Snowflake continuous real-time replication using CDC

Snowflake Reader reads from Snowflake using change data capture, and provides real-time continuous replication after initial load. Snowflake Reader can read data manipulation language (DML) and limited DDL changes made to tables, such as ALTER TABLE ADD Column and ALTER TABLE DROP Column. Snowflake Reader identifies the changes made to each table by periodically querying the table using a CHANGES clause, with incrementing time intervals for each subsequent poll.

Typical use cases for reading from Snowflake include:

Snowflake Reader summary

APIs used/data supported	Snowflake Reader uses the Snowflake JDBC driver snowflake-jdbc-3.17.0.jar, which is bundled with Striim.
Data supported	Tables (Views are not supported)
Supported targets	All targets that support WAEvent input. This includes all database, data warehouse, and file system targets.
Security and authentication	Key pair OAuth 2.0 Username and password
Operations / modes supported	Data can be read from the previous 24 hours (or longer if Snowflake's Time Travel default retention period is increased). Captures inserts, updates and deletes.
Schema management	Snowflake Reader does not support initial schema creation at the target. Use Database Reader to create the initial schema.Database Reader Snowflake Reader supports schema evolution for selected schema changes. See Handling schema evolution.Handling schema evolution
Resilience / recovery	Configurable automatic retries. Supports recovery with at-least once processing. See Recovering applications.
Performance	The `SnowflakeConfiguration` property group is used to specify the `ThreadPoolSize`, `EnableQuickCatchUp`, and `YieldAfter`, properties that allow you to tune the performance of the Snowflake Reader and the load it places on Snowflake. The following considerations apply to configuring `ThreadPoolSize`: `ThreadPoolSize` < `Table Count` – the average read latency will be relatively higher, the overall load on Snowflake will be lower. `ThreadPoolSize` = `Table Count` – Snowflake reader achieves the optimal latency. This configuration can overload source Snowflake when the number of tables I high leading to performance bottlenecks. `ThreadPoolSize` > `Table Count` – not recommended, and may result in unused resources in Snowflake Reader. For more information, see Snowflake Reader operational considerations.
Programmability	Striim TQL Striim Flow Designer Wizards
Metrics and auditing	Key metrics available through Striim monitoring
Key considerations and limitations	To query the change data from Snowflake using SnowflakeReader, change tracking must be enabled on the source table. Private Link Support – Striim supports both AWS and Azure Private link for Secure Connectivity. Note: Business Critical or higher edition is required for enabling Private Link in Snowflake. SnowflakeReader does not currently support external tables. Change data availability limitation: Change data is only available for the duration of the Time Travel retention period (default is 1 day). If an application requests change data that is beyond the retention period, it will halt.

Data coverage

Snowflake CDC Reader supports all the Snowflake data types (see Summary of data types in the Snowflake doc) except for the Vector type.

Snowflake initial setup

Setup varies depending on what authentication type you choose. See Connection profiles.

The Snowflake account to be used by Striim must have the following privileges:

For all authentication types:
- USAGE on the database and schema to be read
- SELECT and OWNERSHIP on the tables to be read
For OAuth:
- CREATE INTEGRATION on the account

The OWNERSHIP privilege gives Striim the ability to enable change tracking on the tables to be read. If you prefer not to grant OWNERSHIP to the account, you must enable change tracking manually by executing the command ALTER TABLE <tablename> SET CHANGE_TRACKING = TRUE; for each table to be read (see Explicitly Enable Change Tracking on the Underlying Tables).

Authentication

See Connection profiles for additional setup steps depending on which authentication type you choose.

Private Link

To use AWS PrivateLink, an ACCOUNTADMIN must execute the following (replace <AWS ID> and <federated token> with the appropriate ID and token for your environment)::

SELECT SYSTEM$AUTHORIZE_PRIVATELINK ('<AWS ID>',‘<federated token>')

To use Azure Private Link, an ACCOUNTADMIN must execute the following (replace <Azure subscription ID> with your subscription ID):

SELECT SYSTEM$AUTHORIZE_PRIVATELINK ('<Azure subscription ID>');

Configuring Snowflake

You must have appropriate permissions in Snowflake for completing these configurations.

