diff --git a/fastlab/content/ailakehouse/discover-and-catalog.md b/fastlab/content/ailakehouse/discover-and-catalog.md new file mode 100644 index 00000000..ffcb2b0f --- /dev/null +++ b/fastlab/content/ailakehouse/discover-and-catalog.md @@ -0,0 +1,137 @@ +# 🔍 Discover and View your Database and Data Lake Assets from your Data Catalog + +## Introduction + +To build better loan products, SeersEquities’ data team must pull insights from everywhere—internal systems and external sources alike. But before they can transform that data, they need to find it. In this lab, you’ll step into that role. + +You’ll use Oracle Autonomous Database's Catalog Tool to **discover, connect to, and query data** stored in cloud object storage and databases. By the end, you’ll have a single view across your data landscape—so you can spend less time hunting for data and more time using it to power smarter decisions. + +Estimated Time: 15 minutes + +[Walkthrough video](videohub:1_vl8obrud:medium) + +### Objectives + +* **Discover and browse data assets** across Autonomous Database and cloud object storage using the Catalog Tool + +* Set up **secure connections** to access and unify data from multiple sources + +* Query and explore data from a unified catalog to support faster, insight-driven decisions + +## Task 1: Access Local Data Catalog | Database Actions + +1. Open the **Navigation Menu** by clicking the hamburger icon in the top-left corner. + ![OCI Console – Open Navigation Menu](./images/select-adb-open-navigation-menu.png "=50%x*") + +2. Navigate to Autonomous AI Database Landing Page. Click on **Oracle AI Database**, then select **Autonomous AI Database**. + ![OCI Console - Navigate to ADB](./images/select-adb-navigate-console-to-adb.png "=50%x*") + +3. **Choose Compartment** where your database resides and **select your database from list** of Autonomous AI Databases displayed + ![OCI Console – Select Compartment and your ADB](./images/select-adb-select-compartment-and-adb.png "=50%x*") + +4. On database details page select **Database Actions** button and click on **View all database actions** + ![OCI Console – Select Database Actions](./images/select-adb-select-database-actions.png "=50%x*") + + **This opens the Database Actions Launchpad**, ***where you can access the built-in web-based tools, including Data Studio.*** + ![OCI Console – Database Actions Launchpad](./images/select-adb-database-actions-launchpad.png "=50%x*") + +5. Click on **Data Studio**. + + ![Access Local Data Catalog](./images/access-local-data-catalog-3.png "Access Local Data Catalog") + +6. Click on **Catalog** on left rail for the **ADB Catalog** to be displayed. You should now see a list of objects contained in your local database. + + ![Access Local Data Catalog](./images/access-local-data-catalog-4.png "Access Local Data Catalog") + +>You’ve now unlocked a unified view of your data—no matter where it lives. With this foundation in place, you’re ready to explore, connect, and start building data products that drive real insights. + +## Task 2: Add Connections to Catalog for Object Storage Buckets & Data Shares + +🚀 Next, you’ll connect your catalog to a specific object storage bucket, so you can start exploring what’s inside. + +1. On the left rail of the Database Actions page, select **Data Load**. + + ![Add Connections to Catalog](./images/select-data-load.png "Add Connections to Catalog") + +2. Then select the **Connections** tile. + + ![Add Connections to Catalog](./images/add-connections-to-catalog-1.png "Add Connections to Catalog") + +3. On the connections page, create a connection to the **Object Storage** bucket called **LOANAPP\_LAB\_FILES**. On the Connections page, click **Create**, then choose **New Cloud Store Location**. + + ![Add Connections to Catalog](./images/add-connections-to-catalog-3.png "Add Connections to Catalog") + +4. On the **Add Cloud Store Location** page displayed: + + - For Name, enter: **LOANAPP\_LAB\_FILES** + - For Description, enter: **My Demo Object Storage Bucket** + - For Credential, select **Public Bucket** radio button + - Choose the **Select Bucket** radio button + - From the dropdown list choose