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Share and publish your Snowflake information to AWS Knowledge Change utilizing Amazon Redshift information sharing


Amazon Redshift is a completely managed, petabyte-scale information warehouse service within the cloud. You can begin with just some hundred gigabytes of information and scale to a petabyte or extra. At this time, tens of 1000’s of AWS clients—from Fortune 500 corporations, startups, and every thing in between—use Amazon Redshift to run mission-critical enterprise intelligence (BI) dashboards, analyze real-time streaming information, and run predictive analytics. With the fixed enhance in generated information, Amazon Redshift clients proceed to attain successes in delivering higher service to their end-users, enhancing their merchandise, and working an environment friendly and efficient enterprise.

On this publish, we focus on a buyer who’s presently utilizing Snowflake to retailer analytics information. The shopper wants to supply this information to purchasers who’re utilizing Amazon Redshift through AWS Knowledge Change, the world’s most complete service for third-party datasets. We clarify intimately easy methods to implement a completely built-in course of that may robotically ingest information from Snowflake into Amazon Redshift and supply it to purchasers through AWS Knowledge Change.

Overview of the answer

The answer consists of 4 high-level steps:

  1. Configure Snowflake to push the modified information for recognized tables into an Amazon Easy Storage Service (Amazon S3) bucket.
  2. Use a custom-built Redshift Auto Loader to load this Amazon S3 landed information to Amazon Redshift.
  3. Merge the info from the change information seize (CDC) S3 staging tables to Amazon Redshift tables.
  4. Use Amazon Redshift information sharing to license the info to clients through AWS Knowledge Change as a public or personal providing.

The next diagram illustrates this workflow.

Solution Architecture Diagram

Stipulations

To get began, you want the next conditions:

Configure Snowflake to trace the modified information and unload it to Amazon S3

In Snowflake, establish the tables that you could replicate to Amazon Redshift. For the aim of this demo, we use the info within the TPCH_SF1 schema’s Buyer, LineItem, and Orders tables of the SNOWFLAKE_SAMPLE_DATA database, which comes out of the field along with your Snowflake account.

  1. Ensure that the Snowflake exterior stage title unload_to_s3 created within the conditions is pointing to the S3 prefix s3-redshift-loader-sourcecreated within the earlier step.
  2. Create a brand new schema BLOG_DEMO within the DEMO_DB database:CREATE SCHEMA demo_db.blog_demo;
  3. Duplicate the Buyer, LineItem, and Orders tables within the TPCH_SF1 schema to the BLOG_DEMO schema:
    CREATE TABLE CUSTOMER AS 
    SELECT * FROM snowflake_sample_data.tpch_sf1.CUSTOMER;
    CREATE TABLE ORDERS AS
    SELECT * FROM snowflake_sample_data.tpch_sf1.ORDERS;
    CREATE TABLE LINEITEM AS 
    SELECT * FROM snowflake_sample_data.tpch_sf1.LINEITEM;

  4. Confirm that the tables have been duplicated efficiently:
    SELECT table_catalog, table_schema, table_name, row_count, bytes
    FROM INFORMATION_SCHEMA.TABLES
    WHERE TABLE_SCHEMA = 'BLOG_DEMO'
    ORDER BY ROW_COUNT;

    unload-step-4

  5. Create desk streams to trace information manipulation language (DML) modifications made to the tables, together with inserts, updates, and deletes:
    CREATE OR REPLACE STREAM CUSTOMER_CHECK ON TABLE CUSTOMER;
    CREATE OR REPLACE STREAM ORDERS_CHECK ON TABLE ORDERS;
    CREATE OR REPLACE STREAM LINEITEM_CHECK ON TABLE LINEITEM;

  6. Carry out DML modifications to the tables (for this publish, we run UPDATE on all tables and MERGE on the buyer desk):
    UPDATE buyer 
    SET c_comment="Pattern remark for weblog demo" 
    WHERE c_custkey between 0 and 10; 
    UPDATE orders 
    SET o_comment="Pattern remark for weblog demo" 
    WHERE o_orderkey between 1800001 and 1800010; 
    UPDATE lineitem 
    SET l_comment="Pattern remark for weblog demo" 
    WHERE l_orderkey between 3600001 and 3600010;
    MERGE INTO buyer c 
    USING 
    ( 
    SELECT n_nationkey 
    FROM snowflake_sample_data.tpch_sf1.nation s 
    WHERE n_name="UNITED STATES") n 
    ON n.n_nationkey = c.c_nationkey 
    WHEN MATCHED THEN UPDATE SET c.c_comment="That is US primarily based customer1";

