# Astronomer AI visibility in Data Engineering & ETL/ELT Pipelines

Canonical: https://devtune.ai/verticals/data-engineering-and-etl-elt-pipelines/astronomer

[Website](https://www.astronomer.io/)

Updated: 2026-09-29T18:51:39.473829+00:00
Prompts: 25
Runs: 6


## Platforms

- perplexity
- bing-copilot-search
- google-ai
- google-ai-mode
- chatgpt-search
- xai-search

Rank: 10
Total brands: 12
Measured responses: 150
Presence percent: 4.666666666666667
Share of voice percent: 2.3131672597864767
Average position: 44.46153846153846
Docs presence percent: 3.3333333333333335
Blog presence percent: 1.3333333333333335
Brand mention percent: 6


## Profile

Overview: Astronomer is a New York-based DataOps company and the primary commercial steward of Apache Airflow, the open-source workflow orchestration standard downloaded over 30 million times per month and used by 80,000+ organizations. Its flagship product, Astro, is a fully managed cloud platform that enables data engineering teams to build, deploy, observe, and govern data and AI pipelines at scale without managing Kubernetes infrastructure. Astro is available as a multi-tenant hosted service or as Astro Private Cloud for single-tenant deployments in a customer's own cloud environment. The company also develops Cosmos, a widely adopted open-source library for running dbt Core projects inside Airflow, and contributes the majority of Airflow's core engineering. Founded in 2015 and Series D-funded with $376M raised, Astronomer serves enterprises across financial services, gaming, retail, healthcare, and manufacturing.
Product summary: Astro by Astronomer is a fully managed DataOps platform built on Apache Airflow that abstracts away infrastructure complexity, enabling data engineers to write DAGs and deploy pipelines with enterprise-grade observability, CI/CD integration, and AI-assisted operations. It includes Astro Private Cloud for regulated environments, the Cosmos dbt integration, an Airflow MCP server for agentic workflows, and a proprietary Astro Executor for reliability and concurrency.


### Key capabilities

- Fully managed Apache Airflow orchestration (Astro cloud and Astro Private Cloud)
- Proprietary Astro Executor for higher concurrency and automatic failure recovery
- Deployments-as-Code with Terraform, CLI, and API support
- Native data observability, task-level lineage, and AI-powered root cause analysis
- Zero-downtime Airflow upgrades with 90-day rollback history
- Cosmos open-source tool for running dbt Core projects as Airflow DAGs
- Local development via Astro CLI and in-browser Astro IDE
- Airflow MCP server for agentic/AI pipeline control
- Multi-cloud deployment across AWS, GCP, and Azure
- Enterprise governance: RBAC, audit logging, SAML SSO, private networking



### Target users

- Data engineers building and maintaining production pipelines
- Data platform and infrastructure teams at mid-to-large enterprises
- ML/AI engineers managing model training and LLMOps workflows
- Analytics engineers integrating dbt with Airflow orchestration
- Enterprise data teams in financial services, healthcare, retail, and gaming
- DevOps/platform teams migrating from legacy schedulers (Oozie, Cron, MWAA)



### Key use cases

- ETL/ELT pipeline orchestration at enterprise scale
- MLOps and AI pipeline management (model training, validation, deployment)
- LLMOps and generative AI data workflows
- Data observability and SLA monitoring
- Cloud migration from legacy schedulers (e.g., Oozie, Cron)
- Operational analytics and automated reporting pipelines
- Multi-team data platform governance and self-service pipeline deployment