Create a Snowflake Reader application

To start using the Snowflake Reader in Striim apps, you can create an application in the following ways:

Create a Snowflake Reader application using a wizard

In Striim, select Apps > Create New, enter Source: Snowflake in the search bar, click the kind of application you want to create, and follow the prompts. See Creating apps using wizards for more information.Creating apps using templates

Create a Snowflake Reader application using TQL

CREATE OR REPLACE APPLICATION demo_app1;

CREATE OR REPLACE SOURCE src USING Global.SnowflakeReader ( 
  adapterName: 'SnowflakeReader', 
  connectionProfileName: 'admin.snowflakekeypair', 
  tables: 'PUBLIC.%', 
  excludedTables: 'PUBLIC.SAMPLETABLE1', 
  pollingInterval: '60s', 
  startTimestamp: 'PUBLIC.SAMPLETABLE2:\'2023-09-28T03:02:56.287-07:00\';
    PUBLIC.SAMPLETABLE5:\'2023-09-29t04:15:26.539-07:00\'', 
  connectionRetryPolicy: 'initialRetryDelay=10, retryDelayMultiplier=1.0, maxRetryDelay=30,
    maxAttempts=3, totalTimeout=600', 
  SnowflakeConfiguration: 'ThreadPoolSize=1',
  CDDLCapture: 'true',
  CDDLAction: 'Process' ) 
OUTPUT TO s1;

CREATE TARGET tgt USING Global.SysOut ( 
  name: 'tgt' ) 
INPUT FROM s1;

END APPLICATION demo_app1;

Create a Snowflake Reader application using the Flow Designer

See Creating and modifying apps using the Flow Designer.

Snowflake Reader programmer's reference

Snowflake Reader properties

property	type	default value	notes
Connection Profile Name	Enum	String	Snowflake Reader requires a connection profile. See Connection profiles.
Connection Retry Policy	String	initialRetryDelay=10, retryDelayMultiplier=1.0, maxRetryDelay=30, maxAttempts=3, totalTimeout=600	With the default setting, if a connection attempt is unsuccessful, the adapter will try again in 10 seconds (`InitialRetryDelay=10`). If that attempt is unsuccessful, every 10 seconds it will try again (`InitialRetryDelay=10s` multiplied by `retryDelayMultiplier=1.0`)until a total of three connection attempts have been made (`maxAttempts=3`), after which the adapter will halt and log an exception. The adapter will halt when either `maxAttempts` or `totalTimeout` is reached. `InitialRetryDelay`, `maxRetryDelay`, and `totalTimeout` may be specified in milliseconds (`ms`), seconds (`s`, the default), or minutes (`m`). If `retryDelayMultiplier` is set to 1, connection will be attempted on the fixed interval set by `InitialRetryDelay`. To disable connection retry, set `maxAttempts=0`. Negative values are not supported.
Excluded Tables	String		Data for any tables specified here will not be returned. For example, if Tables uses a wildcard, data from any tables specified here will be omitted. Multiple table names (separated by semicolons) and wildcards may be used exactly as for Tables.
Polling Interval	String	60s	With the default value of `60s`, Striim will check the Snowflake source for new data every 60 seconds. This may be specified as seconds or minutes, for example, `5m` for five minutes.
Schema Evolution - CDDL Action	Enum	Process	See Handling schema evolution.Handling schema evolution
Schema Evolution - CDDL Capture	Boolean	False	See Handling schema evolution. Supports only ALTER TABLE ... ADD COLUMN and ALTER TABLE ... DROP COLUMN.Handling schema evolution
Snowflake Configuration	String	ThreadPoolSize=1, EnableQuickCatchUp=true, YieldAfter=0 (disabled by default)	You can configure the following properties for Snowflake Configuration: `ThreadPoolSize`: controls the number of threads for concurrently capturing changes from Snowflake tables. Only one thread at a time can read from each table. For optimal performance this property should be equal to the number of tables. `EnableQuickCatchUp`: when the reader is starting from the past, it tries to catch up to the current time by making frequent calls to the Snowflake endpoint. This behavior can have a high cost impact on Snowflake. If this property is set to true, the Snowflake endpoint is less frequently called, thereby reducing the cost, with increased performance and lower catch up time. This property is not exposed in the UI by default. You can add this property using the Add Property option under Snowflake Configuration. `YieldAfter`: this property is beneficial when the ThreadPoolSize is less than the number of tables. In situations where some tables experience heavy traffic (one example is when a table is in a catchup phase while polling data from the past), tables with less traffic may not get a chance to process the polled results. This issue can be addressed by limiting each table's event processing to the count specified in YieldAfter. Once a table reaches its YieldAfter limit, the resources are yielded to the next table waiting in the queue. This property is not exposed in the UI by default. You can add this property using the Add Property option under Snowflake Configuration.
Start Timestamp	String	NOW	With the default value of NOW, reading will begin with data added after the application is started. Optionally specify a timestamp in the Snowflake instance's time zone to begin reading from an earlier point. To set the timestamp for all tables in all schemas, use the format `%.%:'<timestamp>'`. You may set the timestamp for individual tables using the format `<schema>.<table>:'<timestamp>'` or for all tables in a schema using `<schema>.<table>:'<timestamp>'`. You may specify multiple timestamps by separating them with semicolons, for example, `test_schema.table1:'<timestamp>'; public_schema.%:'<timestamp>'`. The Start Timestamp must be within the Time Travel range (by default 24 hours) or the application will halt. See Guides> Business Continuity & Data Recovery > Time Travel > Understanding & using Time Travel. If a table has not had change tracking enabled (see Snowflake initial setup), Snowflake Reader will enable it and start reading that table from the time that change tracking was enabled rather than from the specified timestamp. The supported time formats are: YYYY-MM-DDThh:mmTZD YYYY-MM-DDThh:mm:ssTZD YYYY-MM-DDThh:mm:ss.sssTZD YYYY = four-digit year MM = two-digit month DD = two-digit day hh = two digits of hour mm = two digits of minute ss = two digits of second sss = one to three digits representing a decimal fraction of a second TZD = time zone designator (Z or -hh:mm or +hh:mm)
Tables	String		Specify the tables to be read in the format `<schema>.<table>`. (The database is specified as part of the Connection URL property in the connection profile.) Multiple tables can be specified using the % wildcard, for example, `myschema.inc%` to read all tables with names that start with `inc`, or `myschema.%` to read all tables in `myschema`, Specify multiple selections by separating them with semicolons, for example, `schema1.%;schema2.%`. Wildcards may not be used for schemas. Do not modify this property when recovery is enabled for the application. Reading from tables with the same name in different cases is not supported. Reading from a table having columns with the same name in different cases is not supported.

