In the modern era of business, data plays a pivotal role in driving critical financial and operational decisions. However, harnessing valuable insights from data often requires data science teams to aggregate information from various sources. This entails consolidating data from diverse pipelines, including Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) systems, Software as a Service (SaaS) platforms, and internal databases.
As organizations grow, the volume and complexity of data increase. Managing data manually becomes an arduous task for data teams. The proliferation of data sources leads to a surfeit of data pipelines, making it challenging for data scientists to oversee them all effectively. Ensuring data accuracy and timeliness in such a scenario becomes daunting. While many businesses rely on Workflow Management Systems (WMS) to create and schedule jobs, older WMS systems often prove inefficient and difficult to scale.
A notable example of grappling with this challenge is Airbnb, which, unable to find a suitable software solution, developed its own tool known as Airflow. In 2017, Apache integrated Airflow into its open-source software stack. Apache Airflow distinguishes itself as more than just another WMS. It serves as a comprehensive platform that empowers data science teams to programmatically author, schedule, and monitor workflows.
This article delves into how businesses leverage Apache Airflow. To begin, let’s grasp the significance of constructing data pipelines within a data-centric enterprise.
Understanding Data Pipelines
Data pipelines function as conduits that transport raw data from source to destination. Sources encompass internal databases, CRMs, ERPs, or any tools that house vital business data. The destination typically comprises a data lake or data warehouse, where source data undergoes transformation to yield actionable business insights. During its journey from source to destination, data traverses several transformation steps that prepare raw data for analysis.
Establishing data pipelines assumes paramount importance for enterprises reliant on data-driven decision-making. Team members in such organizations frequently employ numerous applications for various business functions. Manual data collection from each app for analysis invites errors and redundancy. Data pipelines surmount these issues by unifying disparate data sources into a single destination, enabling rapid analyses and valuable insights.
Why Apache Airflow?
Apache Airflow, although resembling a WMS, offers a plethora of additional advantages for organizations grappling with extensive data pipelines.
Programmable Workflow Management
Airflow defines workflows as code, rendering them easily maintainable with robust version control. Its code-first approach fosters collaboration, facilitating the creation of more robust data pipelines and enabling seamless testing to ensure workflow integrity.
Extensibility
Another compelling feature of Apache Airflow is its full extensibility. Users can customize operators to interface with multiple databases, cloud services, and internal applications. Thanks to its popularity, Airflow benefits from a wealth of community-contributed operators.
Scalability
With a REST API at its core, Airflow boasts scalability, empowering data science teams to rapidly craft custom workflows from external sources and generate valuable insights.
Automation Capability
Airflow’s code-based configuration opens avenues for automation, allowing data engineers to streamline repetitive aspects of data pipeline construction. Tasks like data retrieval from APIs, data upload to databases, report generation, and email distribution can all be automated.
Easy Monitoring and Management
Airflow excels in user-friendliness with its robust UI, simplifying workflow monitoring and management. Data engineers can readily access logs, track workflow executions, and trigger task reruns in case of failures.
Alerting System
In the event of task failures, Airflow can send default email notifications or be configured to integrate with tools like Slack, facilitating swift responses to critical issues.
Benefits for Larger Organizations
In smaller organizations, workflows tend to be straightforward and manageable by a single data scientist. However, larger enterprises necessitate an infrastructure team to ensure the smooth operation of numerous data pipelines scattered throughout the organization. Given the vast and intricate data sets distributed across multiple sources, regular pipeline inspections are imperative to maintain data quality and address errors.
Airflow equips engineers with the tools needed to seamlessly connect disparate data sources, efficiently schedule workflows, and monitor them through an intuitive web-based interface.
Apache Airflow vs. Other Automation Solutions
While it’s possible to automate data pipelines using time-based schedulers like Cron, complications arise when dealing with multiple workflows with intricate dependencies. For instance, dependencies on third-party input data may lead to delays in triggering pipelines, affecting multiple teams reliant on this data within a broader pipeline.
Such scenarios are common in large organizations, and traditional schedulers like Cron struggle to handle these complexities. Establishing and managing task relationships becomes intricate and time-consuming when using Cron. In contrast, Airflow simplifies these challenges with Python scripting and provides robust tracking and monitoring capabilities through its UI.
How Airflow Automates
In Airflow, workflows, or pipelines, are represented as Directed Acyclic Graphs (DAGs). Each step in the DAG is defined as a specific task, programmable using Python. DAGs are further defined by necessary operators, relationships, and dependencies, all coded in Python. The sequence of these tasks creates triggers and automations based on events or schedules.
For instance, consider a simple workflow involving real-time data collection for a website’s landing page visits. Processed data for the marketing team gets fed into a machine learning model for instant analysis. With Airflow, automating these tasks becomes feasible, enabling marketing teams to access real-time statistics for campaign attribution.
Leveraging Apache Airflow with Xperity
Apache Airflow has proven to be a formidable tool for organizations such as Uber, Lyft, Netflix, and many others. With Apache Airflow, you can extract value from managing vast amounts of data swiftly. The tool also streamlines reporting and analytics by efficiently handling data pipelines. At Xperity, our team of Apache Airflow experts collaborates with your internal data science teams to maximize the potential of this open-source tool, helping you build more robust data pipelines.
Want to learn more about how Xperity can help you? Contact us today to start the conversation.