Integrations ecosystem: Astro inherits Airflow's ecosystem of 1,200+ community-supported provider building blocks covering databases, cloud platforms, SaaS tools, and data services. Key integrations include AWS (S3, SageMaker, ECS, Redshift), GCP (BigQuery, GCS, Composer), Azure (Blob Storage, Azure Container Instances), Snowflake, dbt Core (via open-source Cosmos), Kafka, Weaviate, Ray/Anyscale, Cohere, Slack, GitHub, and Bitbucket. Astro adds OpenLineage support for cross-system lineage, native CI/CD integration, SAML-based SSO, and secrets management. The Astronomer Registry serves as a discovery hub for Airflow providers and DAG templates. Available via AWS, Azure, and GCP marketplaces. LLM provider integrations (OpenAI, etc.) support AI/LLMOps workflows.
Pricing summary: Astro uses consumption-based pricing measured in Astro Units (AU), with four tiers: Developer (pay-as-you-go, free 14-day trial, smallest deployments from $0.35/hr), Team (pay-as-you-go or annual, dedicated clusters from $2.40/hr, includes private networking and audit logging), Business, and Enterprise (custom agreements, remote execution agents, SAML SSO). Buyers can subscribe via AWS, Azure, or GCP marketplaces. Self-hosted Astronomer Software starts around $25,000–$50,000/year for small deployments. Annual commitments typically yield 15-25% discounts; mid-market Astro deployments commonly run $30,000–$80,000/year. Enterprise support and professional services packages range from $15,000 to $100,000+.
Review summary: Users on G2 (4.5/5, 136 reviews) consistently praise Astro for simplifying Airflow management, eliminating the need for dedicated infrastructure/DevOps resources, and delivering strong customer support and documentation. The intuitive UI, CI/CD integrations with GitHub/Bitbucket, and automatic scaling are frequently highlighted. Criticisms center on pricing being high for smaller teams, less flexibility than self-hosted Airflow, occasional discrepancies between local and managed environments, and a steep learning curve for advanced features.
Competitive positioning: Astronomer positions as the definitive enterprise-grade managed Apache Airflow platform, differentiating on open-source stewardship (18 of Airflow's top committers and 10 PMC members on staff), a purpose-built execution engine (Astro Executor), and a full DataOps lifecycle—from local development through CI/CD, deployment, observability, and AI-assisted root-cause analysis. Against cloud-native managed Airflow services (AWS MWAA, GCP Composer), Astronomer emphasizes faster version adoption, superior developer experience, and enterprise observability. Against code-first orchestrators such as Dagster and Prefect, it competes on Airflow's ecosystem breadth (1,200+ providers), open-source lock-in avoidance, and organizational familiarity. Its Cosmos open-source tool for dbt-in-Airflow further broadens appeal in the modern data stack.
Limitations: Pricing can be prohibitive for smaller teams, with minimum monthly commitments and cloud networking pass-through costs adding 10-20% above base AU rates. The managed platform is less customizable than self-hosted Airflow, with some operators and configurations unavailable or behaving differently in the hosted environment. New users face a steep learning curve for advanced features such as worker queues, Kubernetes Executor billing, and infrastructure-as-code workflows. Some reviewers cite limited granularity in DAG run filtering and incomplete IAC controls for user access. Vendor lock-in concerns arise from dependency on Astronomer's proprietary executor and ecosystem tooling.


### Source urls

- https://www.astronomer.io/
- https://www.astronomer.io/about-us/
- https://www.astronomer.io/pricing/
- https://www.astronomer.io/press-releases/astronomer-secures-93-million-series-d-funding/
- https://www.g2.com/products/astro-by-astronomer/reviews
- https://tracxn.com/d/companies/astronomer/__Jr-0fGKWMmg_H7g0A_Rmcs5UJYsw4dWS7EidP44EXfI
- https://www.astronomer.io/case-studies/foursquare/
- https://www.astronomer.io/case-studies/edu-intelligence-by-welbee/
- https://www.astronomer.io/case-studies/autodesk-uses-astronomers-airflow-powered-orchestration-to-support-its-cloud/
- https://www.astronomer.io/integrations/
- https://www.vendr.com/marketplace/astronomer
- https://pitchbook.com/profiles/company/120275-56

Reviewed at: 2026-04-28T23:30:08.402+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Welbee (Edu Intelligence) | Migrated from Google Cloud Composer to Astro, gaining full pipeline visibility and scaling AI-driven education insights. Engineers can now pinpoint and resolve failures in half the previous time. | 50% reduction in troubleshooting time; 90% reduction in educator manual data analysis workload; 6M+ data transformations |
| Foursquare | Replaced a mix of self-hosted OSS Airflow and Luigi with Astro, centralizing data operations under a unified control plane and dramatically improving data discoverability. | 9,000+ data assets centralized; data discovery reduced from days to minutes |
| Autodesk | Migrated business-critical workflows from Oozie to Airflow via Astronomer Professional Services, enabling self-service pipeline deployment and scaling across engineering teams. | Migration completed in ~12 weeks; supported data engineering teams scaled from 25 to 50+ |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| G2 | 4.5 | 5 | 136 | https://www.g2.com/products/astro-by-astronomer/reviews |