Snowflake Reader sample TQL

CREATE SOURCE SnowflakeReaderSource USING SnowflakeReader ( 
  connectionProfileName: 'admin.snowflakekeypair', 
  tables: 'PUBLIC.%', 
  excludedTables: 'PUBLIC.SAMPLETABLE1', 
  CDDLCapture: 'true') 
OUTPUT TO SnowflakeReaderOutputStream;

Snowflake Reader WAEvent fields

The output data type for Snowflake Reader is WAEvent. The elements are:

metadata: a map including:

TableName: the fully qualified name of the table on which the operation was performed
ChangeWindow_StartTimestamp: start time (using the Snowflake service's time zone) of the interval in which the data was committed in Snowflake
ColumnCount: the number of columns in the table when the change data was captured
Rowid: unique identifier for each event
OperationName: INSERT, UPDATE, DELETE, or the DDL operation (such as AlterColumns)
EventType: DML or DDL
ChangeWindow_EndTimestamp: end time (using the Snowflake service's time zone) of the interval in which the data was committed in Snowflake
DatabaseName: the Snowflake database name
SourceName: value is always SNOWFLAKE
SourceNamespace: the Striim namespace of the Snowflake Reader instance
Timestamp: Striim system time when the event was received from Snowflake

data: an array of fields, numbered from 0, containing:

for an INSERT operation, the values that were inserted
for an UPDATE, the values after the operation was completed
for a DELETE, the value of the primary key and nulls for the other fields

To retrieve the values for these fields, use SELECT ... (DATA[]). See Parsing the fields of WAEvent for CDC readers.

before: for UPDATE operations, contains the primary key value from before the update. When an update changes the primary key value, you may retrieve the previous value using the BEFORE() function.

dataPresenceBitMap, beforePresenceBitMap, and typeUUID are reserved and should be ignored.

Snowflake Reader sample output

The following examples are based on this table:

CREATE TABLE AATHITHYA.TEST.SAMPLETABLE (
COL1 NUMBER PRIMARY KEY, 
COL2 FLOAT, 
COL3 VARCHAR, 
COL4 BINARY, 
C0L5 BOOLEAN, 
COL6 DATE, 
COL7 TIME, 
COL8 TIMESTAMP_LTZ, 
COL9 TIMESTAMP_NTZ, 
COL10 TIMESTAMP_TZ, 
COL11 OBJECT,
COL12 ARRAY,
COL13 VARIANT
);

For the following INSERT:

INSERT INTO AATHITHYA.TEST.SAMPLETABLE VALUES (1, 0.2, 'Striim', '48454C50', TRUE, '2002-03-05', 
'23:59:59.9999', '2020-03-12 01:02:03.123456789', '2020-03-12 01:02:03.123456789', '
2020-03-12 01:02:03.123456789 -0600', NULL, NULL, 1);

The output would be:

T1: WAEvent{
  data: [1,0.2,"Striim","48454C50",true,[ 2002, 3, 5 ],"23:59:59.9999","2020-03-12T01:02:03.123456789-07:00",
    "2020-03-12T01:02:03.123456789","2020-03-12T01:02:03.123456789-06:00",null,null,"1"]
  metadata: {
  "TableName" : "TEST.SAMPLETABLE",
  "ChangeWindow_StartTimestamp" : "2024-05-13T22:50:37.431-07:00",
  "ColumnCount" : 13,
  "RowId" : "74aa5e2e7039d0361c5923b41dae2bc8daced633",
  "OperationName" : "INSERT",
  "ChangeWindow_EndTimestamp" : "2024-05-13T22:50:47.431-07:00",
  "DatabaseName" : "AATHITHYA",
  "SourceName" : "SNOWFLAKE",
  "SourceNamespace" : "admin",
  "Timestamp" : 1715665848.535279000,
  "EventType":"DML"
  }
  userdata: null
  before: null
  dataPresenceBitMap: "fz8="
  beforePresenceBitMap: "AAA="
  typeUUID: {
  "uuidstring" : "01ef11b5-ddd9-4051-9835-3ee77256adfd"
  }
};

For the following UPDATE:

UPDATE AATHITHYA.TEST.SAMPLETABLE SET COL12 = ARRAY_CONSTRUCT(12, 'twelve') WHERE COL1 = 1;

The output would be:

T1: WAEvent{
  data: [1,0.2,"Striim","48454C50",true,[ 2002, 3, 5 ],"23:59:59.9999","2020-03-12T01:02:03.123456789-07:00",
    "2020-03-12T01:02:03.123456789","2020-03-12T01:02:03.123456789-06:00",null,"[\n  12,\n  \"twelve\"\n]","1"]
  metadata: {
  "TableName" : "TEST.SAMPLETABLE",
  "ChangeWindow_StartTimestamp" : "2024-05-13T22:52:47.431-07:00",
  "ColumnCount" : 13,
  "RowId" : "74aa5e2e7039d0361c5923b41dae2bc8daced633",
  "OperationName" : "UPDATE",
  "ChangeWindow_EndTimestamp" : "2024-05-13T22:52:57.431-07:00",
  "DatabaseName" : "AATHITHYA",
  "SourceName" : "SNOWFLAKE",
  "SourceNamespace" : "admin",
  "Timestamp" : 1715665978.893219000,
  "EventType":"DML"
  }
  userdata: null
  before: [ 1, 0.2, "Striim", "48454C50", true, [ 2002, 3, 5 ], "23:59:59.9999", 1584000123.123456789, 
    [ 2020, 3, 12, 1, 2, 3, 123456789 ], 1583996523.123456789, null, null, "1" ]
  dataPresenceBitMap: "fz8="
  beforePresenceBitMap: "fz8="
  typeUUID: {
  "uuidstring" : "01ef11b5-ddd9-4051-9835-3ee77256adfd"
  }
};

For the following DELETE:

DELETE FROM AATHITHYA.TEST.SAMPLETABLE WHERE COL1 = 1;

The output would be:

T1: WAEvent{
  data: [1,0.2,"Striim","48454C50",true,[ 2002, 3, 5 ],"23:59:59.9999","2020-03-12T01:02:03.123456789-07:00",
    "2020-03-12T01:02:03.123456789","2020-03-12T01:02:03.123456789-06:00",null,"[\n  12,\n  \"twelve\"\n]","1"]
  metadata: {
  "TableName" : "TEST.SAMPLETABLE",
  "ChangeWindow_StartTimestamp" : "2024-05-13T22:55:17.431-07:00",
  "ColumnCount" : 13,
  "RowId" : "74aa5e2e7039d0361c5923b41dae2bc8daced633",
  "OperationName" : "DELETE",
  "ChangeWindow_EndTimestamp" : "2024-05-13T22:55:27.431-07:00",
  "DatabaseName" : "AATHITHYA",
  "SourceName" : "SNOWFLAKE",
  "SourceNamespace" : "admin",
  "Timestamp" : 1715666128.735648000,
  "EventType":"DML"
  }
  userdata: null
  before: null
  dataPresenceBitMap: "fz8="
  beforePresenceBitMap: "AAA="
  typeUUID: {
  "uuidstring" : "01ef11b5-ddd9-4051-9835-3ee77256adfd"
  }
};

