Integrate data lakes with Tiger Cloud

Unify the Tiger Cloud operational architecture with data lake architectures

enables you to build real-time applications alongside efficient data pipeline management within a single system.

is a native integration enabling synchronization between s and relational tables running in s to Iceberg tables running in Amazon S3 Tables in your AWS account.

Prerequisites

To follow the steps on this page:

• Create a target with Real-time analytics enabled.

  You need your connection details.

This feature is currently not supported for on Microsoft Azure.

Integrate a data lake with your service

To connect a to your data lake:

AWS Management Console

1. Set the AWS region to host your table bucket

   1. In AWS CloudFormation, select the current AWS region at the top-right of the page.
   2. Set it to the Region you want to create your table bucket in.

   This must match the region your is running in: if the regions do not match AWS charges you for cross-region data transfer.

2. Create your CloudFormation stack

   1. Click Create stack, then select With new resources (standard).

   2. In Amazon S3 URL, paste the following URL, then click Next.

      request

      https://tigerlake.s3.us-east-1.amazonaws.com/tigerlake-connect-cloudformation.yaml
      

   3. In Specify stack details, enter the following details, then click Next:

      • Stack Name: a name for this CloudFormation stack
      • BucketName: a name for this S3 table bucket
      • ProjectID and ServiceID: enter the connection details for your

   4. In Configure stack options check I acknowledge that AWS CloudFormation might create IAM resources, then click Next.

   5. In Review and create, click Submit, then wait for the deployment to complete. AWS deploys your stack and creates the S3 table bucket and IAM role.

   6. Click Outputs, then copy all four outputs.

3. Connect your to the data lake

   1. In , select the you want to integrate with AWS S3 Tables, then click Connectors.

   2. Select the Apache Iceberg connector and supply the:

      • ARN of the S3Table bucket
      • ARN of a role with permissions to write to the table bucket

   Provisioning takes a couple of minutes.

AWS CloudFormation CLI

1. Create your CloudFormation stack

   Replace the following values in the command, then run it from the terminal:

   • Region: region of the S3 table bucket
   • StackName: the name for this CloudFormation stack
   • BucketName: the name of the S3 table bucket to create
   • ProjectID: enter your connection details
   • ServiceID: enter your connection details
   aws cloudformation create-stack \
    --capabilities CAPABILITY_IAM \
    --template-url https://tigerlake.s3.us-east-1.amazonaws.com/tigerlake-connect-cloudformation.yaml \
    --region <Region> \
    --stack-name <StackName> \
    --parameters \
      ParameterKey=BucketName,ParameterValue="<BucketName>" \
      ParameterKey=ProjectID,ParameterValue="<ProjectID>" \
      ParameterKey=ServiceID,ParameterValue="<ServiceID>"
   

Setting up the integration through in , provides a convenient copy-paste option with the placeholders populated.

1. Connect your to the data lake

   1. In , select the you want to integrate with AWS S3 Tables, then click Connectors.

   2. Select the Apache Iceberg connector and supply the:

      • ARN of the S3Table bucket
      • ARN of a role with permissions to write to the table bucket

   Provisioning takes a couple of minutes.

Manual configuration

1. Create a S3 Bucket

   1. Set the AWS region to host your table bucket

      1. In Amazon S3 console, select the current AWS region at the top-right of the page.
      2. Set it to the Region your you want to create your table bucket in.

      This must match the region your is running in: if the regions do not match AWS charges you for cross-region data transfer.

   2. In the left navigation pane, click Table buckets, then click Create table bucket.

   3. Enter Table bucket name, then click Create table bucket.

   4. Copy the Amazon Resource Name (ARN) for your table bucket.

2. Create an ARN role

   1. In IAM Dashboard, click Roles then click Create role

   2. In Select trusted entity, click Custom trust policy, replace the Custom trust policy code block with the following:

      {
          "Version": "2012-10-17",
          "Statement": [
              {
                  "Effect": "Allow",
                  "Principal": {
                      "AWS": "arn:aws:iam::142548018081:root"
                  },
                  "Action": "sts:AssumeRole",
                  "Condition": {
                      "StringEquals": {
                          "sts:ExternalId": "<ProjectID>/<ServiceID>"
                      }
                  }
              }
          ]
      }
      

      "Principal": { "AWS": "arn:aws:iam::123456789012:root" } does not mean root access. This delegates permissions to the entire AWS account, not just the root user.

