Data integration is an integral part of modern business strategy, enabling businesses to convert raw data into actionable information and make data-driven decisions. Tools like Apache Airflow are used and popular for workflow automation. However, its technical complexities and steeper learning curve can create a challenge for teams that require an efficient real-time data pipeline.
This blog compares and contrasts two popular data integration solutions, Hevo vs Airflow, head-on to highlight how Hevo is a better choice for businesses looking for seamless data integration.
Hevo Overview
G2 Rating: 4.3 out of 5 stars (239)
Hevo is a no-code data pipeline platform designed to simplify complex workflows and seamlessly integrate data from multiple sources to destinations/data warehouses. With its easy-to-use UI and 150+ pre-built connectors, data integration is efficient as well as rapid. Hevo also provides advanced transformational capabilities and real-time data integration to make sure your data is always analysis-ready. Its fault-tolerance architecture and round-the-clock support ensure a reliable and maintenance-free experience.
Apache Airflow Overview
G2 Rating: 4.3 out of 5 stars (86)
Apache Airflow is an open-source platform for workflow orchestration, originally designed and started at Airbnb. It uses DAGs (Directed Acyclic Graphs) to schedule jobs across servers and nodes. It is also used to manage and maintain data pipelines. To deploy a workflow on Apache Airflow, you require knowledge of Python. Airflow provides various modules to connect to various data sources and destinations. Its configuration can be challenging for non-developers or users using it for the first time.
Say goodbye to the complexities of Airflow. With Hevo, you can set up pipelines in minutes, enjoy zero maintenance and make sure of data accuracy at scale. Hevo allows businesses to move beyond technical challenges and focus on extracting actionable insights from their data.
Check out what makes Hevo amazing:
- It has a highly interactive UI, which is easy to use.
- It streamlines your data integration task and allows you to scale horizontally.
- The Hevo team is available round the clock to extend exceptional support to you.
Hevo has been rated 4.7/5 on Capterra. Know more about our 2000+ customers and give us a try.
Get Started with Hevo for Free
Head-On: Hevo vs Airflow
| Features | Hevo | Airflow |
| Ease of Use | No-code platform with easy setup | Requires advanced coding skills |
| Automation | Pre-built connectors and auto schema mapping feature | Requires manual setup and maintenance, which is often time-consuming |
| Real-time Data | Offers real-time sync with minimal latency | Batch processing. Often delayed. |
| Scalability | Hevo automatically scales with data volume. | Airflow requires manual intervention. |
| Maintenance | Hevo requires zero maintenance and has a fault-tolerant design. | High maintenance effort |
| Integration | Offers extensive pre-built connectors | Limited; It requires custom scripting |
| Error Handling | Automated and built-in | Manual debugging |
| Support | Offers round-the-clock customer support and extensive documentation. | Airflow has open-source community support with limited dedicated assistance. |
Case Study: How StockGro Improved Data Integration by Shifting from Airflow to Hevo
StockGro Overview
StockGro is India’s first social investment platform designed to help users build their trading skills through a gamified environment. It has millions of downloads and also hosts various tools for connecting users with other investors and stock market experts.
Key Challenges Encountered
As StockGro grew, So did the volume and complexity of data. Handling millions of daily transactions across various mongoDB instances and migrating them to BigQuery became difficult and time-consuming. The team relied on Airflow for their data migration process and faced significant challenges:
“Earlier, Our pipelines were built using self-hosted Airflow, which required constant maintenance and couldn’t handle schema updates. In addition, it was slow, and as a result, it wasn’t real-time and often missed syncing some data. We spent between 5-10 hours per week to maintain these pipelines, plus additional hours to add new collections for syncing. And despite this, we had to run the scripts manually to generate some critical reports.”
– Raman Tehlan, Founding Engineer, StockGro
Recognizing and facing these issues, Raman and the team started exploring other options and alternatives for Airflow to handle their growing demand and configure their data stack for the future.
The Switch to Hevo
After evaluating various tools available in the market, StockGro chose Hevo for its no-code setup and scalability. Setting up initial pipelines was quick, and within days, the team transitioned completely to Hevo.
Key Features That Made a Difference
- Auto Schema Mapping: New Tables were automatically mapped from source to target destination without any manual intervention.
- Zero Maintenance: Hevo pipelines ran seamlessly with no need for frequent oversight. What also helped was its fault-tolerant architecture.
- Wide range of connectors: Hevo offered various pre-built connectors to seamlessly integrate various sources to destinations/data warehouses.
Impact of Switching to Hevo
- Maintenance free: Maintenance hours that used to take 5-10 hours were reduced to 0.
- Real-time Data Sync: Everyone on the team now has access to real-time, accurate data, which reduces the decision-making time.