For the following DDL:

alter table AATHITHYA.TEST.SAMPLETABLE DROP COLUMN COL13;

The output would be:

T1: WAEvent{
  data: ["ALTER TABLE \"TEST\".\"SAMPLETABLE\" DROP COLUMN \"COL13\" ;"]
  metadata: {"CDDLMetadata":{"table":{"fullyQualifiedName":"\"TEST\".\"SAMPLETABLE\"","name":"\"SAMPLETABLE\"",
"dbEntityType":"Table","tableMappingId":null,"type":"TABLE","identifier":{"plainName":"\"TEST\".\"SAMPLETABLE\"",
"firstPart":null,"secondPart":{"plainName":"\"TEST\"","name":"\"TEST\"","metadata":{"metaEscape":null,
"multiWildcard":"%","singleWildcard":"_","bugEscape":"","encloser":"\"","backSlashEscapeSequence":"\\",
"databaseName":"","escapeSequence":"\\","case":"IGNORE_CASE"}},"thirdPart":{"plainName":"\"SAMPLETABLE\"",
"name":"\"SAMPLETABLE\"","metadata":{"metaEscape":null,"multiWildcard":"%","singleWildcard":"_",
"bugEscape":"","encloser":"\"","backSlashEscapeSequence":"\\","databaseName":"","escapeSequence":"\\",
"case":"IGNORE_CASE"}},"name":"\"TEST\".\"SAMPLETABLE\"","metadata":{"metaEscape":null,"multiWildcard":"%",
"singleWildcard":"_","bugEscape":"","encloser":"\"","backSlashEscapeSequence":"\\","databaseName":"",
"escapeSequence":"\\","case":"IGNORE_CASE"}},"schemaName":"\"TEST\"","databaseName":null,
"columns":[{"fullyQualifiedName":null,"name":"\"COL13\"","dbEntityType":"Column",
"identifier":{"plainName":"\"COL13\"","name":"\"COL13\"","metadata":{"metaEscape":null,
"multiWildcard":"%","singleWildcard":"_","bugEscape":"","encloser":"\"","backSlashEscapeSequence":"\\",
"databaseName":"snowflake","escapeSequence":"\\","case":"UPPER_CASE"}},"index":null,
"striimType":"null_STRIIM_UNKNOWN","sourceDataType":null,"targetDataType":null,
"targetDataTypeSyntax":null,"sourceDataLength":null,"targetDataLength":null,"sourcePrecision":null,
"targetPrecision":null,"sourceScale":null,"targetScale":null,"nullable":false,"unique":false,
"stringRepresentation":"Column{name='\"COL13\"', identifier=\"COL13\", index=null,
 striimType='null_STRIIM_UNKNOWN', sourceDataType='null', targetDataType='null', 
targetDataTypeSyntax='null', sourceDataLength=null, targetDataLength=null, sourcePrecision=null, 
targetPrecision=null, sourceScale=null, targetScale=null, nullable=false, unique=false}"}],
"foreignKeys":[],"pkColumns":[],"uqConstraints":{},"notNullConstraints":[],"recordCount":0,
"size":0,"sourceDDL":"ALTER TABLE \"TEST\".\"SAMPLETABLE\" DROP COLUMN \"COL13\" ;"},"violationList":[],
"finalCategory":"Green","sourceDbType":"snowflake","sourceDbSubType":null,"operation":"AlterColumns",
"subOperation":"DropColumn","exception":null,
"sql":"ALTER TABLE \"TEST\".\"SAMPLETABLE\" DROP COLUMN \"COL13\" ;","metaObject":{},
"objectPresenceMap":{"Table":true,"Column":true},"objectRequirementMap":{"Table":true,"Column":true},
"tableValid":true,"successful":true},"ChangeWindow_StartTimestamp":"2024-05-13T22:55:27.431-07:00",
"OperationName":"AlterColumns","EventType":"DDL","DatabaseName":"AATHITHYA","SourceName":"SNOWFLAKE",
"OperationSubName":"DropColumn","SourceNamespace":"admin","Timestamp":1715669128.735648000,
"TableName":"TEST.SAMPLETABLE","ChangeWindow_EndTimestamp":"2024-05-13T22:55:37.431-07:00",
"OperationType":"DDL","PREVIOUS_TYPE_UUID":{"uuidstring":"01ef11b5-ddd9-4051-9835-3ee77256adfd"}}
  userdata: null
  before: null
  dataPresenceBitMap: "AQ=="
  beforePresenceBitMap: "AA=="
  typeUUID: {"uuidstring":"01ef3775-09cf-a841-a1ea-0282642f9e1e"}
};

