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to Snowflake with Dynamic Tables and Streamlit.","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/CDC_connectors_banner.png?v=32b758de\" alt=\"assets/CDC_connectors_banner.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this Quickstart, we will investigate how a financial company builds a BI dashboard using customer transactional data housed on a PostgreSQL database. The data is brought into Snowflake via the Snowflake Connector for PostgreSQL. The main idea is gain insights on potential ways to increase customer spending with promotions.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EVisualize customer data and gain insights ingesting data from PostgreSQL DB to Snowflake using the Snowflake Connector for PostgreSQL Native App and Dynamic Tables\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to connect PostgreSQL data to Snowflake using the \u003Ca href=\"https://other-docs.snowflake.com/en/connectors/postgres6/about\"\u003ESnowflake Connector for PostgreSQL\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHow to visualize data using \u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-about\"\u003EDynamic Tables\u003C/a\u003E and display visualizations within \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake (SiS)\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://www.docker.com/products/docker-desktop/\"\u003EDocker\u003C/a\u003E installed on your local machine\u003C/li\u003E\u003Cli\u003EA tool available for connecting to the PostgreSQL database\n\u003Cul\u003E\u003Cli\u003EThis can be a database-specific tool or a general-purpose tool such as Visual Studio Code or PyCharm\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EFamiliarity with basic Python and SQL\u003C/li\u003E\u003Cli\u003EFamiliarity with data science notebooks\u003C/li\u003E\u003Cli\u003EGo to the \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E sign-up page and register for a free account. After registration, you will receive an email containing a link that will take you to Snowflake, where you can sign in.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Environment\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EYou will use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E, the Snowflake web interface to create Snowflake objects (warehouse, database, schema, role).\u003C/p\u003E\n","\u003Ch4\u003ECreating Objects and Loading Data\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to Worksheets, click \u003Ccode\u003E+\u003C/code\u003E in the top-right corner to create a new Worksheet, and choose \u003Cstrong\u003ESQL Worksheet\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy and paste the following code to create Snowflake objects (warehouse, database, schema) and click \u003Cstrong\u003ERun All\u003C/strong\u003E at the top of the Worksheet\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE accountadmin;\n\n/*---------------------------*/\n-- Create our Database\n/*---------------------------*/\nCREATE OR REPLACE DATABASE cdc_prod;\n\n/*---------------------------*/\n-- Create our Schema\n/*---------------------------*/\nCREATE OR REPLACE SCHEMA cdc_prod.analytics;\n\n/*---------------------------*/\n-- Create our Warehouse\n/*---------------------------*/\n\n-- data science warehouse\nCREATE OR REPLACE WAREHOUSE cdc_ds_wh\n   WAREHOUSE_SIZE = 'xsmall'\n   WAREHOUSE_TYPE = 'standard'\n   AUTO_SUSPEND = 60\n   AUTO_RESUME = TRUE\n   INITIALLY_SUSPENDED = TRUE\n   COMMENT = 'data science warehouse for cdc';\n\n-- Use our Warehouse\nUSE WAREHOUSE cdc_ds_wh;\n/*---------------------------*/\n-- sql completion note\n/*---------------------------*/\nSELECT 'cdc sql is now complete' AS note;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPostgreSQL Environment\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this section, we will set up a PostgreSQL database and create tables to simulate a financial company's customer transactional data.\u003C/p\u003E\n","\u003Ch4\u003EStarting the Database Instance\u003C/h4\u003E\n","\u003Cp\u003EBefore getting started with this step, make sure that you have Docker Desktop installed for either \u003Ca href=\"https://docs.docker.com/desktop/install/mac-install/\"\u003EMac\u003C/a\u003E, \u003Ca href=\"https://docs.docker.com/desktop/install/windows-install/\"\u003EWindows\u003C/a\u003E, or \u003Ca href=\"https://docs.docker.com/desktop/install/linux/\"\u003ELinux\u003C/a\u003E. Ensure that you have \u003Ca href=\"https://docs.docker.com/compose/install/\"\u003EDocker Compose\u003C/a\u003E installed on your machine.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ETo initiate the PostgreSQL database using Docker, you'll need to create a file called \u003Cstrong\u003Edocker-compose.yaml\u003C/strong\u003E. This file will contain the configuration for the PostgreSQL database. If you have another container client, spin up the container and use the PostgreSQL image below.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen the IDE of your choice to copy and paste this file by copy and pasting the following:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Eservices:\n  postgres:\n    image: "postgres:17"\n    container_name: "postgres17"\n    environment:\n      POSTGRES_DB: 'postgres'\n      POSTGRES_USER: 'postgres'\n      POSTGRES_PASSWORD: 'postgres'\n    ports:\n      - "5432:5432"\n    command:\n      - "postgres"\n      - "-c"\n      - "wal_level=logical"\n    volumes:\n      - ./postgres-data:/var/lib/postgresql/data\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EOpen a terminal and navigate to the directory where the \u003Cstrong\u003Edocker-compose.yaml\u003C/strong\u003E file is located. Run the following command to start the PostgreSQL database:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Edocker-compose up -d\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter running this command, you should see one Docker container actively running the source database.