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On this page
  • Data Import Concepts
  • Step-by-Step Guide
  • STEP-1: Allow MySQL to accept connection from Sprinkle
  • STEP-2: Configure MySQL Connection
  • STEP-3: Configure MySQL data import
  • STEP-4: Create a Dataset
  • STEP-5: Run and schedule Ingestion
  1. Ingesting your data
  2. Data Imports
  3. Databases

MySQL DB

Guide to Integrate your MySQL database with Sprinkle

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Last updated 1 year ago

Data Import Concepts

Before setting up the data import, learn about data import concepts

Step-by-Step Guide

STEP-1: Allow MySQL to accept connection from Sprinkle

  1. Network Connectivity:

    • If MySQL server is on public network, accessible over public IP, allow inbound connection on mysql port (default is 3306) from Sprinkle IPs (34.93.254.126, 34.93.106.136)

    • If MySQL server is on private network, configure in Advanced Settings.

  2. Create a Read-Only user, providing any name like "sprinkle"

STEP-2: Configure MySQL Connection

To learn about Connection, refer

  • Log into the Sprinkle application

  • Navigate to Ingest -> Connections Tab -> Setup Connections ->

  • Select MySQL database

  • Provide all the mandatory details

    • Distinct Name: Name to identify this connection

    • Host: Provide the IP address or Host name.

    • Port: Provide the Port number.

    • Database: Provide a database name if there is any, it should be an existing database.

    • Username

    • Password

    • Advanced Settings: If Yes:-

      • Driver Version: Select the driver version.

      • Timezone: The timezone selected will be used to fetch Datetime/Timestamp column value.

      • Connection Properties: You can provide optional connection properties. ex- key1=value1&key2=value2

      • Change Tracking: If enabled, MySQL binlog is used to fetch changed/new rows. This setting cannot be changed later once selected. For more details .

      • Connect via SSH Host: If Yes:-

        • SSH Host: IP address or hostname of the SSH server.

        • SSH Public Key: Add this public key to the ~/.ssh/authorized_keys file on the ssh host machine.

        • SSH Login Username

  • Test Connection

  • Create

STEP-3: Configure MySQL data import

  • Navigate to Datasources -> Datasources Tab -> Add ->

  • Select MySQL database

  • Provide the name -> Create

  • Connection Tab:

    • From the drop-down, select the name of the connection created in STEP-2

    • Update

STEP-4: Create a Dataset

  • Database Name (Required): Database name to select the table from

  • Table Type: (Required):

    • Table: Select Table if you want to replicate the table

      • Source Table (Required): Name of the MySQL table

      • Exclude Columns (Optional): List of columns to exclude

      • Mask Columns (Optional): List of columns to be masked using SHA256

      • Mode (Required):

        • Complete: Ingest full data from the source table in every ingestion job run. Choose this option if your table size is small (<1 million rows) and you want to ingest it infrequently (a few times a day)

        • Incremental: Ingest only the changed or inserted rows in every ingestion job run. Choose this option if your table size is large and you want to ingest in real-time mode. This requires a time column, and a unique key to be present in the table.

          • Remove Duplicate Rows: Duplicate rows are removed by taking the latest value of your unique keys

            • Unique Keys: List of columns forming the unique id

            • Fetch With Delay(In minutes): Use this to apply delay while downloading records. When a non-zero value is specified, records inserted/updated on the source before given minutes will be ingested, instead of data till the current time. Useful when current data is not fully reflected on the source due to heavy loads. It can be left at 0 minutes if not needed.

          • Time Column: Rows with newer time value column will be ingested in each run

          • Destination Table Partition: It will create partitions of the destination table according to the specified column (for Eg: Date). Its value is NO by default. If YES, the destination table will be partitioned, else normal materialized table will be created. This feature is only applicable in BigQuery.

            • Partition Column: Enter the partition column name. A partition will be created on the destination table using the partition column value. Selected column values should be static even if other fields are updated. This should be of Date type or easily converted into Date format. If no column is selected, Partition will be created on the basis of job run time.

            • Partition Date Format: Enter the format of date.

          • Periodic Full Fetch: If yes, Periodically full table will be fetched from the source. Would be useful if data gets deleted from the source and you want to keep the data in sync with the source. If not, only incremental data will be pulled in every run.

      • Filter clause: Filter the data while ingesting. (select * from table_name where CONDITION) only give CONDITION here.

    • Query: Select Query, if you want to insert the output of the MySQL query into the data warehouse

      • Query(SQL) (Required): Query that will be executed on MySQL in each ingestion job run. If a schema is not selected as the default one, provide a fully qualified name(mention schema_name in the query). Ex- select * from schema_name.table_name;

  • Destination Schema (Required): Data warehouse schema where the table will be ingested into

  • Destination Table name (Required): It is the table name to be created in the warehouse. If not given, sprinkle will create like ds_<datasourcename>_<tablename>

  • Create

STEP-5: Run and schedule Ingestion

In the Ingestion Jobs tab:

  • Trigger the Job, using the Run button

  • To schedule, enable Auto-Run. Change the frequency if needed

To learn about data imports, refer

Datasets Tab: To learn about Dataset, refer . Add a Dataset for each table that you want to replicate, providing the following details

To Know more about Ingestion Modes, refer

Destination Create Table Clause: Provide additional clauses to warehouse-create table queries such as clustering, partitioning, and more, useful for optimizing DML statements. on how to use this field.

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