Apache Airflow vs. Datastreamer

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Airflow
Score 8.5 out of 10
N/A
Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.N/A
Datastreamer
Score 7.3 out of 10
N/A
Datastreamer is turnkey data platform to source, unify, and enrich unstructured data with less work than building data pipelines in-house. Traditional ETL processes and pipelines might not meet the needs of organizations who want to implement unstructured and semi-structured sources such as external social media, blogs, news, forums, and dark web data into their products. This leaves data teams to build pipelines internally which comes with time-draining technical complexities and…N/A
Pricing
Apache AirflowDatastreamer
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache AirflowDatastreamer
Free Trial
NoNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional DetailsPricing is determined by which data sources, AI models, and components are added to a pipeline multiplied by the data volume. This is highly-customizable and varies by use-case. Reach out to our team for a demo to discuss pricing for your specific needs.
More Pricing Information
Community Pulse
Apache AirflowDatastreamer
Top Pros

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Top Cons

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Features
Apache AirflowDatastreamer
Workload Automation
Comparison of Workload Automation features of Product A and Product B
Apache Airflow
8.2
9 Ratings
0% above category average
Datastreamer
-
Ratings
Multi-platform scheduling8.89 Ratings00 Ratings
Central monitoring8.49 Ratings00 Ratings
Logging8.19 Ratings00 Ratings
Alerts and notifications7.99 Ratings00 Ratings
Analysis and visualization7.99 Ratings00 Ratings
Application integration8.49 Ratings00 Ratings
Best Alternatives
Apache AirflowDatastreamer
Small Businesses

No answers on this topic

Skyvia
Skyvia
Score 9.7 out of 10
Medium-sized Companies
ActiveBatch Workload Automation
ActiveBatch Workload Automation
Score 8.5 out of 10
Astera Data Pipeline Builder (Centerprise)
Astera Data Pipeline Builder (Centerprise)
Score 8.9 out of 10
Enterprises
Redwood RunMyJobs
Redwood RunMyJobs
Score 9.3 out of 10
Astera Data Pipeline Builder (Centerprise)
Astera Data Pipeline Builder (Centerprise)
Score 8.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache AirflowDatastreamer
Likelihood to Recommend
7.6
(9 ratings)
7.3
(1 ratings)
User Testimonials
Apache AirflowDatastreamer
Likelihood to Recommend
Apache
For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.
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Datastreamer
Datastreamer has great competency in aggregation and classification of large amounts of unstructured, conversational/social data. We perform media monitoring on social media data which is infinitely large and changing every second. Datastreamer is able to stream that high volume of complex data reliably. There are other solutions better suited for small data movement efforts. The AI models and operations set Datastreamer apart from simple web API's that only collect data and pass it on without augmenting it's value. Very appropriate for organizations looking to use this type of information to understand and classify sentiment, identify themes/insights to assist in decision making across multi-department roles in an organization: PR, marketing, security etc.
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Pros
Apache
  • In charge of the ETL processes.
  • As there is no incoming or outgoing data, we may handle the scheduling of tasks as code and avoid the requirement for monitoring.
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Datastreamer
No answers on this topic
Cons
Apache
  • they should bring in some time based scheduling too not only event based
  • they do not store the metadata due to which we are not able to analyze the workflows
  • they only support python as of now for scripted pipeline writing
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Datastreamer
No answers on this topic
Alternatives Considered
Apache
There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.
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Datastreamer
No answers on this topic
Return on Investment
Apache
  • A lot of helpful features out-of-the-box, such as the DAG visualizations and task trees
  • Allowed us to implement complex data pipelines easily and at a relatively low cost
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Datastreamer
No answers on this topic
ScreenShots

Datastreamer Screenshots

Screenshot of Platform overview graphic