Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
N/A
Skyvia
Score 10.0 out of 10
Mid-Size Companies (51-1,000 employees)
Skyvia is a no-code cloud data integration platform for ETL, ELT, Reverse ETL, data migration, one-way and bi-directional data sync, workflow automation, and real-time connectivity. Benefits of Using Skyvia: • Cost efficiency: With flexible pricing plans for each product, Skyvia suites for businesses of any size. • Flexibility: Skyvia provides adaptable, no-code integration tools for both basic and advanced business…
$99
per month
Pricing
Apache Airflow
Skyvia
Editions & Modules
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Basic
$99
per month
Standard
$199
per month
Professional
$249
per month
Offerings
Pricing Offerings
Apache Airflow
Skyvia
Free Trial
No
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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20% discount for annual pricing.
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Community Pulse
Apache Airflow
Skyvia
Features
Apache Airflow
Skyvia
Workload Automation
Comparison of Workload Automation features of Product A and Product B
Apache Airflow
8.7
12 Ratings
5% above category average
Skyvia
-
Ratings
Multi-platform scheduling
9.312 Ratings
00 Ratings
Central monitoring
8.912 Ratings
00 Ratings
Logging
8.512 Ratings
00 Ratings
Alerts and notifications
9.312 Ratings
00 Ratings
Analysis and visualization
6.712 Ratings
00 Ratings
Application integration
9.412 Ratings
00 Ratings
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Airflow
-
Ratings
Skyvia
10.0
23 Ratings
19% above category average
Connect to traditional data sources
00 Ratings
10.023 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Airflow
-
Ratings
Skyvia
10.0
20 Ratings
20% above category average
Simple transformations
00 Ratings
10.020 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.
1. It can be connected to slack for companies that uses that system. 2. Also can be connected to Google Drive to share information from spreadsheets and csv data. 3. It works efficiently with WordPress websites which are the most used platform for web development. 4. It connects ORACLE's databases with different systems online and that's very useful.
Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.
UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.
For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.
Because as a developer and programmer is very easy to work with and provides many options to very complex problems. It provides guides for integration that are very useful and makes us save a lot of money and time on integrations. Also Skyvia is constantly adding more data systems on their lists for us to provide as web managers.
Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.
Compared to similar user-friendly data integration systems like Integrate.io, Stitch, or Azure, Skyvia still wins in the number of connectors and pricing policy. Skyvia is a tool for companies like ours, starving for simple but, at the same time, robust and cost-effective data transfer solutions. It doesn't require additional knowledge for implementation and usage because it's no code. We'd recommend Skyvia as the best solution for non-technical staff.
Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
The core of our business lies in analyzing data from various sources, and while ETL itself is not our primary focus, it is a necessary process to achieve our goals. Thanks to Skyvia, we are able to streamline the ETL process at a reasonable price, allowing us to dedicate our efforts to data analysis without the burden of ETL complexities.