Snowflake Reader data type support and correspondence

Note

If a table contains a column of a geospatial type (GEOGRAPHY or GEOMETRY), Snowflake Reader cannot capture UPDATE or DELETE operations. This is a limitation of Snowflake's Change Streams (see Guides > Data Loading > Streams > Introduction to Streams > Types of Streams and Compilation errors when select from a Stream : Invalid argument types for function 'EQUAL_NULL': (GEOMETRY, GEOMETRY).

Snowflake type	aliases	Striim type	notes
ARRAY		java.lang.String
BINARY	VARBINARY	java.lang.String
BOOLEAN		java.lang.Boolean
DATE		java.time.LocalDate
FLOAT	DOUBLE, DOUBLE PRECISION, FLOAT4, FLOAT8, REAL	java.lang.Double
GEOGRAPHY		java.lang.String	see note above
GEOMETRY		java.lang.String	see note above
NUMBER	BIGINT, BYTEINT, DECIMAL, INT, INTEGER, NUMERIC, SMALLINT, TINYINT	Java.lang.Integer
OBJECT		java.lang.String
TIME		java.time.String
TIMESTAMP_LTZ		java.time.ZonedDateTime
TIMESTAMP_NTZ		java.time.LocalDateTime
TIMESTAMP_TZ		java.time.ZonedDateTime
VARCHAR	CHAR, CHAR VARYING, CHARACTER, NCHAR, NCHAR VARYING, NVARCHAR, NVARCHAR2, STRING, TEXT	java.lang.String
VARIANT		java.lang.String

Target data type support & mapping for Snowflake sources

The table below maps the data types of a Showflake source to the data types of a target such as Azure Synapse, BigQuery, Databricks, and so on when you create an application using a wizard with Auto Schema Creation, perform an initial load using Database Reader with Create Schema enabled, or run the schema conversion utility, or when Striim schema evolution creates or alters target tables.

If your screen is not wide enough to display the whole table, click in a cell and drag left to view the right-hand columns.

