyimpan model data yang diprosesnya. Project ini dapat sama dengan project sumber, atau berbeda, bergantung pada kebutuhan Anda.
Jika Anda ingin memiliki kumpulan project dan set data terpisah untuk setiap beban kerja (misalnya, satu set project sumber dan target untuk SAP dan set project target dan sumber yang berbeda untuk Salesforce), jalankan deployment terpisah untuk setiap beban kerja. Untuk mengetahui informasi selengkapnya, lihat
Menggunakan project yang berbeda untuk memisahkan akses
di bagian langkah opsional.
Model data
Deploy Model: Pilih apakah Anda perlu men-deploy model untuk semua beban kerja atau hanya satu set model (misalnya, SAP, Salesforce, dan Meta). Untuk
mengetahui informasi selengkapnya, lihat Sumber data dan workload yang tersedia.
Set data BigQuery
Set Data Sumber (Mentah): Set data BigQuery
tempat data sumber direplikasi atau tempat data pengujian dibuat.
Sebaiknya gunakan set data terpisah, satu untuk setiap sumber data. Misalnya, satu set data mentah untuk SAP dan satu set data mentah untuk Google Ads.
Set data ini termasuk dalam project sumber.
Set Data CDC: Set data BigQuery tempat data yang diproses CDC mendarat dengan data terbaru yang tersedia. Beberapa workload memungkinkan pemetaan nama kolom. Sebaiknya buat set data CDC terpisah untuk setiap sumber. Misalnya, satu set data CDC untuk SAP, dan
satu set data CDC untuk Salesforce. Set data ini termasuk dalam project sumber.
Set Data Pelaporan Target: Set data BigQuery tempat model data standar Data Foundation di-deploy.
Sebaiknya buat set data pelaporan terpisah untuk setiap sumber. Misalnya,
satu set data pelaporan untuk SAP dan satu set data pelaporan untuk Salesforce. Set data ini dibuat secara otomatis selama deployment jika belum ada. Set data ini termasuk dalam project Target.
Memproses Kumpulan Data K9: Kumpulan data BigQuery tempat komponen DAG lintas beban kerja yang dapat digunakan kembali, seperti dimensi time, dapat di-deploy. Beban kerja memiliki dependensi pada set data ini kecuali jika dimodifikasi. Set data ini dibuat secara otomatis selama deployment jika belum ada. Set data ini termasuk dalam project sumber.
Pemrosesan pasca-Kumpulan Data K9: Kumpulan data BigQuery tempat pelaporan lintas beban kerja dan DAG sumber eksternal tambahan (misalnya, penyerapan Google Trends) dapat di-deploy. Set data ini dibuat secara otomatis selama deployment jika belum ada. Set data ini termasuk dalam project Target.
Opsional: Buat contoh data
Cortex Framework dapat membuat contoh data dan tabel untuk Anda jika Anda tidak memiliki akses ke data Anda sendiri, atau alat replikasi untuk menyiapkan data, atau bahkan jika Anda hanya ingin melihat cara kerja Cortex Framework. Namun,
Anda tetap perlu membuat dan mengidentifikasi set data CDC dan Raw terlebih dahulu.
Buat set data BigQuery untuk data mentah dan CDC per sumber data,
dengan petunjuk berikut.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-18 UTC."],[[["\u003cp\u003eThis page details the initial step in setting up the Cortex Framework Data Foundation, which uses BigQuery to organize incoming data for analysis and AI development.\u003c/p\u003e\n"],["\u003cp\u003eBefore deploying, you must define key parameters, including source and target projects, which can be the same or different, and the specific data models to be deployed for various workloads such as SAP or Salesforce.\u003c/p\u003e\n"],["\u003cp\u003eMultiple BigQuery datasets are needed, including datasets for raw data, CDC processed data, target reporting, pre-processing, and post-processing, with recommendations to separate datasets per data source for better organization.\u003c/p\u003e\n"],["\u003cp\u003eCortex Framework offers the option to generate sample data if real data or replication tools are unavailable, but users must still pre-define the CDC and Raw datasets.\u003c/p\u003e\n"],["\u003cp\u003eThe steps to create BigQuery datasets via the console or command-line interface are provided, with instructions on specifying dataset location, name, and project association.\u003c/p\u003e\n"]]],[],null,["# Step 1: Establish workloads\n===========================\n\nThis page guides you through the initial step of setting up your data foundation,\nthe core of Cortex Framework. Built on top of BigQuery storage,\nthe data foundation organizes your incoming data from various sources.\nThis organized data simplifies analysis and its application in AI development.\n| **Note:** The steps outlined on this page are specifically designed for deploying Cortex Framework Data Foundation from the [official GitHub repository](https://github.com/GoogleCloudPlatform/cortex-data-foundation).\n\nSet up data integration\n-----------------------\n\nGet started by defining some key parameters to act as a blueprint for\norganizing and using your data efficiently within Cortex Framework.\nRemember, these parameters can vary depending on the specific workload, your\nchosen data flow, and the integration mechanism. The following diagram provides\nan overview of data integration within the Cortex Framework Data Foundation:\n\n**Figure 1**. Cortex Framework Data Foundation: Data Integration Overview.\n\nDefine the following parameters before deployment for efficient and effective\ndata utilization within Cortex Framework.\n\n### Projects\n\n- **Source project:** Project where your raw data lives. You need at least one Google Cloud project to store data and run the deployment process.\n- **Target project (optional):** Project where Cortex Framework Data Foundation stores its processed data models. This can be the same as the source project, or a different one depending on your needs.