the Bucket Named: **LOANAPP\_LAB\_FILES** + + ![Add Connections to Catalog](./images/add-connections-to-catalog-5.png "Add Connections to Catalog") + + Click the **Create** button to proceed. + +5. You’ve just added your own object storage connection to the Catalog, now you can easily discover, track, and query external data alongside your database assets. This step lays the foundation for building richer data products and speeding up access to the insights that matter. + + ![Add Connections to Catalog](./images/add-connections-to-catalog-6.png "Add Connections to Catalog") + +## Task 3: Search for items discovered by your Catalog + +With your connections in place, it’s time to search the catalog. You’ll choose which schemas and entity types to display—so you can quickly find the data that matters. + +1. On the left rail, select **Catalog**. + + ![Search Items in Catalog](./images/search-items-in-catalog-1.png "Search Items in Catalog") + +2. Select the **Connections** quick filter. This shows available connections in the catalog accessible to the connected schema. Confirm that **LOANAPP\_LAB\_FILES** appears in the list. + + >**Note**: On the landing page, the catalog offers quick filters to narrow your search results. If you don’t choose one, it defaults to **Tables and Views**. You can also limit results to all schemas or just a selected one. + + ![Search Items in Catalog](./images/search-items-in-catalog-2.png "Search Items in Catalog") + +3. Select the **Data Objects** quick filter to view items in the catalog visible to the connected schema, then, select **Cloud Objects** in the filter section to see a list of objects in our bucket. + + >**Note**: This view shows the database objects and object storage files available to you. + + ![Data Catalog Data Objects](./images/data-catalog-data-objects.png "Data Catalog Data Objects") + +By searching the Catalog with filters and schema selections, you’ve learned how to quickly surface the data that matters. This step helps you cut through clutter and find the right assets faster—so you can spend less time searching and more time building with data. + +## Task 4: Preview Data in Catalog + +Preview the data discovered in the catalog. From this view, you’ll see the files available in your object storage buckets. + +1. Click the preview icon for the file named **LoanAppCustomer_segment.csv**. + + ![Data Catalog Data Objects](./images/preview-icon.png "Data Catalog Data Objects") + +2. This displays the contents of the file being previewed without needing to first load it into the database. + + ![Data Catalog CSV preview](./images/data-catalog-csv-preview.png "Data Catalog CSV Preview") + + Click **Close** to return to Data Catalog listings + + +## Conclusion + +***Congratulations***, you have successfully completed the FastLab on working with Autonomous AI Lakehouse! In this lab, you discovered how to connect, browse, and preview data across your database and object storage—all from a single, unified catalog. You’ve built the foundation SeersEquities needs to turn scattered data into data products. + +With these skills, you can now help the team evaluate new data faster, integrate it seamlessly, and drive smarter, real-time decisions that power better loan products and risk strategies. + +## Signature Workshop + +👉 Click here to [Start the Advanced AI Lakehouse Workshop](https://livelabs.oracle.com/ords/r/dbpm/livelabs/view-workshop?wid=3689) + +This workshop contains labs that dive deeper into working with Oracle Autonomous AI Lakehouse. + +## Learn more +* [Oracle Autonomous AI Lakehouse](https://www.oracle.com/autonomous-database/autonomous-ai-lakehouse/) +* [Use Lakehouse with Autonomous AI Database](https://docs.oracle.com/en/cloud/paas/autonomous-database/serverless/adbsb/autonomous-lakehouse.html) +* [Lakehouse Workload with Autonomous AI Database](https://docs.oracle.com/en/cloud/paas/autonomous-database/serverless/adbsb/appendix-autonomous-database-data-warehouse-workload.html#GUID-604A08F8-8021-43CE-AA95-823045E14BD8) +* [Oracle Autonomous AI Lakehouse - Data Lake Accelerator](https://blogs.oracle.com/datawarehousing/introducing-data-lake-accelerator) +## Acknowledgements + +* **Authors** - Eddie Ambler, Linda Foinding, Database Product Management +* **Last Updated By/Date** - Linda Foinding, Database Product Management, December 2025 \ No newline at end of file diff --git a/fastlab/content/ailakehouse/images/access-local-data-catalog-1.png b/fastlab/content/ailakehouse/images/access-local-data-catalog-1.png new file mode 100644 index 00000000..37ffcb13 Binary files /dev/null and b/fastlab/content/ailakehouse/images/access-local-data-catalog-1.png differ diff --git a/fastlab/content/ailakehouse/images/access-local-data-catalog-2.png b/fastlab/content/ailakehouse/images/access-local-data-catalog-2.png new file mode 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+When a load job is enabled for live feed, it can be a scheduled job or connected to the OCI event notification and subscription mechanism, so that every time a new object is created in the object store bucket, it triggers the live feed, that then loads the contents to the database. + +Estimated Lab Time: 15 minutes + +[Walkthrough video](videohub:1_ijcbblxm:medium) + +### Objectives + +Leverage Data Studio Tools to Build a Live Feed Data Pipeline + * Explore using PL/SQL to interact with data in object storage + * Create a Live Feed table in Autonomous Database + * Simulate how new data loaded into object storage triggers Live Feed automation + +### Prerequisites + + This lab assumes you have: + * Completed Lab 1 --> Task 1 and 2 which creates the **Cloud Store Location named LOANAPP\_LAB\_FILES** + +## Task 1: Prepare to Build a Live Feed Data Pipeline: + + 1. Create a Private Oracle Object Storage Bucket called **MY\_DEMO\_BUCKET** with a directory called **FUNDING** to store your data. + + 2. Navigate back to the Oracle Cloud Console. + + 3. Open the **Navigation** menu in the Oracle Cloud console and click **Storage**. Under **Object Storage & Archive Storage**, click **Buckets**. + + 4. On the **Buckets** page, select the compartment where you want to create the bucket from the **Compartment** drop-down list in the **List Scope** section. Make sure you are in the region where you want to create your bucket. + + 5. Click **Create Bucket**. + + 6. In the **Create Bucket** panel, specify the following: + - **Bucket Name:** Enter **MY\_DEMO\_BUCKET**. + - **Default Storage Tier:** Accept the default **Standard** storage tier. Use this tier for storing frequently accessed data that requires fast and immediate access. For infrequent access, choose the **Archive** storage tier. + - **Encryption:** Accept the default **Encrypt using Oracle managed keys**. + + >**Note:** Bucket names must be unique per tenancy and region. + + 7. Click **Create** to create the bucket. + + ![The completed Create Bucket panel is displayed.](./images/create-bucket-panel.png " ") + + 8. The new bucket is displayed on the **Buckets** page. The default bucket type (visibility) is **Private**. + + ![The new bucket is displayed on the Buckets page.](./images/ll-bucket-created.png " ") + + +## Task 2: Move data from staged lab files bucket to Live Feed folder + + 1. Download the **funding_commitments1.json** file from LOANAPP\_LAB\_FILES + + 2. Upload the **funding_commitments1.json** file to the FUNDING folder under MY\_DEMO\_BUCKET + + 3. Validate files are visible in MY\_DEMO\_BUCKET. + + * Click the **Actions** icon in the **LOANAPP\_LAB\_FILES** panel, then select **Objects** from the context menu. + + ![Switch Tab & Select LOANAPP_FUNDING Connection](./images/move-data-file1.png "") + + 4. Expand the **FUNDING** folder icon to confirm that the **funding\_commitments1.json** file from the **LOANAPP\_FUNDING** bucket has been successfully copied here. + + ![Confirm File 1 Copy](./images/confirm-move-data-file1.png "") + + * Click **Close** to exit. + + ***Congratulations!