Showflake Data Type	Azure Synapse	BigQuery	Databricks	Db2 for z/OS	Fabric Mirror	Iceberg	MariaDB	Microsoft Fabric	MySQL	Oracle	PostgreSQL	Snowflake	Spanner	Spanner PG Dialect	SQL Server	Vertica	Yellowbrick
ARRAY	varchar(8000)	string	string	varchar(250)	string	string	text	varchar(max)	text	VARCHAR2(200)	character varying	VARCHAR	STRING(MAX)	text	varchar(8000)	varchar	character varying
BINARY	binary	bytes(p)	binary	blob	bytes	binary	Not supported	varbinary(max)	Not supported	BLOB	bytea	BINARY	BYTES(p)	Not supported	binary	long varbinary(p)	character varying(40000)
BINARY(p)	binary(p), if (p) <= 8000 binary, if (p) > 8000*	bytes, if (p) > 9223372036854775807* bytes(p), if (p) <= 9223372036854775807	binary	binary(255), if (p) <= 127 blob, if (p) > 127*	bytes	binary	binary(p), if (p) <= 255	varbinary(p), if (p) <= 8000 varbinary(max), if (p) > 8000*	binary(p), if (p) <= 255	BLOB	bytea, if (p) <= 2147483647	BINARY, if (p) <= 8388608 BINARY, if (p) > 8388608*	BYTES(MAX), if (p) > 10485760* BYTES(p), if (p) <= 10485760	bytea, if (p) <= 1048576	binary(p), if (p) <= 8000 binary, if (p) > 8000*	long varbinary(p), if 65000 <= (p) <= 32000000 binary(p), if (p) <= 65000	character varying(40000)
BOOLEAN	BIT	boolean	boolean	Not supported	boolean	boolean	bool	BIT	bool	CHAR(5)	boolean	BOOLEAN	BOOL	boolean	BIT	boolean	boolean
DATE	date	date	date	date	date	date	date	date	date	date	date	date	DATE	date	date	date	date
DOUBLE	float(53)	float64	double	double	double	double	double	float(53)	double	double precision	double precision	float	FLOAT64	float8	float(53)	float	double precision
GEOGRAPHY	varchar(8000)	string	string	varchar(250)	string	string	text	varchar(max)	text	VARCHAR2(200)	character varying	VARCHAR	STRING(MAX)	text	varchar(8000)	varchar	character varying
GEOMETRY	varchar(8000)	string	string	varchar(250)	string	string	text	varchar(max)	text	VARCHAR2(200)	character varying	VARCHAR	STRING(MAX)	text	varchar(8000)	varchar	character varying
NUMBER	numeric(p,s)	bignumeric	decimal(p,s)	varchar(1000)	decimal	decimal(p,s)	decimal(p,s)	decimal(p,s)	decimal(p,s)	number(p,s)	numeric(p,s)	numeric(p,s)	NUMERIC	numeric	numeric(p,s)	numeric(p,s)	decimal(p,s)
NUMBER(p,0)	numeric(p,s), if (p) <= 38, if (s) <= 38	numeric, if (p) <= 29, if (s) <= 9	decimal(p,s), if (p) <= 38, if (s) <= 37	numeric(p,s), if (p) <= 31, if (s) <= 30	decimal, if (p) <= 38	decimal(p,s), if (p) <= 38, if (s) <= 38	decimal(p,s), if (p) <= 65, if (s) <= 30	decimal(p,s), if (p) <= 38, if (s) <= 38	decimal(p,s), if (p) <= 65, if (s) <= 30	number(p,s), if (p) <= 38, if (s) <= 127	numeric(p,s), if (p) <= 1000, if (s) <= 1000	numeric(p,s), if (p) <= 38, if (s) <= 37	NUMERIC, if (p) <= 38, if (s) <= 9	numeric, if (p) <= 131072, if (s) <= 16384	numeric(p,s), if (p) <= 38, if (s) <= 38	numeric(p,s), if (p) <= 1024, if (s) <= 1024	decimal(p,s), if (p) <= 38, if (s) <= 38
NUMBER(p,s)	varchar(8000), if (s) > 38* varchar(8000), if (p,s) > 38* numeric(p,s), if (p) <= 38, if (s) <= 38	string, if (s) > 38* string, if (p,s) > 39* numeric, if (p) <= 29, if (s) <= 9 bignumeric, if 29 <= (p) <= 39, if 9 <= (s) <= 38	string, if (p,s) > 38* decimal(p,s), if (p) <= 38, if (s) <= 37 string, if (s) > 37*	numeric(p,s), if (p) <= 31, if (s) <= 30 varchar(1000), if (p,s) > 31, if (s) > 30*	string, if (p,s) > 38* decimal, if (p) <= 38	string, if (p,s) > 38* string, if (s) > 38* decimal(p,s), if (p) <= 38, if (s) <= 38	TEXT, if (p,s) > 65, if (s) > 30* decimal(p,s), if (p) <= 65, if (s) <= 30	varchar(max), if (s) > 38* varchar(max), if (p,s) > 38* decimal(p,s), if (p) <= 38, if (s) <= 38	TEXT, if (p,s) > 65, if (s) > 30* decimal(p,s), if (p) <= 65, if (s) <= 30	number, if (s) > 127* number, if (p,s) > 38* number(p,s), if (p) <= 38, if (s) <= 127	double precision, if (s) > 1000 double precision, if (p,s) > 1000 numeric(p,s), if (p) <= 1000, if (s) <= 1000	VARCHAR, if (s) > 37* numeric(p,s), if (p) <= 38, if (s) <= 37 VARCHAR, if (p,s) > 38*	STRING(MAX), if (p,s) > 308, if (s) > 15* NUMERIC, if (p) <= 38, if (s) <= 9	numeric, if (p) <= 131072, if (s) <= 16384	varchar(8000), if (s) > 38* varchar(8000), if (p,s) > 38* numeric(p,s), if (p) <= 38, if (s) <= 38	numeric(p,s), if (p) <= 1024, if (s) <= 1024	character varying, if (s) > 38* character varying, if (p,s) > 38* decimal(p,s), if (p) <= 38, if (s) <= 38