  7. Validate that the stream tables have recorded all modifications:
    SELECT * FROM CUSTOMER_CHECK; 
    SELECT * FROM ORDERS_CHECK; 
    SELECT * FROM LINEITEM_CHECK;

    For instance, we are able to question the next buyer key worth to confirm how the occasions have been recorded for the MERGE assertion on the client desk:

    SELECT * FROM CUSTOMER_CHECK the place c_custkey = 60027;

    We will see the METADATA$ISUPDATE column as TRUE, and we see DELETE adopted by INSERT within the METADATA$ACTION column.
    unload-val-step-7

  8. Run the COPY command to dump the CDC from the stream tables to the S3 bucket utilizing the exterior stage title unload_to_s3.Within the following code, we’re additionally copying the info to S3 folders ending with _stg to make sure that when Redshift Auto Loader robotically creates these tables in Amazon Redshift, they get created and marked as staging tables:
    COPY INTO @unload_to_s3/customer_stg/
    FROM (choose *, sysdate() as LAST_UPDATED_TS from demo_db.blog_demo.customer_check)
    FILE_FORMAT = (TYPE = PARQUET)
    OVERWRITE = TRUE HEADER = TRUE;

    COPY INTO @unload_to_s3/customer_stg/
    FROM (choose *, sysdate() as LAST_UPDATED_TS from demo_db.blog_demo.customer_check)
    FILE_FORMAT = (TYPE = PARQUET)
    OVERWRITE = TRUE HEADER = TRUE;

    COPY INTO @unload_to_s3/lineitem_stg/ 
    FROM (choose *, sysdate() as LAST_UPDATED_TS from demo_db.blog_demo.lineitem_check) 
    FILE_FORMAT = (TYPE = PARQUET) 
    OVERWRITE = TRUE HEADER = TRUE;

  9. Confirm the info within the S3 bucket. There will likely be three sub-folders created within the s3-redshift-loader-source folder of the S3 bucket, and every may have .parquet information recordsdata.unload-step-9-valunload-step-9-valYou can too automate the previous COPY instructions utilizing duties, which will be scheduled to run at a set frequency for automated copy of CDC information from Snowflake to Amazon S3.
  10. Use the ACCOUNTADMIN function to assign the EXECUTE TASK privilege. On this state of affairs, we’re assigning the privileges to the SYSADMIN function:
    USE ROLE accountadmin;
    GRANT EXECUTE TASK, EXECUTE MANAGED TASK ON ACCOUNT TO ROLE sysadmin;

  11. Use the SYSADMIN function to create three separate duties to run three COPY instructions each 5 minutes: USE ROLE sysadmin;
    /* Job to dump Buyer CDC desk */ 
    CREATE TASK sf_rs_customer_cdc 
    WAREHOUSE = SMALL 
    SCHEDULE = 'USING CRON 5 * * * * UTC' 
    AS 
    COPY INTO @unload_to_s3/customer_stg/ 
    FROM (choose *, sysdate() as LAST_UPDATED_TS from demo_db.blog_demo.customer_check) 
    FILE_FORMAT = (TYPE = PARQUET) 
    OVERWRITE = TRUE 
    HEADER = TRUE;
    /*Job to dump Orders CDC desk */ 
    CREATE TASK sf_rs_orders_cdc 
    WAREHOUSE = SMALL 
    SCHEDULE = 'USING CRON 5 * * * * UTC' 
    AS 
    COPY INTO @unload_to_s3/orders_stg/ 
    FROM (choose *, sysdate() as LAST_UPDATED_TS from demo_db.blog_demo.orders_check)
    FILE_FORMAT = (TYPE = PARQUET)
    OVERWRITE = TRUE HEADER = TRUE;

    /* Job to dump Lineitem CDC desk */ 
    CREATE TASK sf_rs_lineitem_cdc 
    WAREHOUSE = SMALL 
    SCHEDULE = 'USING CRON 5 * * * * UTC' 
    AS 
    COPY INTO @unload_to_s3/lineitem_stg/ 
    FROM (choose *, sysdate() as LAST_UPDATED_TS from demo_db.blog_demo.lineitem_check)
    FILE_FORMAT = (TYPE = PARQUET)
    OVERWRITE = TRUE HEADER = TRUE;

    When the duties are first created, they’re in a SUSPENDED state.