   3. Replace <ProjectID> and <ServiceID> with the the connection details for your , then click Next.

   4. In Permissions policies. click Next.

   5. In Role details, enter Role name, then click Create role.

   6. In Roles, select the role you just created, then click Add Permissions > Create inline policy.

   7. Select JSON then replace the Policy editor code block with the following:

      {
        "Version": "2012-10-17",
        "Statement": [
          {
            "Sid": "BucketOps",
            "Effect": "Allow",
            "Action": [
              "s3tables:*"
            ],
            "Resource": "<S3TABLE_BUCKET_ARN>"
          },
          {
            "Sid": "BucketTableOps",
            "Effect": "Allow",
            "Action": [
              "s3tables:*"
            ],
            "Resource": "<S3TABLE_BUCKET_ARN>/table/*"
          }
        ]
      }
      

   8. Replace <S3TABLE_BUCKET_ARN> with the Amazon Resource Name (ARN) for the table bucket you just created.

   9. Click Next, then give the inline policy a name and click Create policy.

3. Connect your to the data lake

   1. In , select the you want to integrate with AWS S3 Tables, then click Connectors.

   2. Select the Apache Iceberg connector and supply the:

      • ARN of the S3Table bucket
      • ARN of a role with permissions to write to the table bucket

   Provisioning takes a couple of minutes.

Stream data from your service to your data lake

Records are imported in time order, from oldest to newest. Your or relational table must have a primary key, or composite primary keys as a prerequisite to sync to Iceberg.

When you start syncing, all data in the table is streamed to Iceberg in the following processes:

• Table snapshot: stream data from a snapshot of the source table to the destination Iceberg table at approximately 300.000 records a second. For larger tables, import speeds are approximately 1 billion records or 100 GB of data an hour. However, these numbers vary on table width and the complexity of the schema.
• Table changes: stream changes made to the source table (CDC) after the snapshot is taken to a branch of the destination Iceberg table. This happens at approximately 30.000 events a second. Ingest bursts exceeding this can be handled for a certain amount of time and feathered out over time. This depends on duration of the ingestion burst, and the amount of extra events to be handled.

Once the snapshot is fully imported, the snapshot and CDC Iceberg table branches are merged. Merging takes from a couple of seconds, to ten minutes for larger tables of 5TB or more. During this time, new events are held on the WAL. Once the merge is completed, events in the WAL are CDC’d to Iceberg. This implies eventual consistency of the Iceberg table after you started the the sync.

To stream data from a relational table, or a in your to your data lake, run the following statement:

ALTER TABLE <table_name> SET (
  tigerlake.iceberg_sync = true | false,
  tigerlake.iceberg_partitionby = '<partition_specification>',
  tigerlake.iceberg_namespace = '<namespace>',
  tigerlake.iceberg_table = '<table>'
)

• tigerlake.iceberg_sync: boolean, set to true to start streaming, or false to stop the stream. A stream cannot resume after being stopped.
• tigerlake.iceberg_partitionby: optional property to define a partition specification in Iceberg. By default the Iceberg table is partitioned as day(<time-column of {HYPERTABLE}>). This default behavior is only applicable to s. For more information, see partitioning.
• tigerlake.iceberg_namespace: optional property to set a namespace, the default is timescaledb.
• tigerlake.iceberg_table: optional property to specify a different table name. If no name is specified the table name is used.