\u003C/p\u003E\n","\u003Ch4\u003EConnecting to the Database\u003C/h4\u003E\n","\u003Cp\u003ETo connect to the pre-configured databases using Visual Studio Code or PyCharm, or whichever IDE you choose for a database connection, perform the following steps with the provided credentials:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen your tool of choice for connecting to the PostgreSQL database\n\u003Cul\u003E\u003Cli\u003EFor VSCode, you can use the \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=cweijan.vscode-postgresql-client2\"\u003EPostgreSQL extension\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EFor PyCharm, you can use the \u003Ca href=\"https://www.jetbrains.com/help/pycharm/database-tool-window.html\"\u003EDatabase Tools and SQL plugin\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick the \u003Ccode\u003E+\u003C/code\u003E sign or similar to add data source\u003C/li\u003E\u003Cli\u003EUse these connection parameters:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EUser:\u003C/strong\u003E \u003Ccode\u003Epostgres\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPassword:\u003C/strong\u003E \u003Ccode\u003Epostgres\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EURL:\u003C/strong\u003E \u003Ccode\u003Ejdbc:postgresql://localhost:5432/\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003ETest the connection and save\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003ELoading Data\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ERun the following \u003Cstrong\u003Epostgres\u003C/strong\u003E script in the PostgreSQL to create the database, schema, and tables:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE SCHEMA raw_cdc;\nSET search_path TO raw_cdc;\n\nDROP TABLE IF EXISTS postgres.raw_cdc.customers;\nDROP TABLE IF EXISTS postgres.raw_cdc.merchants;\nDROP TABLE IF EXISTS postgres.raw_cdc.products;\nDROP TABLE IF EXISTS postgres.raw_cdc.transactions;\n\nCREATE TABLE postgres.raw_cdc.customers (\n   customer_id INTEGER PRIMARY KEY,\n   firstname VARCHAR,\n   lastname VARCHAR,\n   age INTEGER,\n   email VARCHAR,\n   phone_number VARCHAR\n);\n\nCREATE TABLE postgres.raw_cdc.merchants (\n   merchant_id integer PRIMARY KEY,\n   merchant_name VARCHAR,\n   merchant_category VARCHAR\n);\n\nCREATE TABLE postgres.raw_cdc.products (\n   product_id INTEGER PRIMARY KEY,\n   product_name VARCHAR,\n   product_category VARCHAR,\n   price DOUBLE PRECISION\n);\n\nCREATE TABLE postgres.raw_cdc.transactions (\n   transaction_id VARCHAR PRIMARY KEY,\n   customer_id INTEGER,\n   product_id INTEGER,\n   merchant_id INTEGER,\n   transaction_date DATE,\n   transaction_time VARCHAR,\n   quantity INTEGER,\n   total_price DOUBLE PRECISION,\n   transaction_card VARCHAR,\n   transaction_category VARCHAR\n);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload and save these csv files in a directory on your local machine:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/customers.csv\"\u003Ecustomers.csv\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/merchants.csv\"\u003Emerchants.csv\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/products.csv\"\u003Eproducts.csv\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/transactions.csv\"\u003Etransactions.csv\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWe'll need to move the files from the local computer to the Docker container before loading the data into the PostgreSQL database.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to your terminal to get the Docker container ID with this command:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Edocker ps\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ETo copy the CSV files to the container, run these commands in your terminal, replacing the file path with your actual file path,m and replacing \u003Ccode\u003Econtainer_id\u003C/code\u003E with your actual container ID from the previous command:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Edocker cp /Users/your_username/Downloads/customers.csv container_id:/tmp/customers.csv\ndocker cp /Users/your_username/Downloads/merchants.csv container_id:/tmp/merchants.csv\ndocker cp /Users/your_username/Downloads/products.csv container_id:/tmp/products.csv\ndocker cp /Users/your_username/Downloads/transactions.csv container_id:/tmp/transactions.csv\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EBack in your PostgreSQL console, run these SQL commands to load the files from the container to the PostgreSQL tables:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003ECOPY postgres.raw_cdc.customers FROM '/tmp/customers.csv' DELIMITER ',' CSV HEADER;\nCOPY postgres.raw_cdc.merchants FROM '/tmp/merchants.csv' DELIMITER ',' CSV HEADER;\nCOPY postgres.raw_cdc.products FROM '/tmp/products.csv' DELIMITER ',' CSV HEADER;\nCOPY postgres.raw_cdc.transactions FROM '/tmp/transactions.csv' DELIMITER ',' CSV HEADER;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003ENext, make sure to run the \u003Ccode\u003ECREATE PUBLICATION\u003C/code\u003E command to enable the logical replication for the tables in the \u003Ccode\u003Eraw_cdc\u003C/code\u003E schema. This will allow the Snowflake Connector for PostgreSQL to capture the changes made to the tables in the PostgreSQL database:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE PUBLICATION agent_postgres_publication FOR ALL TABLES;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003ELastly, check that the tables have been loaded correctly by running the following SQL commands:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * FROM postgres.raw_cdc.customers;\nSELECT * FROM postgres.raw_cdc.merchants;\nSELECT * FROM postgres.raw_cdc.products;\nSELECT * FROM postgres.raw_cdc.transactions;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Connector\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EDuring this step, you will install and configure the Snowflake Connector for PostgreSQL Native App to capture changes made to the PostgreSQL database tables.\u003C/p\u003E\n","\u003Ch4\u003EInstall the Snowflake Connector for PostgreSQL Native App\u003C/h4\u003E\n","\u003Cp\u003ENavigate to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to the \u003Cstrong\u003EData Products\u003C/strong\u003E then to the \u003Cstrong\u003EMarketplace\u003C/strong\u003E section\u003C/li\u003E\u003Cli\u003ESearch for the \u003Cstrong\u003ESnowflake Connector for PostgreSQL\u003C/strong\u003E Native App and install the application\u003C/li\u003E\u003Cli\u003EYou should find your installed Native App under \u003Cstrong\u003EData Products\u003C/strong\u003E, \u003Cstrong\u003EApps\u003C/strong\u003E section\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EConfigure the Snowflake Connector for PostgreSQL Native App\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EOn your Snowflake Account, navigate to the \u003Cstrong\u003EData Products\u003C/strong\u003E, \u003Cstrong\u003EApps\u003C/strong\u003E section\u003C/li\u003E\u003Cli\u003EOpen the application\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EMark all as done\u003C/strong\u003E as we will create our source databases from scratch.