Migrating data from MongoDB to Databricks using CData SSIS Components.


Easily push MongoDB data to Databricks using the CData SSIS Tasks for MongoDB and Databricks.

Databricks is a unified data analytics platform that allows organizations to easily process, analyze, and visualize large amounts of data. It combines data engineering, data science, and machine learning capabilities in a single platform, making it easier for teams to collaborate and derive insights from their data.

The CData SSIS Components enhance SQL Server Integration Services by enabling users to easily import and export data from various sources and destinations.

In this article, we explore the data type mapping considerations when exporting to Databricks and walk through how to migrate MongoDB data to Databricks using the CData SSIS Components for MongoDB and Databricks.

Data Type Mapping

Databricks Schema CData Schema

int, integer, int32

int

smallint, short, int16

smallint

double, float, real

float

date

date

datetime, timestamp

datetime

time, timespan

time

string, varchar

If length > 4000: nvarchar(max), Otherwise: nvarchar(length)

long, int64, bigint

bigint

boolean, bool

tinyint

decimal, numeric

decimal

uuid

nvarchar(length)

binary, varbinary, longvarbinary

binary(1000) or varbinary(max) after SQL Server 2000

Special Considerations

  • String/VARCHAR: String columns from Databricks can map to different data types depending on the length of the column. If the column length exceeds 4000, then the column is mapped to nvarchar (max). Otherwise, the column is mapped to nvarchar (length).
  • DECIMAL Databricks supports DECIMAL types up to 38 digits of precision, but any source column beyond that can cause load errors.

About MongoDB Data Integration

Accessing and integrating live data from MongoDB has never been easier with CData. Customers rely on CData connectivity to:

MongoDB's flexibility means that it can be used as a transactional, operational, or analytical database. That means CData customers use our solutions to integrate their business data with MongoDB or integrate their MongoDB data with their data warehouse (or both). Customers also leverage our live connectivity options to analyze and report on MongoDB directly from their preferred tools, like Power BI and Tableau.

For more details on MongoDB use case and how CData enhances your MongoDB experience, check out our blog post: The Top 10 Real-World MongoDB Use Cases You Should Know in 2024.


Getting Started


Prerequisites

Create the project and add components

  1. Open Visual Studio and create a new Integration Services Project.
  2. Add a new Data Flow Task to the Control Flow screen and open the Data Flow Task.
  3. Add a CData MongoDB Source control and a CData Databricks Destination control to the data flow task.

Configure the MongoDB source

Follow the steps below to specify properties required to connect to MongoDB.

  1. Double-click the CData MongoDB Source to open the source component editor and add a new connection.
  2. In the CData MongoDB Connection Manager, configure the connection properties, then test and save the connection.

    Set the Server, Database, User, and Password connection properties to connect to MongoDB. To access MongoDB collections as tables you can use automatic schema discovery or write your own schema definitions. Schemas are defined in .rsd files, which have a simple format. You can also execute free-form queries that are not tied to the schema.

  3. After saving the connection, select "Table or view" and select the table or view to export into Databricks, then close the CData MongoDB Source Editor.

Configure the Databricks destination

With the MongoDB Source configured, we can configure the Databricks connection and map the columns.

  1. Double-click the CData Databricks Destination to open the destination component editor and add a new connection.
  2. In the CData Databricks Connection Manager, configure the connection properties, then test and save the connection. To connect to a Databricks cluster, set the properties as described below.

    Note: The needed values can be found in your Databricks instance by navigating to Clusters, selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

    • Server: Set to the Server Hostname of your Databricks cluster.
    • HTTPPath: Set to the HTTP Path of your Databricks cluster.
    • Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).

    Other helpful connection properties

    • QueryPassthrough: When this is set to True, queries are passed through directly to Databricks.
    • ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
    • UseUploadApi: Setting this property to true will improve performance if there is a large amount of data in a Bulk INSERT operation.
    • UseCloudFetch: This option specifies whether to use CloudFetch to improve query efficiency when the table contains over one million entries.
  3. After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert.
  4. On the Column Mappings tab, configure the mappings from the input columns to the destination columns.

Run the project

You can now run the project. After the SSIS Task has finished executing, data from your SQL table will be exported to the chosen table.

Ready to get started?

Download a free trial of the MongoDB SSIS Component to get started:

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Learn more:

MongoDB Icon MongoDB SSIS Components

Powerful SSIS Source & Destination Components that allows you to easily connect SQL Server with live MongoDB document databases through SSIS Workflows.

Use the MongoDB Data Flow Components to synchronize with MongoDB data. Perfect for data synchronization, local back-ups, workflow automation, and more!

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