*** You have now successfully interacted with data in object storage using PL/SQL from the Data Studio tools and your Autonomous Database. + +## Task 3: Build Initial Live Feed Table + +1. From the **Data Load | Oracle Database** tab - Navigate to Live Feed. + + * On Left rail expand **Data Load**, then click on **Live Feed**. + + ![Navigate from Data Load Connections to Live Feed](./images/navigate-connections-to-live-feed.png "") + + >You should now see the Live Feed Page + +2. Click the **Create Live Table Feed** button to enter the Create Live Feed wizard + + ![Create Live Feed Wizard](./images/live-feed-wizard-step1.png "") + +3. Enter details for the Live Table Feed Preview. + + * Select Cloud Store Location: **My\_Demo\_Bucket** + * Select Radial Box: **Basic** + * For Folders Select: **FUNDING** + * For Extensions Select: json + + ![Create Live Feed Wizard - step 1 - Data Source](./images/live-feed-wizard-step1-data-source.png "") + + >Note: that you should now see the funding commitments file matching the object filter and a preview of its content. + + * Click the **Next** button to proceed. + +4. Configure Live Feed Table Settings as follows: + + * **For Option**: Choose **Merge Into Table** from drop-down list + + * **For target Table Name**: Enter the name of the target table of the Live Feed -- **FUNDING\_PROVIDER\_OFFER\_STG**. ***In ALL CAPS***
+ + * Then modify **Mapping** details exactly as shown below: + * **Update Data Type** to NUMBER for: FUNDING\_PROVIDER\_ID and FUNDING\_OFFER\_REFERENCE\_ID
+ * **For Merge Key**: Select FUNDING\_PROVIDER\_ID and FUNDING\_OFFER\_REFERENCE\_ID
+ * **Unselect last row:** Inclusion of SYSTIMESTAMP Source + + ![Create Live Feed Wizard - step 2](./images/live-feed-wizard-step2-table-settings.png "") + + * Click the **Next** button to proceed. + +5. Review the information shown on the Preview page. + + ![Create Live Feed Wizard - step 3](./images/live-feed-preview.png "") + + * Click **Next** to proceed. + +6. Enter remaining details for the **Live Table Feed** + + a. Enter live feed name **LOANAPP\_FUNDING\_FEED**
+ b. Check box to **Enable for Scheduling**.
+ c. Select every **2 minutes** for the polling time interval + + ![Create Live Feed Wizard - step 4](./images/live-feed-wizard-step4.png "") + + * Click **Create** + +7. When the popup box appears, select **Yes** to run the Live Feed. + + ![Run Initial Live Table Feed](./images/do-run-live-feed.png) + +8. Review Live Feed Table and set page Refresh Interval + + * **You should see 3 rows loaded** + * **Set page refresh interval to 10 seconds** to see when new data is loaded + + ![Active Live Table Feed](./images/active-live-feed.png) + +***Congratulations!*** You have successfully created your Live Feed table. + + +## Task 4: Test Live Feed Table Data Population + +1. Download the **funding_commitments1.json** file from LOANAPP\_LAB\_FILES + +2. Upload the **funding_commitments1.json** file to the FUNDING folder under MY\_DEMO\_BUCKET + +3. Navigate to the **Data Load | Oracle Database** tab. + + * Review the details for the Live Table Feed. **Here we see that 4 new rows were loaded.** + >Remember that it may take up to 2 minutes to display the new data, as we have configured a 2 minute polling schedule for our Live Feed process. + + ![Review Live Feed Execution](./images/verify-live-feed-load-file2.png) + +4. Open SQL worksheet and query the staging table called **FUNDING\_PROVIDER\_OFFER\_STG**. + +***Congratulations!*** On creating a Live Feed that can automatically load data from object storage into your database and be integrated into an automated business process. + +## Conclusion +***Congratulations***, you have successfully completed the FastLab on working with Autonomous AI Lakehouse! You built a data pipeline using the Oracle Live Table Feed tool and successfully used the data from the pipeline in a PL/SQL block that was run from Oracle Autonomous Database to automate business processes. + +In this workshop, you’ve also seen how Oracle’s Data Share tool helps teams like SeersEquities’ Risk Department securely access loan data without needing to duplicate the data, or incur delays in data access due to manual handoffs between the teams. +Having dependable access to data ensures faster risk analysis, smarter decisions, and tighter collaboration across the business. +Oracle makes it easy to use and share data on it's data platform powered by Oracle Autonomous Database, that unlocks the value of governed, AI-ready