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/mark_all_done.png?v=32b758de\" alt=\"assets/mark_all_done.png\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EClick \u003Cstrong\u003EStart configuration\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EIf you have Event Tables already activated in your account, the \u003Cstrong\u003EEvent Log Database\u003C/strong\u003E, \u003Cstrong\u003EEvent Log Schema\u003C/strong\u003E, and \u003Cstrong\u003EEvent Table\u003C/strong\u003E will populate automatically with what is active. The names of the \u003Cstrong\u003EEvent Log Database\u003C/strong\u003E, \u003Cstrong\u003EEvent Log Schema\u003C/strong\u003E, and \u003Cstrong\u003EEvent Table\u003C/strong\u003E could be slightly different from what is shown.\u003C/li\u003E\u003Cli\u003EOn the \u003Cstrong\u003EConfigure Connector\u003C/strong\u003E screen, select \u003Cstrong\u003EConfigure\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/configure_connector.png?v=32b758de\" alt=\"assets/configure_connector.png\"\u003E\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EOn the \u003Cstrong\u003EVerify Agent Connection\u003C/strong\u003E screen select \u003Cstrong\u003EGenerate file\u003C/strong\u003E to download the Agent Configuration file. The downloaded file name should resemble \u003Cstrong\u003Esnowflake.json\u003C/strong\u003E. Save this file for use during the Agent configuration section.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/generate_file.png?v=32b758de\" alt=\"assets/generate_file.png\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAgent Configuration\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EDuring this section, you will configure the Agent that will operate alongside our Source Databases.\u003C/p\u003E\n","\u003Ch3\u003EConfigure the Agents\u003C/h3\u003E\n","\u003Cp\u003EThe first step is to create the \u003Cstrong\u003Eagent-postgresql\u003C/strong\u003E directory. In this directory, you will create 2 directories named \u003Cstrong\u003Eagent-keys\u003C/strong\u003E and \u003Cstrong\u003Econfiguration\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch4\u003ECreating Configuration Files\u003C/h4\u003E\n","\u003Cp\u003EYou will fill the configuration files for each agent to operate correctly. The configuration files include \u003Cstrong\u003Esnowflake.json\u003C/strong\u003E file to connect to Snowflake, \u003Cstrong\u003Edatasources.json\u003C/strong\u003E file to connect to the Source Databases, and \u003Cstrong\u003Epostgresql.conf\u003C/strong\u003E file with additional Agent Environment Variables.\u003C/p\u003E\n","\u003Cp\u003EHere's how the file structure should look like in the beginning:\u003C/p\u003E\n","\u003Cp\u003EDirectory Structure\u003C/p\u003E\n","\u003Cp\u003E<ul>\n<li>agent-postgresql\n<ul>\n<li>agent-keys\n</li>\n<li>configuration\n<ul>\n<li>datasources.json</li>\n<li>postgresql.conf</li>\n<li>snowflake.json</li>\n</ul>\n</li>\n<li>docker-compose.yaml</li>\n</ul>\n</li>\n</ul>\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to the directory called \u003Cstrong\u003Eagent-postgresql\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECreate the docker-compose file named \u003Cstrong\u003Edocker-compose.yaml\u003C/strong\u003E in the \u003Cstrong\u003Eagent-postgresql\u003C/strong\u003E directory with the following content:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Eservices:\n  postgresql-agent:\n    container_name: postgresql-agent\n    image: snowflakedb/database-connector-agent:latest\n    volumes:\n      - ./agent-keys:/home/agent/.ssh\n      - ./configuration/snowflake.json:/home/agent/snowflake.json\n      - ./configuration/datasources.json:/home/agent/datasources.json\n    env_file:\n      - configuration/postgresql.conf\n    mem_limit: 6g\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EPut the previously downloaded \u003Cstrong\u003Esnowflake.json\u003C/strong\u003E file in the \u003Cstrong\u003Econfiguration\u003C/strong\u003E directory folder\u003C/li\u003E\u003Cli\u003ECreate the file named \u003Cstrong\u003Edatasources.json\u003C/strong\u003E in the \u003Cstrong\u003Econfiguration\u003C/strong\u003E directory with the following content:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E{\n  "PSQLDS1": {\n    "url": "jdbc:postgresql://host.docker.internal:5432/postgres",\n    "username": "postgres",\n    "password": "postgres",\n    "publication": "agent_postgres_publication",\n    "ssl": false\n  }\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ECreate the file named \u003Cstrong\u003Epostgresql.conf\u003C/strong\u003E in the \u003Cstrong\u003Econfiguration\u003C/strong\u003E directory with the following content:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003EJAVA_OPTS=-Xmx5g\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ENavigating to the \u003Cstrong\u003Eagent-postgresql\u003C/strong\u003E directory in your terminal, start the agent using the following command. The agent should generate public/private key for authorization to Snowflake.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Edocker-compose up -d\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter running the \u003Ccode\u003Edocker-compose up -d\u003C/code\u003E command, you will see in your file structure that the \u003Cstrong\u003Eagent-keys\u003C/strong\u003E directory has been populated with the private and public keys. At the end, your directory structure should resemble the following.\u003C/p\u003E\n","\u003Cp\u003EDirectory Structure\u003C/p\u003E\n","\u003Cp\u003E<ul>\n<li>agent-postgresql\n<ul>\n<li>agent-keys\n<ul>\n<li>database-connector-agent-app-private-key.p8</li>\n<li>database-connector-agent-app-public-key.pub</li>\n</ul>\n</li>\n<li>configuration\n<ul>\n<li>datasources.json</li>\n<li>postgresql.conf</li>\n<li>snowflake.json</li>\n</ul>\n</li>\n<li>docker-compose.yaml</li>\n</ul>\n</li>\n</ul>\u003C/p\u003E\n","\u003Ch4\u003EVerifying Connection with Snowflake\u003C/h4\u003E\n","\u003Cp\u003ENavigate to Snowsight to your previously created Snowflake Connector for PostgreSQL Native App. Click on the \u003Cstrong\u003ERefresh\u003C/strong\u003E button in the Agent Connection Section. When successfully configured, you should see the "Successfully configured" message. Click "Define data to sync".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/successfully_configured.png?v=32b758de\" alt=\"assets/successfully_configured.png\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EReplication Process\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this step, we will instruct the Connector to begin replicating the selected tables.