  12. Alter the three duties and set them to RESUME state:
    ALTER TASK sf_rs_customer_cdc RESUME;
    ALTER TASK sf_rs_orders_cdc RESUME;
    ALTER TASK sf_rs_lineitem_cdc RESUME;

  13. Validate that every one three duties have been resumed efficiently: SHOW TASKS;unload-setp-13-valNow the duties will run each 5 minutes and search for new information within the stream tables to dump to Amazon S3.As quickly as information is migrated from Snowflake to Amazon S3, Redshift Auto Loader robotically infers the schema and immediately creates corresponding tables in Amazon Redshift. Then, by default, it begins loading information from Amazon S3 to Amazon Redshift each 5 minutes. You can too change the default setting of 5 minutes.
  14. On the Amazon Redshift console, launch the question editor v2 and connect with your Amazon Redshift cluster.
  15. Browse to the dev database, public schema, and broaden Tables.
    You possibly can see three staging tables created with the identical title because the corresponding folders in Amazon S3.
  16. Validate the info in one of many tables by working the next question:SELECT * FROM "dev"."public"."customer_stg";unload-step-16-val

Configure the Redshift Auto Loader utility

The Redshift Auto Loader makes information ingestion to Amazon Redshift considerably simpler as a result of it robotically masses information recordsdata from Amazon S3 to Amazon Redshift. The recordsdata are mapped to the respective tables by merely dropping recordsdata into preconfigured areas on Amazon S3. For extra particulars concerning the structure and inside workflow, consult with the GitHub repo.

We use an AWS CloudFormation template to arrange Redshift Auto Loader. Full the next steps:

  1. Launch the CloudFormation template.
  2. Select Subsequent.
    autoloader-step-2
  3. For Stack title, enter a reputation.
  4. Present the parameters listed within the following desk.
    CloudFormation Template Parameter Allowed Values Description
    RedshiftClusterIdentifier Amazon Redshift cluster identifier Enter the Amazon Redshift cluster identifier.
    DatabaseUserName Database consumer title within the Amazon Redshift cluster The Amazon Redshift database consumer title that has entry to run the SQL script.
    DatabaseName S3 bucket title The title of the Amazon Redshift main database the place the SQL script is run.
    DatabaseSchemaName Database title in Amazon Redshift The Amazon Redshift schema title the place the tables are created.
    RedshiftIAMRoleARN Default or the legitimate IAM function ARN hooked up to the Amazon Redshift cluster The IAM function ARN related to the Amazon Redshift cluster. Your default IAM function is about for the cluster and has entry to your S3 bucket, depart it on the default.
    CopyCommandOptions Copy possibility; default is delimiter ‘|’ gzip

    Present the extra COPY command information format parameters.

    If InitiateSchemaDetection = Sure, then the method makes an attempt to detect the schema and robotically set the acceptable copy command choices.

    Within the occasion of failure on schema detection or when InitiateSchemaDetection = No, then this worth is used because the default COPY command choices to load information.

    SourceS3Bucket S3 bucket title The S3 bucket the place the info is saved. Ensure the IAM function that’s related to the Amazon Redshift cluster has entry to this bucket.
    InitiateSchemaDetection Sure/No

    Set to Sure to dynamically detect the schema previous to file load and create a desk in Amazon Redshift if it doesn’t exist already. If a desk already exists, then it received’t drop or recreate the desk in Amazon Redshift.

    If schema detection fails, the method makes use of the default COPY choices as laid out in CopyCommandOptions.

    The Redshift Auto Loader makes use of the COPY command to load information into Amazon Redshift. For this publish, set CopyCommandOptions as follows, and configure any supported COPY command choices:

    delimiter '|' dateformat 'auto' TIMEFORMAT 'auto'

    autoloader-input-parameters

  5. Select Subsequent.
  6. Settle for the default values on the following web page and select Subsequent.
  7. Choose the acknowledgement examine field and select Create stack.
    autoloader-step-7
  8. Monitor the progress of the Stack creation and wait till it’s full.
  9. To confirm the Redshift Auto Loader configuration, register to the Amazon S3 console and navigate to the S3 bucket you offered.
    It is best to see a brand new listing s3-redshift-loader-source is created.
    autoloader-step-9

Copy all the info recordsdata exported from Snowflake beneath s3-redshift-loader-source.