### Review themes



#### Praised

- Simplifies Airflow deployment and management
- Eliminates need for dedicated DevOps/infrastructure team
- Intuitive and clean UI
- Strong customer support and responsiveness
- CI/CD integration with GitHub and Bitbucket
- Auto-scaling and reliability
- Excellent documentation and onboarding
- Built-in observability and pipeline monitoring



#### Criticized

- High cost, especially for smaller teams
- Steep learning curve for advanced features
- Less customizable than self-hosted Airflow
- Vendor lock-in concerns
- Some features/operators missing or limited vs. OSS Airflow
- Limited DAG run filtering (e.g., by logical date or user)
- Incomplete infrastructure-as-code (IAC) controls for user access
- Alert functionality less effective in some configurations




### Company facts

Founded year: 2015
Hq: New York, NY, USA


#### Founders

- Greg Neiheisel
- Ry Walker
- Tim Brunk

Employees range: 251-500
Total funding: ~$376M
Valuation: ~$1.2B (as of 2022 Series C)
Arr: Not available
Customer count: 800+
Status: Private (Series D)


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Fivetran | 46 | 150 | 30.666666666666664 | 26.7972972972973 |
| Airbyte | 37 | 150 | 24.666666666666668 | 22.18888888888889 |
| Integrate.io | 27 | 150 | 18 | 29.89887640449438 |
| Matillion | 25 | 150 | 16.666666666666664 | 24.08888888888889 |
| dbt | 23 | 150 | 15.333333333333332 | 21.891304347826086 |
| Dagster | 22 | 150 | 14.666666666666666 | 29.666666666666668 |
| Hevo Data | 10 | 150 | 6.666666666666667 | 38.46666666666667 |
| Rivery | 9 | 150 | 6 | 15.777777777777779 |
| Meltano | 8 | 150 | 5.333333333333334 | 35.36 |
| Astronomer | 7 | 150 | 4.666666666666667 | 44.46153846153846 |
| Hightouch | 3 | 150 | 2 | 28.083333333333332 |
| Census | 1 | 150 | 0.6666666666666667 | 41 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| perplexity | 0 | 0 |
| bing-copilot-search | 0 | 0 |
| google-ai | 0 | 0 |
| google-ai-mode | 0 | 0 |
| chatgpt-search | 0 | 0 |
| xai-search | 7 | 28.000000000000004 |



## Strengths





## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which ETL tools have an open API and SDK so we can build custom connectors for internal data sources quickly? | 6 |
| What ELT platforms give data engineers the best debugging experience when a pipeline fails mid-run with partial data loaded? | 4 |
| What data pipeline tools integrate natively with major cloud data warehouses for automatic schema management and optimized load performance? | 4 |
| Which ELT platforms maintain low-latency incremental syncs so dashboards reflect source data within minutes rather than hours? | 4 |
| Looking for an orchestration platform that integrates with my existing transformation layer — which tools support running SQL models as pipeline steps? | 4 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 2 |
| Developer Experience | 5 | 1 |
| Integrations & Ecosystem | 5 | 2 |
| Performance & Reliability | 5 | 1 |
| Setup & First Run | 5 | 1 |



## Prompt results

- Prompt text: Which data orchestration tools support complex multi-step pipelines with branching logic, sensors, and cross-team dependencies?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: 33



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Dagster | 2 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search





##### Xai-search

| Display name | Position |
| --- | --- |
| Dagster | 2 |
| dbt | 14 |
| Fivetran | 28 |
| Astronomer | 33 |


- Prompt text: Which ETL tools have an open API and SDK so we can build custom connectors for internal data sources quickly?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: 34



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Airbyte | 1 |
| Meltano | 3 |
| Fivetran | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Airbyte | 1 |
| Fivetran | 2 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Integrate.io | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| dbt | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Airbyte | 1 |
| Meltano | 2 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 3 |
| Airbyte | 8 |
| Fivetran | 21 |
| Astronomer | 34 |
| Meltano | 41 |