\u003C/p\u003E\n","\u003Ch3\u003EConfigure Data Ingestion\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EChange the role to \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EDownload the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/notebooks/0_start_here.ipynb\"\u003ESnowflake Notebook\u003C/a\u003E and import it into Snowflake by navigating to Snowsight and going to \u003Cstrong\u003ENotebooks\u003C/strong\u003E and to using the \u003Ccode\u003EImport .ipynb file\u003C/code\u003E button.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003ECDC_PROD\u003C/strong\u003E for the database, \u003Cstrong\u003EANALYTICS\u003C/strong\u003E for the schema, and \u003Cstrong\u003ECDC_DS_WH\u003C/strong\u003E for the warehouse. This Notebook includes the SQL scripts needed to create the destination database for table replication of the PostgreSQL tables into Snowflake and monitor the replication process.\u003C/li\u003E\u003Cli\u003ERun the first 3 cells in the Notebook labeled \u003Cstrong\u003Ecreate_db_objects\u003C/strong\u003E, \u003Cstrong\u003Etable_replication\u003C/strong\u003E, and \u003Cstrong\u003Echeck_replication_state\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ERun the cell labeled \u003Cstrong\u003Echeck_replication_state\u003C/strong\u003E until the output indicates successful replication as indicated in the Notebook.\u003C/li\u003E\u003Cli\u003EOnce the replication process is complete, you can run the rest of the Notebook.\u003C/li\u003E\u003Cli\u003ENotice the Dynamic Table, \u003Cstrong\u003Ecdc_prod.analytics.customer_purchase_summary\u003C/strong\u003E, is created in the last cell labeled \u003Cstrong\u003Ecreate_dynamic_table\u003C/strong\u003E. This table will be used to visualize the data in the \u003Cstrong\u003ECustomer Spending Dashboard\u003C/strong\u003E Streamlit app.\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStreamlit App\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this section, we will create a Streamlit in Snowflake application to visualize the customer purchase summary data.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Streamlit in Snowflake Application\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EChange the role to \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ENavigate to Snowsight and go to \u003Cstrong\u003EProjects\u003C/strong\u003E then \u003Cstrong\u003EStreamlit\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E to create a new Streamlit application\u003C/li\u003E\u003Cli\u003EFor the \u003Cstrong\u003EApp Title\u003C/strong\u003E, enter \u003Cstrong\u003ECustomer Spending Dashboard\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EFor the \u003Cstrong\u003EApp location\u003C/strong\u003E, enter \u003Cstrong\u003ECDC_PROD\u003C/strong\u003E for the database and \u003Cstrong\u003EANALYTICS\u003C/strong\u003E for the schema\u003C/li\u003E\u003Cli\u003EFor the \u003Cstrong\u003EApp warehouse\u003C/strong\u003E, choose the \u003Cstrong\u003ECDC_DS_WH\u003C/strong\u003E warehouse and click \u003Cstrong\u003ECreate\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECopy and paste the contents of the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/customer_spending_dashboard.py\"\u003Ecustomer_purchase_summary.py\u003C/a\u003E file into the Streamlit app code editor\u003C/li\u003E\u003Cli\u003EHere, we can view the purchase summary for all or selected customers by selecting various filter for dates, customer IDs, and product categories and more\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECDC\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this section, we will ingest new transaction data from PostgreSQL into Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EIngest New Data\u003C/h3\u003E\n","\u003Cp\u003ENavigate to your PostgreSQL console and run the following SQL command to create a stored procedure that inserts 1000 new records into the \u003Ccode\u003Etransactions\u003C/code\u003E table every minute:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE PROCEDURE insert_transactions()\nLANGUAGE plpgsql\nAS $$\nDECLARE\n    v_new_transaction_id TEXT;\n    v_customer_id INT;\n    v_product_id INT;\n    v_merchant_id INT;\n    v_transaction_date DATE;\n    v_transaction_time TEXT;\n    v_quantity INT;\n    v_product_price DOUBLE PRECISION;\n    v_total_price DOUBLE PRECISION;\n    v_existing_customer RECORD;\n    v_existing_product RECORD;\n    v_existing_merchant RECORD;\n    v_transaction_card TEXT;\n    v_transaction_category TEXT;\nBEGIN\n    -- Loop for 30 minutes (inserting 1000 records every minute)\n    FOR i IN 1..30 LOOP\n        FOR j IN 1..1000 LOOP\n            -- Select random valid customer, product, and merchant from existing tables\n            SELECT * INTO v_existing_customer\n            FROM postgres.raw_cdc.customers\n            ORDER BY RANDOM()\n            LIMIT 1;\n\n            SELECT * INTO v_existing_product\n            FROM postgres.raw_cdc.products\n            ORDER BY RANDOM()\n            LIMIT 1;\n\n            SELECT * INTO v_existing_merchant\n            FROM postgres.raw_cdc.merchants\n            ORDER BY RANDOM()\n            LIMIT 1;\n\n            -- Generate new transaction ID (unique)\n            v_new_transaction_id := 'TX' || EXTRACT(EPOCH FROM NOW())::TEXT || j::TEXT;\n\n            -- Generate current date and time in New York time zone\n            v_transaction_date := (CURRENT_TIMESTAMP AT TIME ZONE 'America/New_York')::DATE;\n            v_transaction_time := TO_CHAR(CURRENT_TIMESTAMP AT TIME ZONE 'America/New_York', 'HH24:MI:SS');\n\n            -- Generate random quantity between 1 and 7\n            v_quantity := FLOOR(RANDOM() * 7 + 1);\n\n            -- Get product price and calculate total price\n            v_product_price := v_existing_product.price;\n            v_total_price := v_product_price * v_quantity;\n\n            v_transaction_card := (ARRAY['American Express', 'Visa', 'Mastercard', 'Discover'])[FLOOR(RANDOM() * 4 + 1)];\n            v_transaction_category := CASE WHEN RANDOM() < 0.8 THEN 'Purchase' ELSE 'Refund' END;\n\n            -- Insert new transaction into the transactions table\n            INSERT INTO postgres.raw_cdc.transactions (\n                transaction_id, customer_id, product_id, merchant_id, transaction_date, transaction_time, quantity, total_price, transaction_card, transaction_category\n            )\n            VALUES (\n                v_new_transaction_id, v_existing_customer.customer_id, v_existing_product.product_id,\n                v_existing_merchant.merchant_id, v_transaction_date, v_transaction_time,\n                