Merge the info from the CDC S3 staging tables to Amazon Redshift tables

To merge your information from Amazon S3 to Amazon Redshift, full the next steps:

  1. Create a short lived staging desk merge_stg and insert all of the rows from the S3 staging desk which have metadata_action as INSERT, utilizing the next code. This contains all the brand new inserts in addition to the replace.
    CREATE TEMP TABLE merge_stg 
    AS
    SELECT * FROM
    (
    SELECT *, DENSE_RANK() OVER (PARTITION BY c_custkey ORDER BY last_updated_ts DESC
    ) AS rnk
    FROM customer_stg WHERE rnk = 1 AND metadata$motion = 'INSERT'

    The previous code makes use of a window perform DENSE_RANK() to pick the most recent entries for a given c_custkey by assigning a rank to every row for a given c_custkey and organize the info in descending order utilizing last_updated_ts. We then choose the rows with rnk=1 and metadata$motion = ‘INSERT’ to seize all of the inserts.

  2. Use the S3 staging desk customer_stg to delete the information from the bottom desk buyer, that are marked as deletes or updates:
    DELETE FROM buyer 
    USING customer_stg 
    WHERE buyer.c_custkey = customer_stg.c_custkey;

    This deletes all of the rows which might be current within the CDC S3 staging desk, which takes care of rows marked for deletion and updates.

  3. Use the non permanent staging desk merge_stg to insert the information marked for updates or inserts:
    INSERT INTO buyer 
    SELECT c_custkey, c_name, c_address, c_nationkey, c_phone, c_acctbal, c_mktsegment, c_comment 
    FROM merge_stg;

  4. Truncate the staging desk, as a result of we’ve got already up to date the goal desk:truncate customer_stg;
  5. You can too run the previous steps as a saved process:
    CREATE OR REPLACE PROCEDURE merge_customer()
    AS $$
    BEGIN
    /*CREATING TEMP TABLE TO GET THE MOST LATEST RECORDS FOR UPDATES/NEW INSERTS*/
    CREATE TEMP TABLE merge_stg AS
    SELECT * FROM
    (
    SELECT *, DENSE_RANK() OVER (PARTITION BY c_custkey ORDER BY last_updated_ts DESC ) AS rnk
    FROM customer_stg
    )
    WHERE rnk = 1 AND metadata$motion = 'INSERT';
    /* DELETING FROM THE BASE TABLE USING THE CDC STAGING TABLE ALL THE RECORDS MARKED AS DELETES OR UPDATES*/
    DELETE FROM buyer
    USING customer_stg
    WHERE buyer.c_custkey = customer_stg.c_custkey;
    /*INSERTING NEW/UPDATED RECORDS IN THE BASE TABLE*/ 
    INSERT INTO buyer
    SELECT c_custkey, c_name, c_address, c_nationkey, c_phone, c_acctbal, c_mktsegment, c_comment
    FROM merge_stg;
    truncate customer_stg;
    END;
    $$ LANGUAGE plpgsql;

    For instance, let’s take a look at the earlier than and after states of the client desk when there’s been a change in information for a selected buyer.

    The next screenshot reveals the brand new modifications recorded within the customer_stg desk for c_custkey = 74360.
    merge-process-new-changes
    We will see two information for a buyer with c_custkey=74360 one with metadata$motion as DELETE and one with metadata$motion as INSERT. Meaning the report with c_custkey was up to date on the supply and these modifications must be utilized to the goal buyer desk in Amazon Redshift.

