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Warehouses and Lakes
Engineering Analytics

How to load data from Jira to AWS Datalake

Learn how to use Airbyte to synchronize your Jira data into AWS Datalake within minutes.

TL;DR
What is Jira
What is AWS Datalake
Prerequisites
Step 1: Setup Jira
Step 2: Setup AWS Datalake
Step 3: Setup a connectionUse CasesWrapping Up
Example H2
Example H3

Sync data between Jira and AWS Datalake

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TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up Jira as a source connector (using Auth, or usually an API key)
  2. set up AWS Datalake as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Jira

Jira is an issue tracking software by Atlassian that assists developers in bug tracking and agile project management. With software support throughout the entire development process, from planning to tracking, to the final release, and reports based on real-time data to improve team performance, Jira is the go-to software development tool for agile teams.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

Integrate Jira with AWS Datalake in minutes

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Prerequisites

  1. A Jira account to transfer your customer data automatically from.
  2. A AWS Datalake account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Jira and AWS Datalake, for seamless data migration.

When using Airbyte to move data from Jira to AWS Datalake, it extracts data from Jira using the source connector, converts it into a format AWS Datalake can ingest using the provided schema, and then loads it into AWS Datalake via the destination connector. This allows businesses to leverage their Jira data for advanced analytics and insights within AWS Datalake, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Jira as a source connector

1. First, navigate to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.

2. Click on the "Add Source" button in the top right corner of the screen.

3. Select "Jira" from the list of available sources.

4. Enter a name for your Jira source connector and click "Next".

5. Enter your Jira credentials, including the Jira URL, email address, and API token.

6. Test the connection to ensure that the credentials are correct and the connection is successful.

7. Select the Jira projects and issue types that you want to replicate in Airbyte.

8. Choose the replication frequency and any other settings that you want to apply to your Jira source connector.

9. Click "Create Source" to save your Jira source connector and begin replicating data from Jira to Airbyte.  

It is important to note that the specific steps for connecting your Jira source connector may vary depending on your specific use case and the version of Jira that you are using. For more detailed instructions and troubleshooting tips, refer to the Airbyte documentation or consult with a Jira expert.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your Jira data to AWS Datalake

Once you've successfully connected Jira as a data source and AWS Datalake as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Jira from the dropdown list of your configured sources.
  3. Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific Jira objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Jira to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your Jira data.

Use Cases to transfer your Jira data to AWS Datalake

Integrating data from Jira to AWS Datalake provides several benefits. Here are a few use cases:

  1. Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Jira data, extracting insights that wouldn't be possible within Jira alone.
  2. Data Consolidation: If you're using multiple other sources along with Jira, syncing to AWS Datalake allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: Jira has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Jira data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Jira data.
  6. Data Science and Machine Learning: By having Jira data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Jira provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Jira table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Jira account as an Airbyte data source connector.
  2. Configure AWS Datalake as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Jira to AWS Datalake after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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Connectors Used

Jira
AWS Datalake

Related Syncs with Jira

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

Jira
AWS Datalake

Related Syncs with Jira

Jira to AWS Datalake
Jira to Amazon SQS
Jira to BigQuery
Jira to Databricks Lakehouse
Jira to Firebolt
Jira to Redshift
Jira to S3
Jira to S3 Glue
Jira to Snowflake Data Cloud
Jira to Teradata

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Frequently Asked Questions

What data can you extract from Jira?

Jira's API provides access to a wide range of data related to project management and issue tracking. The following are the categories of data that can be accessed through Jira's API:  

1. Issues: This includes all the information related to the issues such as issue type, status, priority, description, comments, attachments, and more.  

2. Projects: This includes information about the projects such as project name, description, project lead, and more.  

3. Users: This includes information about the users such as user name, email address, and more.  

4. Workflows: This includes information about the workflows such as workflow name, workflow steps, and more.  

5. Custom fields: This includes information about the custom fields such as custom field name, type, and more.  

6. Dashboards: This includes information about the dashboards such as dashboard name, description, and more.  

7. Reports: This includes information about the reports such as report name, description, and more.  

8. Agile boards: This includes information about the agile boards such as board name, board type, and more.  

Overall, Jira's API provides access to a vast amount of data that can be used to improve project management and issue tracking.

What data can you transfer to AWS Datalake?

You can transfer a wide variety of data to AWS Datalake. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Jira to AWS Datalake?

The most prominent ETL tools to transfer data from Jira to AWS Datalake include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Jira and various sources (APIs, databases, and more), transforming it efficiently, and loading it into AWS Datalake and other databases, data warehouses and data lakes, enhancing data management capabilities.