v_quantity, v_total_price, v_transaction_card, v_transaction_category\n            );\n        END LOOP;\n\n        -- Commit after every batch of 1000 rows\n        COMMIT;\n\n        -- Wait for 30 seconds before inserting the next batch\n        PERFORM pg_sleep(30);\n    END LOOP;\nEND;\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo run the stored procedure, execute the following SQL command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECALL insert_transactions();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENavigate to the Streamlit dashboard and refresh the page by clicking on \u003Cstrong\u003ERefresh\u003C/strong\u003E to view the new data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWhen you're finished with this Quickstart, you can clean up the objects created in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EClean Up Script\u003C/h3\u003E\n","\u003Cp\u003ENavigate to the last cell in the Snowflake Notebook to uncomment and run the last cell labeled \u003Cstrong\u003Eclean_up\u003C/strong\u003E to drop the connector objects created in this Quickstart.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003ECongrats! You're reached the end of this Quickstart!\u003C/h3\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EWith the completion of this Quickstart, you have now delved into:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHow to connect PostgreSQL data to Snowflake using the Snowflake Connector for PostgreSQL\u003C/li\u003E\u003Cli\u003EVisualize data using Dynamic Tables and display visualizations within Streamlit in Snowflake (SiS)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://other-docs.snowflake.com/en/connectors/postgres6/about\"\u003ESnowflake Connector for PostgreSQL\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-about\"\u003ESnowflake Dynamic Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks\"\u003ESnowflake Notebooks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/index\"\u003ESnowpark API\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/tree/main\"\u003EFork Repo on Github\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/content/dam/snowflake-site/developers/2024/10/Real-Time-Financial-Insights-Using-CDC-Architecture.png\"\u003EDownload Reference Architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/real-time-financial-insights-using-cdc-with-the-snowflake-connector-for-postgresql-and-dynamic-989c2911c393\"\u003ERead the Blog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://youtu.be/M77Aun9ke7I?list=TLGGUvQ7ij03_TUyNDA5MjAyNQ\"\u003EWatch the Demo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Real-Time Financial Insights Using Change Data Capture (CDC) with the Snowflake Connector for PostgreSQL and Dynamic Tables","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n![assets/CDC_connectors_banner.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/CDC_connectors_banner.png?v=32b758de)\n\nIn this Quickstart, we will investigate how a financial company builds a BI dashboard using customer transactional data housed on a PostgreSQL database. The data is brought into Snowflake via the Snowflake Connector for PostgreSQL. The main idea is gain insights on potential ways to increase customer spending with promotions.\n\n### What You Will Build\n- Visualize customer data and gain insights ingesting data from PostgreSQL DB to Snowflake using the Snowflake Connector for PostgreSQL Native App and Dynamic Tables\n\n### What You Will Learn\n- How to connect PostgreSQL data to Snowflake using the [Snowflake Connector for PostgreSQL](https://other-docs.snowflake.com/en/connectors/postgres6/about)\n- How to visualize data using [Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about) and display visualizations within [Streamlit in Snowflake (SiS)](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n\n### Prerequisites\n- [Docker](https://www.docker.com/products/docker-desktop/) installed on your local machine\n- A tool available for connecting to the PostgreSQL database\n  - This can be a database-specific tool or a general-purpose tool such as Visual Studio Code or PyCharm\n- Familiarity with basic Python and SQL\n- Familiarity with data science notebooks\n- Go to the [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) sign-up page and register for a free account. After registration, you will receive an email containing a link that will take you to Snowflake, where you can sign in.\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake Environment\n\n### Overview\nYou will use [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#), the Snowflake web interface to create Snowflake objects (warehouse, database, schema, role).\n\n#### Creating Objects and Loading Data\n1. Navigate to Worksheets, click `+` in the top-right corner to create a new Worksheet, and choose **SQL Worksheet**\n\n2. Copy and paste the following code to create Snowflake objects (warehouse, database, schema) and click **Run All** at the top of the Worksheet\n```\nUSE ROLE accountadmin;\n\n/*---------------------------*/\n-- Create our Database\n/*---------------------------*/\nCREATE OR REPLACE DATABASE cdc_prod;\n\n/*---------------------------*/\n-- Create our Schema\n/*---------------------------*/\nCREATE OR REPLACE SCHEMA cdc_prod.analytics;\n\n/*---------------------------*/\n-- Create our Warehouse\n/*---------------------------*/\n\n-- data science warehouse\nCREATE OR REPLACE WAREHOUSE cdc_ds_wh\n   WAREHOUSE_SIZE = 'xsmall'\n   WAREHOUSE_TYPE = 'standard'\n   AUTO_SUSPEND = 60\n   AUTO_RESUME = TRUE\n   INITIALLY_SUSPENDED = TRUE\n   COMMENT = 'data science warehouse for cdc';\n\n-- Use our Warehouse\nUSE WAREHOUSE cdc_ds_wh;\n/*---------------------------*/\n-- sql completion note\n/*---------------------------*/\nSELECT 'cdc sql is now complete' AS note;\n```\n\n\u003C!-- ------------------------ --\u003E\n## PostgreSQL Environment\n\n### Overview\nIn this section, we will set up a PostgreSQL database and create tables to simulate a financial company's customer transactional data.