    The next screenshot reveals the present state of the buyer desk earlier than these modifications have been merged utilizing the previous saved process:
    merge-process-current-state

  6. Now, to replace the goal desk, we are able to run the saved process as follows: CALL merge_customer()The next screenshot reveals the ultimate state of the goal desk after the saved process is full.
    merge-process-after-sp

Run the saved process on a schedule

You can too run the saved process on a schedule through Amazon EventBridge. The scheduling steps are as follows:

  1. On the EventBridge console, select Create rule.
    sp-schedule-1
  2. For Title, enter a significant title, for instance, Set off-Snowflake-Redshift-CDC-Merge.
  3. For Occasion bus, select default.
  4. For Rule Sort, choose Schedule.
  5. Select Subsequent.
    sp-schedule-step-5
  6. For Schedule sample, choose A schedule that runs at an everyday charge, equivalent to each 10 minutes.
  7. For Price expression, enter Worth as 5 and select Unit as Minutes.
  8. Select Subsequent.
    sp-schedule-step-8
  9. For Goal varieties, select AWS service.
  10. For Choose a Goal, select Redshift cluster.
  11. For Cluster, select the Amazon Redshift cluster identifier.
  12. For Database title, select dev.
  13. For Database consumer, enter a consumer title with entry to run the saved process. It makes use of non permanent credentials to authenticate.
  14. Optionally, you may also use AWS Secrets and techniques Supervisor for authentication.
  15. For SQL assertion, enter CALL merge_customer().
  16. For Execution function, choose Create a brand new function for this particular useful resource.
  17. Select Subsequent.
    sp-schedule-step-17
  18. Evaluate the rule parameters and select Create rule.

After the rule has been created, it robotically triggers the saved process in Amazon Redshift each 5 minutes to merge the CDC information into the goal desk.

Configure Amazon Redshift to share the recognized information with AWS Knowledge Change

Now that you’ve got the info saved inside Amazon Redshift, you may publish it to clients utilizing AWS Knowledge Change.

  1. In Amazon Redshift, utilizing any question editor, create the info share and add the tables to be shared:
    CREATE DATASHARE salesshare MANAGEDBY ADX;
    ALTER DATASHARE salesshare ADD SCHEMA tpch_sf1;
    ALTER DATASHARE salesshare ADD TABLE tpch_sf1.buyer;

    ADX-step1

  2. On the AWS Knowledge Change console, create your dataset.
  3. Choose Amazon Redshift datashare.
    ADX-step3-create-datashare
  4. Create a revision within the dataset.
    ADX-step4-create-revision
  5. Add property to the revision (on this case, the Amazon Redshift information share).
    ADX-addassets
  6. Finalize the revision.
    ADX-step-6-finalizerevision

After you create the dataset, you may publish it to the general public catalog or on to clients as a personal product. For directions on easy methods to create and publish merchandise, consult with NEW – AWS Knowledge Change for Amazon Redshift

Clear up

To keep away from incurring future fees, full the next steps:

  1. Delete the CloudFormation stack used to create the Redshift Auto Loader.
  2. Delete the Amazon Redshift cluster created for this demonstration.
  3. When you have been utilizing an current cluster, drop the created exterior desk and exterior schema.
  4. Delete the S3 bucket you created.
  5. Delete the Snowflake objects you created.

Conclusion

On this publish, we demonstrated how one can arrange a completely built-in course of that repeatedly replicates information from Snowflake to Amazon Redshift after which makes use of Amazon Redshift to supply information to downstream purchasers over AWS Knowledge Change. You need to use the identical structure for different functions, equivalent to sharing information with different Amazon Redshift clusters throughout the similar account, cross-accounts, and even cross-Areas if wanted.


Concerning the Authors

Raks KhareRaks Khare is an Analytics Specialist Options Architect at AWS primarily based out of Pennsylvania. He helps clients architect information analytics options at scale on the AWS platform.

Ekta Ahuja is a Senior Analytics Specialist Options Architect at AWS. She is obsessed with serving to clients construct scalable and sturdy information and analytics options. Earlier than AWS, she labored in a number of completely different information engineering and analytics roles. Exterior of labor, she enjoys baking, touring, and board video games.

Tahir Aziz is an Analytics Answer Architect at AWS. He has labored with constructing information warehouses and large information options for over 13 years. He loves to assist clients design end-to-end analytics options on AWS. Exterior of labor, he enjoys touring
and cooking.

Ahmed Shehata is a Senior Analytics Specialist Options Architect at AWS primarily based on Toronto. He has greater than twenty years of expertise serving to clients modernize their information platforms, Ahmed is obsessed with serving to clients construct environment friendly, performant and scalable Analytic options.

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