- Scalability: Hevo scaled effortlessly and efficiently with StockGro’s 5x data growth, maintaining reliability and syncing speed.
“Everyone in the team now has access to real-time, accurate data, making it easy to move fast. Hevo has made it effortless for us to maintain pipelines, and the whole process is very reliable and scalable.”
– Raman Tehlan, Founding Engineer, StockGro
Integrate Salesforce to Snowflake
Integrate Google Ads to Redshift
Integrate MongoDB to BigQuery
Hevo vs Airflow: A Comparative Breakdown in 5 Points
1. Ease of Use
Hevo is a no-code tool designed for simplicity. Users can set up data pipelines within minutes without any technical expertise. Its intuitive user interface and design ensure that non-engineering teams can also easily set up workflows.
Airflow is powerful, but it requires technical know-how to set up and maintain workflows. It requires technical expertise in Python. It is suitable for teams with strong engineering backgrounds.
2. Pricing
Hevo is a paid Saas platform with transparent pricing plans. It offers various tiers of pricing based on data volume. It may seem costlier upfront, but it significantly reduces operational and maintenance costs by eliminating the need for a dedicated team.
Airflow is open-source and free to use, but hidden costs arise from hosting, maintaining, and troubleshooting data pipelines. The need for skilled engineers proficient in Python increases the overall cost.
3. Real-Time Data Integration
Hevo is designed for real-time data synchronization, which ensures up-to-date insights for swift decision-making. Its built-in features like auto-mapping and advanced transformational capabilities always make the data analysis ready.
Airflow is designed for batch processing. It lacks native support for handling real-time data. These limitations often result in delays, and the teams have to use other tools along with Airflow, to make up for this gap.
4. Scalability
Hevo is built for scalability and can handle larger volumes of data with ease. It also can support the growing sources needed for the business. It scales with the business needs, maintaining the same level of accuracy and syncing speed.
Scalability with Airflow can be a bit of a bottleneck. It works fine with orchestrating small, pre-defined workflows but struggles with high volumes of data and real-time data integration without extensive tailoring and customization.
5. Support and Customer Assistance
Hevo provides dedicated customer support around the clock, which ensures that issues are resolved swiftly without causing major disruptions to the workflow. It also provides extensive documentation and resources for smaller and simpler queries.
As an open-source tool, Airflow relies heavily on community support. Its active user base and forum can be useful for resolving simple queries. However, businesses may face challenges with time-sensitive problems and complex customizations.
How to Migrate from Airflow to Hevo
- Assess current data pipeline and integration needs and requirements.
- Use Hevo’s pre-built connectors to configure the pipeline with minimal effort and time.
- Hevo allows automatic synchronization and mapping features to map data from source to destination without manual intervention. It also allows both pre and post-transformation capabilities to ensure data is always analysis-ready.
- Use Hevo’s pipeline monitoring tools to track pipeline performance and the status of integrated data.
Missed our webinar?
Watch the recording of How PushPress Built a Self-Serve Analytics Engine with a Lean Team. Learn from real-world success stories and see how PushPress migrated from Airflow to Hevo, enhancing their SaaS business model and product analysis with actionable dashboards.
Migrate Data Without the Complexity — Choose Hevo
No credit card required
Conclusion
Apache Airflow might be ideal for workflow orchestration, but its steep learning curve and maintenance demands make it less effective for smooth and seamless data integration. Hevo’s no-code platform, on the other hand, is designed to empower businesses like StockGro to focus on analytics rather than data pipeline maintenance. Hevo delivers real-time data that is not only reliable but also scalable with minimal effort.
As data volumes and the complexity of data grow, investing in a solution like Hevo Data ensures your team can focus on using this data for business success rather than being bogged down by technical hurdles. If you are currently using Airflow for data integration and migration and feel constrained by its limitations. Hevo provides a modern, efficient, highly scalable, and future-ready alternative. Sign up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.
FAQs
1. Who are the competitors of Airflow?
There are different Apache Airflow alternatives, such as Hevo Data, Luigi, Prefect, and Dagster.
2. When to not use Airflow?
Do not use Airflow when you need real-time data integration, larger volumes of data, or your team does not have the required expertise in Python. Its setup, maintenance, and customization process make it not user-friendly for non-technical users.
3. Does Airflow cost money?
As an open-source project, Apache Airflow is free to download, use, and customize. However, the total cost of ownership includes other factors such as Infrastructure, Development, Scaling, and Maintenance.
With over a decade of experience, Sarad has been instrumental in designing and developing Hevo's fundamental components. His expertise lies in building lean solutions for various software challenges. Sarad is passionate about mentoring fellow engineers and continually exploring new technologies to stay at the forefront of the industry. His dedication and innovative approach have made significant contributions to Hevo's success.