- Prompt text: I'm evaluating ETL platforms for a company starting its modern data stack — which tools are fastest to onboard and connect to a cloud warehouse?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 3 |
| Matillion | 5 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search





##### Xai-search

| Display name | Position |
| --- | --- |
| Fivetran | 3 |
| Integrate.io | 8 |
| Hevo Data | 10 |
| Meltano | 15 |
| Matillion | 27 |
| Airbyte | 74 |


- Prompt text: Which ELT platforms can sync billions of rows per day from a high-volume transactional database without impacting source system performance?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 3 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Fivetran | 2 |
| Integrate.io | 10 |
| dbt | 18 |
| Matillion | 19 |
| Rivery | 27 |
| Airbyte | 62 |


- Prompt text: What ELT platforms give data engineers the best debugging experience when a pipeline fails mid-run with partial data loaded?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Airbyte | 1 |
| Fivetran | 4 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Matillion | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Dagster | 1 |
| dbt | 4 |
| Fivetran | 5 |
| Matillion | 6 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| dbt | 5 |
| Dagster | 10 |
| Integrate.io | 11 |
| Matillion | 22 |


- Prompt text: Which ETL platforms have strong SLAs and automatic retry logic so data teams get alerted before business stakeholders notice pipeline delays?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: 32



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Matillion | 2 |
| Fivetran | 4 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 2 |
| Dagster | 4 |
| Matillion | 5 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 2 |
| Fivetran | 6 |
| dbt | 25 |
| Astronomer | 32 |
| Matillion | 60 |
| Hevo Data | 79 |
| Dagster | 87 |


- Prompt text: Which open-source ETL tools can be self-hosted on a single VM and are easy to configure without deep infrastructure knowledge?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: 17



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Meltano | 4 |
| Airbyte | 8 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Integrate.io | 7 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Airbyte | 1 |
| Meltano | 3 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 9 |
| Fivetran | 15 |
| Astronomer | 17 |
| Airbyte | 30 |
| Meltano | 50 |


- Prompt text: What ELT platforms handle schema drift and evolving source schemas automatically without breaking existing pipelines?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 4 |
| Matillion | 5 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Airbyte | 1 |



##### Google-ai-mode





##### Chatgpt-search





##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 1 |
| Hevo Data | 3 |
| Fivetran | 6 |
| Airbyte | 16 |
| dbt | 28 |
| Matillion | 34 |


- Prompt text: What data orchestration tools scale reliably to thousands of concurrent tasks without degrading scheduler performance?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search





##### Xai-search

| Display name | Position |
| --- | --- |
| Dagster | 10 |
| Matillion | 30 |
| Fivetran | 31 |
| Airbyte | 32 |


- Prompt text: Which data pipeline tools offer code-first transformation layers that data engineers can version-control and test like software?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| dbt | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search





##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 4 |
| dbt | 5 |
| Matillion | 9 |
| Dagster | 15 |
| Fivetran | 16 |


- Prompt text: I need a reverse ETL tool to sync data warehouse segments back to a CRM and ad platforms — which platforms do this best?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Hightouch | 1 |
| Integrate.io | 4 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Hightouch | 2 |



##### Xai-search

| Display name | Position |
| --- | --- |
| dbt | 4 |
| Rivery | 10 |
| Integrate.io | 12 |
| Hightouch | 30 |
| Census | 41 |


- Prompt text: Which data pipeline platforms can a small data team of 2 get running with managed connectors for 20+ sources without building custom integrations?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Rivery | 2 |
| Airbyte | 3 |
| Matillion | 4 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 2 |
| Hevo Data | 3 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 2 |
| Matillion | 7 |
| Fivetran | 12 |
| Hevo Data | 13 |
| Airbyte | 42 |


- Prompt text: What ETL platforms have built-in data quality checks and can alert the team when row counts or null rates deviate from expected ranges?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: 29



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Integrate.io | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Matillion | 1 |
| Fivetran | 2 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 4 |
| Fivetran | 24 |
| Matillion | 25 |
| dbt | 27 |
| Astronomer | 29 |