\n\nIf you want to have separate sets of projects and datasets for each workload\n(for example, one set of source and target projects for\nSAP and a different set of target and source projects for Salesforce),\nrun separate deployments for each workload. For more information, see\n[Using different projects to segregate access](/cortex/docs/optional-step-segregate-access)\nin the optional steps section.\n\n### Data model\n\n- **Deploy Models:** Choose whether you need to deploy models for all workloads or only one set of models (for example, SAP, Salesforce, and Meta). For more information, see available [Data sources and workloads](/cortex/docs/data-sources-and-workloads).\n\n### BigQuery datasets\n\n| **Note:** Some of these datasets might not be required and won't be present for some data sources.\n\n- **Source Dataset (Raw):** BigQuery dataset where the source data is replicated to or where the test data is created. The recommendation is to have separate datasets, one for each data source. For example, one raw dataset for SAP and one raw dataset for Google Ads. This dataset belongs to the source project.\n- **CDC Dataset:** BigQuery dataset where the CDC processed data lands the latest available records. Some workloads allow for field name mapping. The recommendation is to have a separate CDC dataset for each source. For example, one CDC dataset for SAP, and one CDC dataset for Salesforce. This dataset belongs to the source project.\n- **Target Reporting Dataset:** BigQuery dataset where the Data Foundation predefined data models are deployed. We recommend to have a separate reporting dataset for each source. For example, one reporting dataset for SAP and one reporting dataset for Salesforce. This dataset is automatically created during deployment if it doesn't exist. This dataset belongs to the Target project.\n- **Pre-processing K9 Dataset:** BigQuery dataset where cross-workload, reusable DAG components, such as `time` dimensions, can be deployed. Workloads have a dependency on this dataset unless modified. This dataset is automatically created during deployment if it doesn't exist. This dataset belongs to the source project.\n- **Post-processing K9 Dataset:** BigQuery dataset where cross-workload reporting, and additional external source DAGs (for example, Google Trends ingestion) can be deployed. This dataset is automatically created during deployment if it doesn't exist. This dataset belongs to the Target project.\n\n### Optional: Generate sample data\n\nCortex Framework can generate sample data and tables for you if\nyou don't have access to your own data, or replication tools to set up data, or\neven if you only want to see how Cortex Framework works. However,\nyou still need to create and identify the CDC and Raw datasets ahead of time.\n\nCreate BigQuery datasets for raw data and CDC per data source,\nwith the following instructions. \n\n### Console\n\n1. Open the BigQuery page in the Google Cloud console.\n\n [Go to the BigQuery page](https://console.cloud.google.com/bigquery)\n2. In the **Explorer** panel, select the project where you want to create\n the dataset.\n\n3. Expand the\n more_vert\n **Actions** option and click **Create dataset**:\n\n4. On the **Create dataset** page:\n\n - For **Dataset ID** , enter a unique dataset [name](/bigquery/docs/datasets#dataset-naming).\n - For **Location type** , choose a geographic [location](/bigquery/docs/locations)\n for the dataset. After a dataset is created, the\n location can't be changed.\n\n | **Note:** If you choose `EU` or an EU-based region for the dataset location, your Core Cortex Framework Customer Data resides in the EU. Core Cortex Framework Customer Data is defined in the [Service\n | Specific Terms](/terms/service-terms#13-google-bigquery-service).\n - **Optional** . For more customization details for your dataset, see\n [Create datasets: Console](/bigquery/docs/datasets#console).\n\n5. Click **Create dataset**.\n\n### BigQuery\n\n1. Create a new dataset for raw data by copying the following command:\n\n bq --location= \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e mk -d \u003cvar translate=\"no\"\u003eSOURCE_PROJECT\u003c/var\u003e: \u003cvar translate=\"no\"\u003eDATASET_RAW\u003c/var\u003e\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e with the dataset's [location](/bigquery/docs/locations).\n - \u003cvar translate=\"no\"\u003eSOURCE_PROJECT\u003c/var\u003e with your source project ID.\n - \u003cvar translate=\"no\"\u003eDATASET_RAW\u003c/var\u003e with the name for your dataset for raw data. For example, `CORTEX_SFDC_RAW`.\n2. Create a new dataset for CDC data by copying the following command:\n\n bq --location=\u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e mk -d \u003cvar translate=\"no\"\u003eSOURCE_PROJECT\u003c/var\u003e: \u003cvar translate=\"no\"\u003eDATASET_CDC\u003c/var\u003e\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e with the dataset's [location](/bigquery/docs/locations).\n - \u003cvar translate=\"no\"\u003eSOURCE_PROJECT\u003c/var\u003e with your source project ID.\n - \u003cvar translate=\"no\"\u003eDATASET_CDC\u003c/var\u003e with the name for your dataset for CDC data. For example, `CORTEX_SFDC_CDC`.\n3. Confirm that the datasets were created with the following command:\n\n bq ls\n\n4. **Optional** . For more information about creating datasets, see\n [Create datasets](/bigquery/docs/datasets#bq).\n\nNext steps\n----------\n\nAfter you complete this step, move on to the following deployment steps:\n\n1. [Establish workloads](/cortex/docs/deployment-step-one) (this page).\n2. [Clone repository](/cortex/docs/deployment-step-two).\n3. [Determine integration mechanism](/cortex/docs/deployment-step-three).\n4. [Set up components](/cortex/docs/deployment-step-four).\n5. [Configure deployment](/cortex/docs/deployment-step-five).\n6. [Execute deployment](/cortex/docs/deployment-step-six)."]]
