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- Home page of The Apache Software Foundation. The ASF develops, shepherds, and incubates hundreds of freely-available, enterprise-grade projects that serve as the backbone for some of the most visible and widely used applications in computing today.
- Airflow is primarily a workflow engine and the execution of transformation happens in either source or target database. This means the developers need to be an expert in both source and destination capabilities and should spend extra effort in maintaining the execution engines separately.
Airflow is a platform created by the community to programmatically author, schedule, and monitor workflows. Machine learning is the hot topic of the industry. It won't be so cool if not for the data processing involved Airflow is an ETL (Extract, Transform, Load) workflow orchestration tool, used in data transformation pipelines.
Amazon Managed Workflows is a new managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines in the cloud at scale. Apache Airflow is an open source tool used to programmatically author, schedule, and monitor sequences of processes and tasks referred to as “workflows”.
Organizations want to leverage massive amounts of data for analytics and insights. To effectively use this data, customers often need to first build a workflow that defines the series of sequential tasks that prepare and process the data. Many customers use Apache Airflow to take advantage of its open source community and large library of pre-built integrations to third-party data processing tools such as Apache Spark and Hadoop. With Apache Airflow, workflows are created as Python scripts that are familiar to data engineers and data scientists. However, data engineers need to install, maintain, scale, and secure Airflow themselves, which adds cost and operational complexity.
Now, data engineers and data scientists can use the same open source Airflow platform and Python language to create workflows without having to manage the underlying infrastructure for scalability, availability, and security. Managed Workflows automatically scales its workflow execution capacity to meet your needs, and is integrated with AWS security services to enable fast and secure access to data.
There is no up-front cost commitment or minimum fee for using Managed Workflows , and you pay only for what you use.
You can get started with Managed Workflows using the AWS Management Console, Command Line Interface (CLI), AWS CloudFormation or SDK. You can create an account and begin deploying Directed Accyclic Graphs (DAGs) to your Airflow environment immediately without reliance on development resources or provisioning infrastructure.
Managed Workflows is generally available today in the following regions: US East (Ohio and N. Virginia), US West (Oregon), EU (Stockholm, Ireland, and Frankfurt), and Asia Pacific (Tokyo, Singapore, and Sydney), with more regions to follow.
To learn more about Managed Workflows:
- Read Jeff Barr's blog post
- Visit the Amazon Managed Workflows product page
- Read the Amazon Managed Workflows User Guide
Organizations with a separate transactional database and data warehouse typically have a large number of data engineering activities. They extract, transform, and load data from a variety of sources to their data warehouse. The sources include their own customer transactions, data from SAAS (Software as a Service) offerings as well as third party data which could be beneficial for analyzing customer behavior. Such ETL jobs are managed by ETL frameworks that help in organizing the jobs into directed workflow graphs, monitor them, and keep track of the service level agreements.
Airflow ETL is one such popular framework that helps in workflow management. It is excellent scheduling capabilities and graph-based execution flow makes it a great alternative for running ETL jobs. This post will help you to learn the basics of Airflow and execute an ETL job to transfer data from Amazon S3 to Redshift.
The goal of this post is to familiarize developers about the capabilities of airflow and to get them started on their first ETL job implementation using Airflow.
Table Of Contents
- Methods to Perform Airflow ETL
- Method 1: Using Airflow as Primary ETL Tool
Understanding Airflow ETL
Airflow is an open-source framework and can be deployed in on-premise servers or cloud servers. It has built-in connectors to most of the Industry standard source and target combinations. It also allows writing custom plugins for databases that are not supported out of the box. Airflow provides a directed acyclic graph view which helps in managing the task flow and serves as a documentation for the multitude of jobs. It also has a rich web UI to help with monitoring and job management. Know more here.
Methods To Perform An Airflow ETL Job
Two popular methods that can be used to perform an Airflow ETL job are:
Making use of custom code to perform an ETL Job is one such way. Airflow works on the basis of a concept called operators. Operators denote basic logical blocks in the ETL workflows. It could be anything from the movement of a file to complex transformations. A task is formed using one or more operators. Multiple tasks are stitched together to form directed acyclic graphs.
Hevo Data provides a hassle-free & a fully managed solution using its No-Code Data Pipelines. It helps you export the data effortlessly from Airflow without any intervention. Hevo’s pre-built integration with Airflow will take full charge of the data export process, allowing you to focus on key business activities.
Prerequisites
You would need the following before you could move on to performing an Airflow ETL job:
- Python 3.6 or above. You can click here, in case you need to download Python.
- Airflow installed and configured in the system. In case you do not have it installed already, you can follow this guide to set it up.
- An AWS account with permissions for S3 and Redshift.
Methods to Perform an Airflow ETL Job
Two popular methods that can be used to perform an Airflow ETL job are:
Method 1: Using Airflow as a Primary ETL Tool
Airflow works on the basis of a concept called operators. Operators denote basic logical blocks in the ETL workflows. It could be anything from the movement of a file to complex transformations. A task is formed using one or more operators. Multiple tasks are stitched together to form directed acyclic graphs. Every ETL job is a DAG for airflow.
Performing an Airflow ETL job involves the following steps:
We will now dig deep into each of the above steps of executing an Airflow ETL job.
