Migrating data from Jira to Databricks using CData SSIS Components.

Easily push Jira data to Databricks using the CData SSIS Tasks for Jira 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 Jira data to Databricks using the CData SSIS Components for Jira 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 Jira Data Integration

CData simplifies access and integration of live Jira data. Our customers leverage CData connectivity to:

Gain bi-directional access to their Jira objects like issues, projects, and workflows.
Use SQL stored procedures to perform functional actions like changing issues status, creating custom fields, download or uploading an attachment, modifying or retrieving time tracking settings, and more.
Authenticate securely using a variety of methods, including username and password, OAuth, personal access token, API token, Crowd or OKTA SSO, LDAP, and more.

Most users leverage CData solutions to integrate Jira data with their database or data warehouse, whether that's using CData Sync directly or relying on CData's compatibility with platforms like SSIS or Azure Data Factory. Others are looking to get analytics and reporting on live Jira data from preferred analytics tools like Tableau and Power BI.

Learn more about how customers are seamlessly connecting to their Jira data to solve business problems from our blog: Drivers in Focus: Collaboration Tools.

Getting Started

Prerequisites

Visual Studio 2022
SQL Server Integration Services Projects extension for Visual Studio 2022
CData SSIS Components for Databricks
CData SSIS Components for Jira

Create the project and add components

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

Configure the Jira source

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

Double-click the CData Jira Source to open the source component editor and add a new connection.
In the CData Jira Connection Manager, configure the connection properties, then test and save the connection.
To connect to JIRA, provide the User and Password. Additionally, provide the Url; for example, https://yoursitename.atlassian.net.
After saving the connection, select "Table or view" and select the table or view to export into Databricks, then close the CData Jira Source Editor.

Configure the Databricks destination

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

Double-click the CData Databricks Destination to open the destination component editor and add a new connection.
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.
After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert.
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 Jira SSIS Component to get started:

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Learn more:

Jira SSIS Components

Powerful SSIS Source & Destination Components that allows you to easily connect SQL Server with live Jira data through SSIS Workflows.

Use the Jira Data Flow Components to synchronize with Jira Projects, Groups, Workflows, etc. Perfect for data synchronization, local back-ups, workflow automation, and more!