\n\n#### Starting the Database Instance\nBefore getting started with this step, make sure that you have Docker Desktop installed for either [Mac](https://docs.docker.com/desktop/install/mac-install/), [Windows](https://docs.docker.com/desktop/install/windows-install/), or [Linux](https://docs.docker.com/desktop/install/linux/). Ensure that you have [Docker Compose](https://docs.docker.com/compose/install/) installed on your machine. \n\n1. To initiate the PostgreSQL database using Docker, you'll need to create a file called **docker-compose.yaml**. This file will contain the configuration for the PostgreSQL database. If you have another container client, spin up the container and use the PostgreSQL image below. \n\n2. Open the IDE of your choice to copy and paste this file by copy and pasting the following:\n```\nservices:\n  postgres:\n    image: \"postgres:17\"\n    container_name: \"postgres17\"\n    environment:\n      POSTGRES_DB: 'postgres'\n      POSTGRES_USER: 'postgres'\n      POSTGRES_PASSWORD: 'postgres'\n    ports:\n      - \"5432:5432\"\n    command:\n      - \"postgres\"\n      - \"-c\"\n      - \"wal_level=logical\"\n    volumes:\n      - ./postgres-data:/var/lib/postgresql/data\n```\n\n3. Open a terminal and navigate to the directory where the **docker-compose.yaml** file is located. Run the following command to start the PostgreSQL database:\n\n```\ndocker-compose up -d\n```\nAfter running this command, you should see one Docker container actively running the source database.\n\n#### Connecting to the Database\nTo connect to the pre-configured databases using Visual Studio Code or PyCharm, or whichever IDE you choose for a database connection, perform the following steps with the provided credentials:\n1. Open your tool of choice for connecting to the PostgreSQL database\n   - For VSCode, you can use the [PostgreSQL extension](https://marketplace.visualstudio.com/items?itemName=cweijan.vscode-postgresql-client2)\n   - For PyCharm, you can use the [Database Tools and SQL plugin](https://www.jetbrains.com/help/pycharm/database-tool-window.html)\n2. Click the `+` sign or similar to add data source\n3. Use these connection parameters:\n    - **User:** `postgres`\n    - **Password:** `postgres`\n    - **URL:** `jdbc:postgresql://localhost:5432/`\n4. Test the connection and save\n\n#### Loading Data\n1. Run the following **postgres** script in the PostgreSQL to create the database, schema, and tables:\n\n```\nCREATE SCHEMA raw_cdc;\nSET search_path TO raw_cdc;\n\nDROP TABLE IF EXISTS postgres.raw_cdc.customers;\nDROP TABLE IF EXISTS postgres.raw_cdc.merchants;\nDROP TABLE IF EXISTS postgres.raw_cdc.products;\nDROP TABLE IF EXISTS postgres.raw_cdc.transactions;\n\nCREATE TABLE postgres.raw_cdc.customers (\n   customer_id INTEGER PRIMARY KEY,\n   firstname VARCHAR,\n   lastname VARCHAR,\n   age INTEGER,\n   email VARCHAR,\n   phone_number VARCHAR\n);\n\nCREATE TABLE postgres.raw_cdc.merchants (\n   merchant_id integer PRIMARY KEY,\n   merchant_name VARCHAR,\n   merchant_category VARCHAR\n);\n\nCREATE TABLE postgres.raw_cdc.products (\n   product_id INTEGER PRIMARY KEY,\n   product_name VARCHAR,\n   product_category VARCHAR,\n   price DOUBLE PRECISION\n);\n\nCREATE TABLE postgres.raw_cdc.transactions (\n   transaction_id VARCHAR PRIMARY KEY,\n   customer_id INTEGER,\n   product_id INTEGER,\n   merchant_id INTEGER,\n   transaction_date DATE,\n   transaction_time VARCHAR,\n   quantity INTEGER,\n   total_price DOUBLE PRECISION,\n   transaction_card VARCHAR,\n   transaction_category VARCHAR\n);\n```\n\n2. Download and save these csv files in a directory on your local machine: \n    - [customers.csv](https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/customers.csv)\n    - [merchants.csv](https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/merchants.csv)\n    - [products.csv](https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/products.csv)\n    - [transactions.csv](https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/postgres_csv/transactions.csv)\n\n3. We'll need to move the files from the local computer to the Docker container before loading the data into the PostgreSQL database.\n\n4. Navigate to your terminal to get the Docker container ID with this command:\n```\ndocker ps\n```\n5. To copy the CSV files to the container, run these commands in your terminal, replacing the file path with your actual file path,m and replacing `container_id` with your actual container ID from the previous command: \n```\ndocker cp /Users/your_username/Downloads/customers.csv container_id:/tmp/customers.csv\ndocker cp /Users/your_username/Downloads/merchants.csv container_id:/tmp/merchants.csv\ndocker cp /Users/your_username/Downloads/products.csv container_id:/tmp/products.csv\ndocker cp /Users/your_username/Downloads/transactions.csv container_id:/tmp/transactions.csv\n```\n\n6. Back in your PostgreSQL console, run these SQL commands to load the files from the container to the PostgreSQL tables:\n```\nCOPY postgres.raw_cdc.customers FROM '/tmp/customers.csv' DELIMITER ',' CSV HEADER;\nCOPY postgres.raw_cdc.merchants FROM '/tmp/merchants.csv' DELIMITER ',' CSV HEADER;\nCOPY postgres.raw_cdc.products FROM '/tmp/products.csv' DELIMITER ',' CSV HEADER;\nCOPY postgres.raw_cdc.transactions FROM '/tmp/transactions.csv' DELIMITER ',' CSV HEADER;\n```\n\n7. Next, make sure to run the `CREATE PUBLICATION` command to enable the logical replication for the tables in the `raw_cdc` schema. This will allow the Snowflake Connector for PostgreSQL to capture the changes made to the tables in the PostgreSQL database:\n```\nCREATE PUBLICATION agent_postgres_publication FOR ALL TABLES;\n```\n\n8. Lastly, check that the tables have been loaded correctly by running the following SQL commands:\n```\nSELECT * FROM postgres.raw_cdc.customers;\nSELECT * FROM postgres.raw_cdc.merchants;\nSELECT * FROM postgres.raw_cdc.products;\nSELECT * FROM postgres.raw_cdc.transactions;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake Connector\n\n### Overview\nDuring this step, you will install and configure the Snowflake Connector for PostgreSQL Native App to capture changes made to the PostgreSQL database tables.\n\n#### Install the Snowflake Connector for PostgreSQL Native App\nNavigate to [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#): \n1. Navigate to the **Data Products** then to the **Marketplace** section\n2. Search for the **Snowflake Connector for PostgreSQL** Native App and install the application\n3. You should find your installed Native App under **Data Products**, **Apps** section\n\n#### Configure the Snowflake Connector for PostgreSQL Native App\n1. On your Snowflake Account, navigate to the **Data Products**, **Apps** section\n2. Open the application\n3. Select **Mark all as done** as we will create our source databases from scratch.\n\n![assets/mark_all_done.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/mark_all_done.png?v=32b758de)\n\n4. Click **Start configuration**\n5. If you have Event Tables already activated in your account, the **Event Log Database**, **Event Log Schema**, and **Event Table** will populate automatically with what is active. The names of the **Event Log Database**, **Event Log Schema**, and **Event Table** could be slightly different from what is shown.