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Begin by exporting the necessary data from Harness. If Harness provides a built-in export feature, use it to export your data into a CSV or JSON format, which can be easily handled by AWS services. Ensure that the exported file is saved locally on your system.
Install the AWS Command Line Interface (CLI) on your local machine. Once installed, configure it by running `aws configure` and provide your AWS Access Key, Secret Key, region, and preferred output format. This setup will allow you to interact with AWS services directly from your terminal.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket where you will store the exported data from Harness. Ensure that the bucket name is unique across AWS and select a region close to where you will perform your AWS Glue operations to minimize latency.
Use the AWS CLI to upload the exported data file to the newly created S3 bucket. Run the following command, replacing ``, ``, and `` with your respective file path, bucket name, and desired object name:
```
aws s3 cp s3:///
```
This command will place your Harness data into S3, ready for further processing.
Navigate to the AWS Glue service in the AWS Management Console. Create a new crawler that will scan the data in your S3 bucket and automatically infer the schema. Configure the crawler to point to the S3 bucket location and set the appropriate IAM role that has permissions to access the S3 bucket and create entries in the Glue Data Catalog.
Execute the Glue crawler to populate the Glue Data Catalog with table definitions based on the data structure in your S3 bucket. This process will facilitate seamless data transformation and querying within AWS Glue. Ensure that the crawler runs successfully and verify the table schema in the Data Catalog.
Set up an AWS Glue ETL job to transform or load the data as needed. In the AWS Glue console, create a new job, specify the script language (Python or Scala), and provide the necessary script that defines your data transformation logic. Assign the job to use the IAM role with S3 and Glue permissions. Finally, run the job to execute the ETL process, which can output the processed data back to another S3 bucket or any target data store supported by AWS Glue.
This guide outlines a direct approach to moving and processing data from Harness to S3 using AWS Glue, leveraging AWS's built-in services and tools.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
The harness is the industry’s first Software Delivery stage to use AI to facilitate your DevOps processes - CI, CD & GitOps, Feature Flags, Cloud Costs, and much more. Our AI takes your distribution pipelines to the next level. You can automate yellow verifications, prioritize what tests to run, condition the impact of changes, automate cloud costs, and much more. Lead your delivery pipelines with familiar developer knowledge-YAML, Git Commits. Remove all unnecessary toil and speed up developer productivity.
Harness's API provides access to a wide range of data related to software delivery and deployment. The following are the categories of data that can be accessed through Harness's API:
1. Applications: Information related to the applications being deployed, including their names, versions, and deployment status.
2. Environments: Details about the environments where the applications are being deployed, such as their names, types, and configurations.
3. Pipelines: Information about the pipelines used for software delivery, including their names, stages, and execution status.
4. Workflows: Details about the workflows used for software deployment, such as their names, steps, and execution status.
5. Artifacts: Information about the artifacts used in the software delivery process, including their names, versions, and locations.
6. Metrics: Data related to the performance of the software delivery process, such as deployment frequency, lead time, and mean time to recovery.
7. Logs: Details about the logs generated during the software delivery process, including their content, timestamps, and severity levels.
8. Notifications: Information about the notifications sent during the software delivery process, such as their types, recipients, and content.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