OBJECT	varchar(8000)	string	string	clob	string	string	json	varchar(max)	json	clob	json	VARIANT	STRING(MAX)	jsonb	varchar(max)	long varchar(10000000)	character varying
TIME	time	time	string	time	time-millis	string	time	time(6)	time	VARCHAR2(150)	time	time	STRING(150)	varchar(150)	time	time	time
TIME(p)	time	time	string	time	time-millis	string	time	time(6)	time	VARCHAR2(150)	time	time	STRING(150)	varchar(150)	time	time	time
TIMESTAMPLTZ	datetime2	timestamp	timestamp	timestamp	local-timestamp-millis	string	timestamp	datetime2	timestamp	timestamp with local time zone	timestamp without time zone	timestamp	TIMESTAMP	timestamptz	datetime2	timestamp	timestamp
TIMESTAMPLTZ(p)	datetime2	timestamp	timestamp	timestamp	local-timestamp-millis	string	timestamp	datetime2	timestamp	timestamp with local time zone	timestamp without time zone	timestamp	TIMESTAMP	timestamptz	datetime2	timestamp	timestamp
TIMESTAMPNTZ	datetime2	timestamp	timestamp	timestamp	timestamp-millis	timestamp_ntz	datetime	datetime2(6)	datetime	timestamp	timestamp without time zone	timestamp	TIMESTAMP	timestamptz	datetime2	timestamp	timestamp
TIMESTAMPNTZ(p)	datetime2	timestamp	timestamp	timestamp	timestamp-millis	timestamp_ntz	datetime	datetime2(6)	datetime	timestamp	timestamp without time zone	timestamp	TIMESTAMP	timestamptz	datetime2	timestamp	timestamp
TIMESTAMPTZ	datetimeoffset	timestamp	timestamp	timestamp with time zone	string	timestamp	timestamp	datetime2(6)	timestamp	timestamp with time zone	timestamp with time zone	timestamp with time zone	TIMESTAMP	timestamptz	datetimeoffset	timestamp with time zone	timestamptz
TIMESTAMPTZ(p)	datetimeoffset	timestamp	timestamp	timestamp with time zone	string	timestamp	timestamp	datetime2(6)	timestamp	timestamp with time zone	timestamp with time zone	timestamp with time zone	TIMESTAMP	timestamptz	datetimeoffset	timestamp with time zone	timestamptz
VARCHAR	varchar(p) varchar(8000)	string	string	clob(p) varchar(p)	string	varchar(p)	longtext varchar(p)	varchar(p) varchar(max)	longtext varchar(p)	clob VARCHAR2(p)	character varying(p)	VARCHAR(p)	STRING(p) STRING(MAX)	text varchar(p)	varchar(p) varchar(max)	long varchar(p)	character varying(p)
VARCHAR(p)	varchar(p), if (p) <= 8000 varchar(8000), if (p) > 8000*	string	string	varchar(p), if (p) <= 4045 clob(p), if 4045 <= (p) <= 2147483647	string	string, if (p) > 2147483647 varchar(p), if (p) <= 2147483647	longtext, if (p) > 2147483647* longtext, if 65535 <= (p) <= 2147483647 varchar(p), if (p) <= 65535	varchar(max), if (p) > 8000* varchar(p), if (p) <= 8000	longtext, if (p) > 2147483647* longtext, if 65535 <= (p) <= 2147483647 varchar(p), if (p) <= 65535	clob, if (p) > 4000* VARCHAR2(p), if (p) <= 4000	character varying(p), if (p) <= 10485760 character varying, if (p) > 10485760*	VARCHAR, if (p) > 16777216* VARCHAR(p), if (p) <= 16777216	STRING(MAX), if (p) > 2621440 STRING(p), if (p) <= 2621440	varchar(p), if (p) <= 1048576 text, if (p) > 1048576*	varchar(max), if (p) > 8000* varchar(p), if (p) <= 8000	varchar(p), if (p) <= 65000 long varchar(p), if 65000 <= (p) <= 32000000	character varying, if (p) > 64000* character varying(p), if (p) <= 64000
VARIANT	varchar(8000)	string	string	clob	string	string	LONGTEXT	varchar(max)	LONGTEXT	clob	text	VARCHAR	STRING(MAX)	text	varchar(max)	long varchar	character varying

*When using the schema conversion utility, these mappings appear in converted_tables_with_striim_intelligence.sql.

Snowflake continuous real-time replication using CDC

Snowflake Reader summary

Data coverage

Snowflake initial setup

Authentication

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Configuring Snowflake

Create a Snowflake Reader application

Create a Snowflake Reader application using a wizard

Create a Snowflake Reader application using TQL

Create a Snowflake Reader application using the Flow Designer

Snowflake Reader programmer's reference

Snowflake Reader properties

Snowflake Reader sample TQL

Snowflake Reader WAEvent fields

Snowflake Reader sample output

Snowflake Reader data type support and correspondence

Note

Target data type support & mapping for Snowflake sources

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