Partitioning intervals

By default, the partition interval for an Iceberg table is one day(time-column) for a . table sync does not enable any partitioning in Iceberg for non-hypertables. You can set it using tigerlake.iceberg_partitionby. The following partition intervals and specifications are supported:

Interval	Description	Source types
hour	Extract a date or timestamp day, as days from epoch. Epoch is 1970-01-01.	date, timestamp, timestamptz
day	Extract a date or timestamp day, as days from epoch.	date, timestamp, timestamptz
month	Extract a date or timestamp day, as days from epoch.	date, timestamp, timestamptz
year	Extract a date or timestamp day, as days from epoch.	date, timestamp, timestamptz
truncate[W]	Value truncated to width W, see options

These partitions define the behavior using the Iceberg partition specification.

Sample code

The following samples show you how to tune data sync from a or a relational table to your data lake:

• Sync a with the default one-day partitioning interval on the ts_column column

  To start syncing data from a to your data lake using the default one-day chunk interval as the partitioning scheme to the Iceberg table, run the following statement:

  ALTER TABLE my_hypertable SET (tigerlake.iceberg_sync = true);
  

  This is equivalent to day(ts_column).

• Specify a custom partitioning scheme for a

  You use the tigerlake.iceberg_partitionby property to specify a different partitioning scheme for the Iceberg table at sync start. For example, to enforce an hourly partition scheme from the chunks on ts_column on a , run the following statement:

  ALTER TABLE my_hypertable SET (
    tigerlake.iceberg_sync = true,
    tigerlake.iceberg_partitionby = 'hour(ts_column)'
  );
  

• Set the partition to sync relational tables

  relational tables do not forward a partitioning scheme to Iceberg, you must specify the partitioning scheme using tigerlake.iceberg_partitionby when you start the sync. For example, for a standard table to sync to the Iceberg table with daily partitioning , run the following statement:

  ALTER TABLE my_postgres_table SET (
    tigerlake.iceberg_sync = true,
    tigerlake.iceberg_partitionby = 'day(timestamp_col)'
  );
  

• Stop sync to an Iceberg table for a or a relational table

  ALTER TABLE my_hypertable SET (tigerlake.iceberg_sync = false);
  

• Update or add the partitioning scheme of an Iceberg table

  To change the partitioning scheme of an Iceberg table, you specify the desired partitioning scheme using the tigerlake.iceberg_partitionby property. For example. if the samples table has an hourly (hour(ts)) partition on the ts timestamp column, to change to daily partitioning, call the following statement:

  ALTER TABLE samples SET (tigerlake.iceberg_partitionby = 'day(ts)');
  

  This statement is also correct for Iceberg tables without a partitioning scheme. When you change the partition, you do not have to pause the sync to Iceberg. Apache Iceberg handles the partitioning operation in function of the internal implementation.

Specify a different namespace

By default, tables are created in the the timescaledb namespace. To specify a different namespace when you start the sync, use the tigerlake.iceberg_namespace property. For example:

ALTER TABLE my_hypertable SET (
  tigerlake.iceberg_sync = true,
  tigerlake.iceberg_namespace = 'my_namespace'
);

Specify a different Iceberg table name

The table name in Iceberg is the same as the source table in . Some services do not allow mixed case, or have other constraints for table names. To define a different table name for the Iceberg table at sync start, use the tigerlake.iceberg_table property. For example:

ALTER TABLE Mixed_CASE_TableNAME SET (
  tigerlake.iceberg_sync = true,
  tigerlake.iceberg_table = 'my_table_name'
);

Limitations

• Service requires 17.6 and above is supported.
• Amazon S3 Tables Iceberg REST catalog only is supported.
• In order to collect deletes made to data in the columstore, certain columnstore optimizations are disabled for s, this includes Direct Compress.
• The TRUNCATE statement is not supported, and does not truncate data in the corresponding Iceberg table.
• Data in a that has been moved to the low-cost object storage tier is not synced.
• Writing to the same S3 table bucket from multiple services is not supported, bucket-to-service mapping is one-to-one.
• Iceberg snapshots are pruned automatically if the amount exceeds 2500.
• A with long running continuous aggregates refresh transactions, plus 30 minutes, can cause issues with holding the replication slot too long. Please consider batching in these cases.