data. + + +## Signature Workshop + +👉 Click here to [Start the Advanced AI Lakehouse Workshop](https://livelabs.oracle.com/ords/r/dbpm/livelabs/view-workshop?wid=3689) + +This workshop contains labs that dive deeper into working with Oracle Autonomous AI Lakehouse. + +## Learn more +* [Oracle Autonomous AI Lakehouse](https://www.oracle.com/autonomous-database/autonomous-ai-lakehouse/) +* [Use Lakehouse with Autonomous AI Database](https://docs.oracle.com/en/cloud/paas/autonomous-database/serverless/adbsb/autonomous-lakehouse.html) +* [Lakehouse Workload with Autonomous AI Database](https://docs.oracle.com/en/cloud/paas/autonomous-database/serverless/adbsb/appendix-autonomous-database-data-warehouse-workload.html#GUID-604A08F8-8021-43CE-AA95-823045E14BD8) +* [Oracle Autonomous AI Lakehouse - Data Lake Accelerator](https://blogs.oracle.com/datawarehousing/introducing-data-lake-accelerator) +## Acknowledgements + +* **Authors** - Eddie Ambler, Linda Foinding, Database Product Management +* **Last Updated By/Date** - Linda Foinding, Database Product Management, December 2025 \ No newline at end of file diff --git a/fastlab/content/ailakehouse/manifest.json b/fastlab/content/ailakehouse/manifest.json new file mode 100644 index 00000000..7b51a93f --- /dev/null +++ b/fastlab/content/ailakehouse/manifest.json @@ -0,0 +1,15 @@ +{ + "workshoptitle": "LiveLabs FastLab - Autonomous AI Lakehouse", + "help": "livelabs-help-database_us@oracle.com", + "tutorials": + [ + { + "title": "Lab 1: Discover and View your Data Assets from Autonomous AI Data Catalog", + "filename":"discover-and-catalog.md" + }, + { + "title": "Lab 2: Build a Data Pipeline: Load & Transform Data", + "filename":"load-transform.md" + } + ] +} \ No newline at end of file diff --git a/fastlab/content/graph/index.html b/fastlab/content/graph/index.html new file mode 100644 index 00000000..aebbdda4 --- /dev/null +++ b/fastlab/content/graph/index.html @@ -0,0 +1,63 @@ + + + + + + + + + Oracle LiveLabs + + + + + + + + + + + + + +
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You will go from beginner to confident user in a short time. + +Estimated Time: 15 minutes + +## FastLab Introduction + +This workshop focuses on working with property graphs in Oracle AI Database. You will create a graph from two tables, one containing bank account information, and another containing bank transactions information. You will then run graph pattern queries in SQL on this graph. You will find circular payment chains, multi-hop paths between accounts, and more. + +In Oracle AI Database the GRAPH_TABLE function and MATCH clause of the SQL:2023 standard enable you to write simple SQL queries to follow connections in data. This workshop illustrates how you can model your data as a graph and run graph queries in SQL to quickly see relationships in your data that are difficult to identify otherwise. + +In this 15-minute FastLab, you'll create a property graph from sample banking data and use SQL to query transfer paths, uncovering hidden connections like suspicious transaction chains. + +### Prerequisites + +- An Oracle Autonomous AI Database + + - Check our [LiveLabs FastLab - Create an Autonomous AI Database. Fast!](https://livelabs.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?wid=4276) + +## Task 1: Launch SQL Worksheet + +You will use Database Actions as your SQL workspace. This task opens SQL Worksheet so you can run the rest of the lab. + +1. In the ADB Console, click **Database Actions** and select **SQL**. + +## Task 2: Create Tables for Banking Data + +We'll create two relational tables: one for bank accounts (vertices) and one for transfers (edges). This simulates a simple banking scenario where accounts are connected via money transfers. + +1. Create the `bank_accounts` table to store account details. + + ```sql + + -- Task 2: Create bank_accounts table + CREATE TABLE bank_accounts ( + account_id NUMBER PRIMARY KEY, + name VARCHAR2(50), + address VARCHAR2(100) + ); + + ``` + +2. Create the `bank_transfers` table to store transfer relationships. + + ```sql + + -- Task 2: Create bank_transfers table + CREATE TABLE bank_transfers ( + transfer_id NUMBER PRIMARY