\n6. On the **Configure Connector** screen, select **Configure**\n\n![assets/configure_connector.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/configure_connector.png?v=32b758de)\n\n7. On the **Verify Agent Connection** screen select **Generate file** to download the Agent Configuration file. The downloaded file name should resemble **snowflake.json**. Save this file for use during the Agent configuration section.\n\n![assets/generate_file.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/generate_file.png?v=32b758de)\n\n\u003C!-- ------------------------ --\u003E\n## Agent Configuration\n\n### Overview\nDuring this section, you will configure the Agent that will operate alongside our Source Databases.\n\n### Configure the Agents\nThe first step is to create the **agent-postgresql** directory. In this directory, you will create 2 directories named **agent-keys** and **configuration**.\n\n#### Creating Configuration Files\nYou will fill the configuration files for each agent to operate correctly. The configuration files include **snowflake.json** file to connect to Snowflake, **datasources.json** file to connect to the Source Databases, and **postgresql.conf** file with additional Agent Environment Variables.\n\nHere's how the file structure should look like in the beginning:\n\nDirectory Structure\n\u003Cul\u003E\n  \u003Cli\u003Eagent-postgresql\n    \u003Cul\u003E\n      \u003Cli\u003Eagent-keys\n      \u003C/li\u003E\n      \u003Cli\u003Econfiguration\n        \u003Cul\u003E\n          \u003Cli\u003Edatasources.json\u003C/li\u003E\n          \u003Cli\u003Epostgresql.conf\u003C/li\u003E\n          \u003Cli\u003Esnowflake.json\u003C/li\u003E\n        \u003C/ul\u003E\n      \u003C/li\u003E\n      \u003Cli\u003Edocker-compose.yaml\u003C/li\u003E\n    \u003C/ul\u003E\n  \u003C/li\u003E\n\u003C/ul\u003E\n\n1. Navigate to the directory called **agent-postgresql**\n2. Create the docker-compose file named **docker-compose.yaml** in the **agent-postgresql** directory with the following content:\n```\nservices:\n  postgresql-agent:\n    container_name: postgresql-agent\n    image: snowflakedb/database-connector-agent:latest\n    volumes:\n      - ./agent-keys:/home/agent/.ssh\n      - ./configuration/snowflake.json:/home/agent/snowflake.json\n      - ./configuration/datasources.json:/home/agent/datasources.json\n    env_file:\n      - configuration/postgresql.conf\n    mem_limit: 6g\n```\n\n3. Put the previously downloaded **snowflake.json** file in the **configuration** directory folder\n4. Create the file named **datasources.json** in the **configuration** directory with the following content:\n```\n{\n  \"PSQLDS1\": {\n    \"url\": \"jdbc:postgresql://host.docker.internal:5432/postgres\",\n    \"username\": \"postgres\",\n    \"password\": \"postgres\",\n    \"publication\": \"agent_postgres_publication\",\n    \"ssl\": false\n  }\n}\n```\n\n5. Create the file named **postgresql.conf** in the **configuration** directory with the following content:\n```\nJAVA_OPTS=-Xmx5g\n```\n\n6. Navigating to the **agent-postgresql** directory in your terminal, start the agent using the following command. The agent should generate public/private key for authorization to Snowflake.\n```\ndocker-compose up -d\n```\nAfter running the `docker-compose up -d` command, you will see in your file structure that the **agent-keys** directory has been populated with the private and public keys. At the end, your directory structure should resemble the following.\n\nDirectory Structure\n\u003Cul\u003E\n  \u003Cli\u003Eagent-postgresql\n    \u003Cul\u003E\n      \u003Cli\u003Eagent-keys\n        \u003Cul\u003E\n          \u003Cli\u003Edatabase-connector-agent-app-private-key.p8\u003C/li\u003E\n          \u003Cli\u003Edatabase-connector-agent-app-public-key.pub\u003C/li\u003E\n        \u003C/ul\u003E\n      \u003C/li\u003E\n      \u003Cli\u003Econfiguration\n        \u003Cul\u003E\n          \u003Cli\u003Edatasources.json\u003C/li\u003E\n          \u003Cli\u003Epostgresql.conf\u003C/li\u003E\n          \u003Cli\u003Esnowflake.json\u003C/li\u003E\n        \u003C/ul\u003E\n      \u003C/li\u003E\n      \u003Cli\u003Edocker-compose.yaml\u003C/li\u003E\n    \u003C/ul\u003E\n  \u003C/li\u003E\n\u003C/ul\u003E\n\n#### Verifying Connection with Snowflake\nNavigate to Snowsight to your previously created Snowflake Connector for PostgreSQL Native App. Click on the **Refresh** button in the Agent Connection Section. When successfully configured, you should see the \"Successfully configured\" message. Click \"Define data to sync\".\n\n![assets/successfully_configured.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/connectors-postgres-cdc/successfully_configured.png?v=32b758de)\n\n\u003C!-- ------------------------ --\u003E\n## Replication Process\n\n### Overview\nIn this step, we will instruct the Connector to begin replicating the selected tables.\n\n### Configure Data Ingestion\n1. Change the role to **ACCOUNTADMIN**\n2. Download the [Snowflake Notebook](https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/notebooks/0_start_here.ipynb) and import it into Snowflake by navigating to Snowsight and going to **Notebooks** and to using the `Import .ipynb file` button. \n3. Select the **CDC_PROD** for the database, **ANALYTICS** for the schema, and **CDC_DS_WH** for the warehouse. This Notebook includes the SQL scripts needed to create the destination database for table replication of the PostgreSQL tables into Snowflake and monitor the replication process.\n4. Run the first 3 cells in the Notebook labeled **create_db_objects**, **table_replication**, and **check_replication_state**.\n5. Run the cell labeled **check_replication_state** until the output indicates successful replication as indicated in the Notebook.\n6. Once the replication process is complete, you can run the rest of the Notebook.\n7. Notice the Dynamic Table, **cdc_prod.analytics.customer_purchase_summary**, is created in the last cell labeled **create_dynamic_table**. This table will be used to visualize the data in the **Customer Spending Dashboard** Streamlit app.\n\n\u003C!-- ------------------------ --\u003E\n## Streamlit App\n\n### Overview\nIn this section, we will create a Streamlit in Snowflake application to visualize the customer purchase summary data.