Step 1: Preparing the Source and Target Environments
Our input file for this exercise looks as below.
Www.airflow.com
You will now login to Redshift console and create a table to hold this table. Use the below command for this.
Step 2: Starting the Airflow Web Server
Use the below command to start airflow web server
Once started, you can access the UI at localhost:8080.
Go to localhost:8080 to view the airflow UI.
Step 3: Creating a Connection to S3
To create a connection to S3, go to the Admin tab, and select connections.
Airflow Products Company Filters
Click on create and select S3 in the ‘conn type’ as below.
In the ‘Extra’ section, add your AWS credentials below.
Leave all sections other than ‘conn id’ and ‘conn type’ blank. We will be using the ‘conn id’ when we create DAG in the following steps.
Step 4: Creating a Redshift Connection
Click ‘Create’ in the connections tab and add details as below.
As above, in the Extras section add the credentials in JSON format. In the ‘conn type’ section use Postgres. Can i download windows 10 minecraft on mac.
Step 5: Creating the DAG File
The DAG file will use an operator called s3_to_redshift_operator. Place the below file in the ‘dags’ folder located in the airflow installation directory. If this folder does not already exist, feel free to create one and place the file in there.
The above code defines a DAG and an associated task that uses the default s3_to_redshift_operator. The above code is implemented to run once on a 1-6-2020. It also specifies two retries in case the job fails. After placing this file in the ‘dags’ folder, refresh the webserver UI and you will notice the new DAG appearing as below.
Step 6: Triggering the Job and Monitoring the Results
To trigger the job, use the far left button on the right-hand side of the DAG list. Also, make the OFF button on the left-hand side ON. Once the run button is pressed, switch to the DAG runs view in Admin section and you will notice the status of the job as ‘running’.
For monitoring, there is another view called graph view, which shows the full graph of each DAG and status of each task in the graph.
The graph view of our ETL job is as below. This view is very helpful in case of dags with multiple tasks.
It shows our task as green, which means successfully completed. Access the Redshift console again and you will find the data copied to Redshift table.
And that concludes our steps to execute this simple S3 to Redshift transfer. Airflow is capable of handling much more complex DAGs and scheduling scenarios. That said, it is not without its limitations. You will now learn about some of the typical challenges in using airflow as your primary ETL tool.
Challenges Involved in using Airflow as a Primary ETL Tool
- The above transfer works fine in case of one-off loads. But typically the requirement is for a continuous load. In this case, a staging table and additional logic to handle duplicates will all need to be part of the DAG.
- Airflow works based on operators. Even though there are many built-in and community-based operators available, support for SAAS offerings is limited in airflow. If you are someone who uses a lot of SAAS applications for running your business, your developers will need to implement airflow plugins to connect to them and transfer data.
- Even though airflow provides a web UI, the DAG definition is still based on code or configuration.
- Airflow is primarily a workflow engine and the execution of transformation happens in either source or target database. This means the developers need to be an expert in both source and destination capabilities and should spend extra effort in maintaining the execution engines separately.
- Transformation operators in Airflow are limited and in most cases, developers will have to implement custom ones.
If all of those challenges seem too much to address and you want your developers to focus on your core business logic, rather than spending time on maintaining and customizing an ETL framework, a cloud-based ETL tool like Hevo can be a great option for you.
Method 2: Execute an ETL job using a No-code Data Pipeline Platform, Hevo
Using Hevo will enable you to transfer data from Amazon S3 to Redshift within minutes without the involvement of manual scripts. Unlike Airflow ETL, Hevo works completely based on cloud and the user need not maintain any infrastructure at all. With Hevo, You can execute an ETL job from S3 to Redshift in two easy steps.
Step 1: Set-up the Source by Configuring Amazon S3
Step 2: Connect your Redshift Data Warehouse to Transfer Data
Well, that is all! Hevo will now stream data from S3 to Redshift in real-time. Sign up for a risk-free 14-day free trial here to take Hevo a whirl!
More Reasons to Try Hevo:
- Minimal Setup Time: Hevo can be set up on a visual interface in no time. This ensures that your ETL projects come to life instantly.
- 100’s of Data Integrations: Hevo can seamlessly ETL data from a wide array of data sources – databases, cloud applications, analytics applications, file storage, and so on. Explore the complete integration list here.
- Robust Data Transformations: Hevo can easily transform the data both before and after loading it into the warehouse. This ensures that the data is always ready for analysis.
- Automatic Schema Mapping: Once you have connected your data source, Hevo automatically detects the complete schema of the incoming data and maps it to the destination tables. This rids you of any additional schema mapping hassles when dealing large sets of data.
- Incremental Data Load: Hevo allows you to transfer the data that has changed or modified since the last data sync. This will ensure that there’s efficient utilization of bandwidth at both your source and destination systems.
- Dedicate Support: Hevo provides support over email, call, and chat, 24*7, ensuring that you always have a reliable partner to solve your ETL troubles in the hour of need.
While Airflow ETL is a great means to set up and manage your ETL pipeline free of cost, it is not without its limitations. If you are looking for a seamless way to set up your data pipeline infrastructure, do try out Hevo by signing up for a 14-day free trial here.