KEY, + from_acct NUMBER, + to_acct NUMBER, + amount NUMBER, + transaction_date DATE, + FOREIGN KEY (from_acct) REFERENCES bank_accounts(account_id), + FOREIGN KEY (to_acct) REFERENCES bank_accounts(account_id) + ); + + ``` + +## Task 3: Insert Sample Data + +Populate the tables with sample accounts and transfers. We'll include "Russell Rivera" as in the use case, along with a chain of transfers to demonstrate path queries. + +1. Insert sample accounts. + + ```sql + + -- Task 3: Insert sample accounts + INSERT INTO bank_accounts VALUES (1, 'Russell Rivera', '123 Main St, Anytown'); + INSERT INTO bank_accounts VALUES (2, 'Alice Johnson', '456 Oak Ave, Anytown'); + INSERT INTO bank_accounts VALUES (3, 'Bob Smith', '789 Pine Rd, Anytown'); + INSERT INTO bank_accounts VALUES (4, 'Charlie Lee', '101 Elm St, Anytown'); + + ``` + +2. Insert sample transfers to create connections (e.g., Russell -> Alice -> Bob -> Charlie). + + ```sql + + -- Task 3: Insert sample transfers + INSERT INTO bank_transfers VALUES (101, 1, 2, 500, SYSDATE); -- Russell to Alice + INSERT INTO bank_transfers VALUES (102, 2, 3, 300, SYSDATE); -- Alice to Bob + INSERT INTO bank_transfers VALUES (103, 3, 4, 700, SYSDATE); -- Bob to Charlie + INSERT INTO bank_transfers VALUES (104, 1, 3, 1000, SYSDATE); -- Direct Russell to Bob + + ``` + +3. Verify the data. + + ```sql + + -- Task 3: Verify data + SELECT * FROM bank_accounts; + SELECT * FROM bank_transfers; + + ``` + +## Task 4: Create a Property Graph + +A Property Graph models entities (vertices) and relationships (edges). Here, accounts are vertices, and transfers are directed edges with properties like amount. + +1. Create the property graph `BANK_GRAPH` as a view on the relational tables. + + ```sql + + -- Task 4: Create Property Graph + CREATE PROPERTY GRAPH BANK_GRAPH + VERTEX TABLES ( + bank_accounts + KEY (account_id) + PROPERTIES (account_id, name, address) + ) + EDGE TABLES ( + bank_transfers + SOURCE KEY (from_acct) REFERENCES bank_accounts (account_id) + DESTINATION KEY (to_acct) REFERENCES bank_accounts (account_id) + KEY (transfer_id) + PROPERTIES (transfer_id, amount, transaction_date) + ); + + ``` + +## Task 5: Query the Graph to Uncover Connections + +Use SQL's `GRAPH_TABLE` to traverse the graph. This use case finds accounts connected to Russell Rivera via 1 to 3 transfer hops, helping detect potential fraud rings or influence networks. + +1. Simple query: Find direct transfers from any account. + + ```sql + + -- Task 5: Simple graph query - direct transfers + SELECT * + FROM GRAPH_TABLE (BANK_GRAPH + MATCH (s) -[e IS BANK_TRANSFERS]-> (d) + COLUMNS ( + VERTEX_ID(s) AS source_name, + VERTEX_ID(d) AS dest_name, + EDGE_ID(e).amount AS transfer_amount + ) + ); + + ``` + +2. Advanced query: Find accounts connected to Russell Rivera within 1-3 hops (as in the use case). + + ```sql + + -- Task 5: Find connections to Russell Rivera via 1-3 transfers + SELECT account_id1, account_id2 + FROM GRAPH_TABLE (BANK_GRAPH + MATCH (v1) -[IS BANK_TRANSFERS]->{1,3} (v2) + WHERE v1.name = 'Russell Rivera' + COLUMNS ( + v1.account_id AS account_id1, + v2.account_id AS account_id2 + ) + ); + + ``` + + This reveals indirect connections, such as paths through Alice or direct to Bob, in milliseconds—impossible with traditional SQL joins for deeper traversals. + +## Signature Workshop + +Ready to dive deeper? These workshops move you from demo to hands-on practice. + +👉 [Explore Operational Property Graphs in Oracle AI Database](https://livelabs.oracle.com/ords/r/dbpm/livelabs/view-workshop?wid=3978) + +## Learn More + +* [Oracle AI Database Graph Documentation](https://docs.oracle.com/en/database/oracle/oracle-database/23/spgdg/) +* [Property Graph Queries in SQL](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/GRAPH-TABLE.html) +* [Oracle Graph Analytics](https://www.oracle.com/database/technologies/graph-analytics.html) +* [Try Oracle Autonomous AI Database Free](https://www.oracle.com/autonomous-database/free-trial/) + +## Acknowledgements +* **Author** - Linda Foinding, Principal Product Manager, Database Product Management +* **Last Updated By/Date** - Linda Foinding, December 2025