\n\n### Create the Streamlit in Snowflake Application\n1. Change the role to **ACCOUNTADMIN**\n2. Navigate to Snowsight and go to **Projects** then **Streamlit**\n3. Click on the **+ Streamlit App** to create a new Streamlit application\n4. For the **App Title**, enter **Customer Spending Dashboard**\n5. For the **App location**, enter **CDC_PROD** for the database and **ANALYTICS** for the schema\n6. For the **App warehouse**, choose the **CDC_DS_WH** warehouse and click **Create**\n7. Copy and paste the contents of the [customer_purchase_summary.py](https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/blob/main/scripts/customer_spending_dashboard.py) file into the Streamlit app code editor\n8. Here, we can view the purchase summary for all or selected customers by selecting various filter for dates, customer IDs, and product categories and more\n\n\u003C!-- ------------------------ --\u003E\n## CDC\n\n### Overview\nIn this section, we will ingest new transaction data from PostgreSQL into Snowflake.\n\n### Ingest New Data\nNavigate to your PostgreSQL console and run the following SQL command to create a stored procedure that inserts 1000 new records into the `transactions` table every minute:\n```\nCREATE OR REPLACE PROCEDURE insert_transactions()\nLANGUAGE plpgsql\nAS $$\nDECLARE\n    v_new_transaction_id TEXT;\n    v_customer_id INT;\n    v_product_id INT;\n    v_merchant_id INT;\n    v_transaction_date DATE;\n    v_transaction_time TEXT;\n    v_quantity INT;\n    v_product_price DOUBLE PRECISION;\n    v_total_price DOUBLE PRECISION;\n    v_existing_customer RECORD;\n    v_existing_product RECORD;\n    v_existing_merchant RECORD;\n    v_transaction_card TEXT;\n    v_transaction_category TEXT;\nBEGIN\n    -- Loop for 30 minutes (inserting 1000 records every minute)\n    FOR i IN 1..30 LOOP\n        FOR j IN 1..1000 LOOP\n            -- Select random valid customer, product, and merchant from existing tables\n            SELECT * INTO v_existing_customer\n            FROM postgres.raw_cdc.customers\n            ORDER BY RANDOM()\n            LIMIT 1;\n\n            SELECT * INTO v_existing_product\n            FROM postgres.raw_cdc.products\n            ORDER BY RANDOM()\n            LIMIT 1;\n\n            SELECT * INTO v_existing_merchant\n            FROM postgres.raw_cdc.merchants\n            ORDER BY RANDOM()\n            LIMIT 1;\n\n            -- Generate new transaction ID (unique)\n            v_new_transaction_id := 'TX' || EXTRACT(EPOCH FROM NOW())::TEXT || j::TEXT;\n\n            -- Generate current date and time in New York time zone\n            v_transaction_date := (CURRENT_TIMESTAMP AT TIME ZONE 'America/New_York')::DATE;\n            v_transaction_time := TO_CHAR(CURRENT_TIMESTAMP AT TIME ZONE 'America/New_York', 'HH24:MI:SS');\n\n            -- Generate random quantity between 1 and 7\n            v_quantity := FLOOR(RANDOM() * 7 + 1);\n\n            -- Get product price and calculate total price\n            v_product_price := v_existing_product.price;\n            v_total_price := v_product_price * v_quantity;\n\n            v_transaction_card := (ARRAY['American Express', 'Visa', 'Mastercard', 'Discover'])[FLOOR(RANDOM() * 4 + 1)];\n            v_transaction_category := CASE WHEN RANDOM() \u003C 0.8 THEN 'Purchase' ELSE 'Refund' END;\n\n            -- Insert new transaction into the transactions table\n            INSERT INTO postgres.raw_cdc.transactions (\n                transaction_id, customer_id, product_id, merchant_id, transaction_date, transaction_time, quantity, total_price, transaction_card, transaction_category\n            )\n            VALUES (\n                v_new_transaction_id, v_existing_customer.customer_id, v_existing_product.product_id,\n                v_existing_merchant.merchant_id, v_transaction_date, v_transaction_time,\n                v_quantity, v_total_price, v_transaction_card, v_transaction_category\n            );\n        END LOOP;\n\n        -- Commit after every batch of 1000 rows\n        COMMIT;\n\n        -- Wait for 30 seconds before inserting the next batch\n        PERFORM pg_sleep(30);\n    END LOOP;\nEND;\n$$;\n```\n\nTo run the stored procedure, execute the following SQL command:\n```\nCALL insert_transactions();\n```\n\nNavigate to the Streamlit dashboard and refresh the page by clicking on **Refresh** to view the new data.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\n### Overview\nWhen you're finished with this Quickstart, you can clean up the objects created in Snowflake.\n\n### Clean Up Script\nNavigate to the last cell in the Snowflake Notebook to uncomment and run the last cell labeled **clean_up** to drop the connector objects created in this Quickstart.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n### Congrats! You're reached the end of this Quickstart!\n\n### What You Learned\nWith the completion of this Quickstart, you have now delved into:\n- How to connect PostgreSQL data to Snowflake using the Snowflake Connector for PostgreSQL\n- Visualize data using Dynamic Tables and display visualizations within Streamlit in Snowflake (SiS)\n\n### Resources\n- [Snowflake Connector for PostgreSQL](https://other-docs.snowflake.com/en/connectors/postgres6/about)\n- [Snowflake Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about)\n- [Snowflake Notebooks](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks)\n- [Snowpark API](https://docs.snowflake.com/en/developer-guide/snowpark/index)\n- [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n- [Fork Repo on Github](https://github.com/Snowflake-Labs/sfguide-intro-to-cdc-using-snowflake-postgres-connector-dynamic-tables/tree/main)\n- [Download Reference Architecture](/content/dam/snowflake-site/developers/2024/10/Real-Time-Financial-Insights-Using-CDC-Architecture.png)\n- [Read the Blog](https://medium.com/snowflake/real-time-financial-insights-using-cdc-with-the-snowflake-connector-for-postgresql-and-dynamic-989c2911c393)\n- [Watch the Demo](https://youtu.be/M77Aun9ke7I?list=TLGGUvQ7ij03_TUyNDA5MjAyNQ)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-dd990b155d","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c6a7d28862",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c9ae55a779","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-19",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-85656945ab","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-01030fa2b8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-eed4f1a3fd",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-4b8b230da1","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-c461d27e6e","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/connectors-postgres-cdc",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESnowflake 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