- Prompt text: Which data pipeline tools support real-time streaming ingestion alongside batch loads from the same platform?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Airbyte | 3 |
| Fivetran | 5 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Airbyte | 1 |
| Fivetran | 4 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 1 |
| Rivery | 5 |


- Prompt text: What data pipeline tools integrate natively with major cloud data warehouses for automatic schema management and optimized load performance?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Airbyte | 1 |
| Fivetran | 4 |
| Integrate.io | 7 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Airbyte | 2 |
| Fivetran | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 2 |
| Matillion | 3 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Matillion | 4 |
| Integrate.io | 7 |
| Rivery | 39 |


- Prompt text: What are the easiest ELT tools to get data flowing from a SaaS CRM into a cloud data warehouse in under a day with no custom code?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 3 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Hevo Data | 2 |
| Matillion | 3 |
| Airbyte | 4 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 2 |
| Fivetran | 24 |
| Hevo Data | 28 |
| Rivery | 38 |


- Prompt text: What ETL platforms do analytics engineers prefer when they want SQL-based transformations with testing and documentation built in?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| dbt | 1 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| dbt | 2 |



##### Google-ai-mode





##### Chatgpt-search





##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 1 |
| dbt | 3 |
| Matillion | 4 |


- Prompt text: What data pipeline tools handle late-arriving data and backfilling years of historical records reliably without manual intervention?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Dagster | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search





##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 1 |
| dbt | 48 |
| Dagster | 61 |


- Prompt text: What data orchestration tools have the best getting-started experience for a data engineer moving from manually scheduled SQL scripts?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| dbt | 1 |
| Dagster | 4 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Dagster | 4 |



##### Xai-search

| Display name | Position |
| --- | --- |
| dbt | 9 |
| Dagster | 11 |
| Rivery | 12 |
| Airbyte | 39 |


- Prompt text: Which data pipeline tools have the best observability and data lineage views so you can trace where a bad value came from?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Dagster | 2 |
| dbt | 3 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Dagster | 6 |
| dbt | 9 |
| Integrate.io | 20 |


- Prompt text: Looking for a data orchestration platform with a great local development workflow — which tools let you test DAGs or workflows locally before deploying?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: 6



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Dagster | 3 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Dagster | 2 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Dagster | 1 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Astronomer | 6 |
| Dagster | 13 |
| dbt | 14 |


- Prompt text: Which ELT platforms maintain low-latency incremental syncs so dashboards reflect source data within minutes rather than hours?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 3 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Integrate.io | 2 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 2 |
| Hevo Data | 4 |
| Rivery | 5 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Fivetran | 2 |
| Integrate.io | 5 |
| Matillion | 7 |
| dbt | 43 |
| Airbyte | 48 |
| Hevo Data | 57 |


- Prompt text: Looking for an orchestration platform that integrates with my existing transformation layer — which tools support running SQL models as pipeline steps?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: 24



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Dagster | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Matillion | 2 |
| Fivetran | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Dagster | 1 |



##### Xai-search

| Display name | Position |
| --- | --- |
| dbt | 3 |
| Dagster | 6 |
| Astronomer | 24 |


- Prompt text: What data engineering platforms work well in a multi-cloud setup where sources span one cloud and the warehouse is on another?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 4 |
| Matillion | 8 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Airbyte | 7 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Fivetran | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| dbt | 2 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Integrate.io | 2 |
| Airbyte | 22 |
| Fivetran | 32 |


- Prompt text: Which ELT platforms have the largest library of pre-built source connectors covering SaaS apps, databases, and event streams?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Xai-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 2 |
| Rivery | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Airbyte | 2 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Integrate.io | 4 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fivetran | 1 |
| Airbyte | 2 |
| Meltano | 3 |
| Hevo Data | 5 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Fivetran | 2 |
| Airbyte | 3 |
| Integrate.io | 32 |
| Matillion | 36 |





## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://www.astronomer.io/docs/learn/testing-airflow | Test Airflow DAGs | astronomer.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/379d7b0e-f19b-499b-90c3-90f3aade44ab/e7b824bc458592317f44b52deadd29068d827105.png | commercial | documentation | 1 | 1 |
| https://www.astronomer.io/docs/learn/data-quality | Data quality and Airflow \| Astronomer Documentation | astronomer.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/379d7b0e-f19b-499b-90c3-90f3aade44ab/e7b824bc458592317f44b52deadd29068d827105.png | commercial | docs | 1 | 0 |
| https://llms.astronomer.io/airflow-vs-dagster-vs-prefect-for-dbt-analytics-engineering | Apache Airflow vs Dagster vs Prefect for dbt and analytics ... | llms.astronomer.io | Not available | commercial | other | 1 | 0 |
| https://www.astronomer.io/astro-vs-other-managed-airflow-services/ | Astro vs Other Managed Apache Airflow® Services | astronomer.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/379d7b0e-f19b-499b-90c3-90f3aade44ab/e7b824bc458592317f44b52deadd29068d827105.png | commercial | home | 1 | 0 |
| https://www.astronomer.io/legal/sla/ | Service Level Addendum | astronomer.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/379d7b0e-f19b-499b-90c3-90f3aade44ab/e7b824bc458592317f44b52deadd29068d827105.png | commercial | home | 1 | 0 |
| https://www.astronomer.io/blog/expert-tips-for-monitoring-the-health-and-slas-of-your-apache-airflow-dags/ | Airflow Monitoring: Mastering SLAs, DAGs, & Observability | astronomer.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/379d7b0e-f19b-499b-90c3-90f3aade44ab/e7b824bc458592317f44b52deadd29068d827105.png | commercial | blog | 1 | 0 |
| https://www.astronomer.io/docs/astro/observe-slas | Service Level Agreements (SLAs) | astronomer.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/379d7b0e-f19b-499b-90c3-90f3aade44ab/e7b824bc458592317f44b52deadd29068d827105.png | commercial | docs | 1 | 0 |
| https://www.astronomer.io/docs/learn/2.x/using-slas | Leverage SLAs for enhanced data quality monitoring | astronomer.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/379d7b0e-f19b-499b-90c3-90f3aade44ab/e7b824bc458592317f44b52deadd29068d827105.png | commercial | docs | 1 | 0 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Looking for a data orchestration platform with a great local development workflow — which tools let you test DAGs or workflows locally before deploying? | google-ai | ...While raw Airflow can be notoriously painful to run locally, the ecosystem has evolved to fix this—primarily through Astronomer's Astro CLI . * Why it's great for local testing: Using the Astro CLI, you can spin up a fully containerized... |
| What data engineering platforms work well in a multi-cloud setup where sources span one cloud and the warehouse is on another? | google-ai | Managed Airflow (Astronomer): Provides flexible, containerized orchestration to schedule and monitor cross-cloud data movement tasks securely. |
| Which ETL tools have an open API and SDK so we can build custom connectors for internal data sources quickly? | google-ai-mode | Apache Airflow : Offers robust support through the `astronomer-cosmos` library or the official dbt provider hooks/operators. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| perplexity | Airbyte | The strongest options are: \| Tool \| Best fit \| Custom-connector approach \| \|---\|---\|---\| \| Airbyte \| A managed or self-hosted platform, especially for HTTP APIs \| Start with Connector Builder for common REST/GraphQL sources; use its low-code YAM... |
| perplexity | Meltano | [1][2] \| \| Meltano / Singer SDK \| Open-source, code-first pipelines \| Build custom Singer taps (extractors) and targets (loaders). |
| bing-copilot-search | Airbyte | The most notable options are Airbyte, Fivetran, and Microsoft Power Platform. 🔑 Key ETL Tools with Open API/SDK for Custom Connectors -------------------------------------------------------- \| Tool \| SDK/API Availability \| Language/Framework \| Depl... |
| bing-copilot-search | Fivetran | The most notable options are Airbyte, Fivetran, and Microsoft Power Platform. 🔑 Key ETL Tools with Open API/SDK for Custom Connectors -------------------------------------------------------- \| Tool \| SDK/API Availability \| Language/Framework \| Depl... |
| google-ai | Fivetran | Fivetran * SDK & Language: Features the Fivetran Connector SDK , which uses lightweight Python scripts. |
| google-ai | Integrate.io | Integrate.io * Ecosystem Support: Python is the dominant language across these SDKs, enabling your team to easily reuse existing internal scripts and libraries. |
| google-ai-mode | dbt | Dagster : Uses the `dagster_dbt` library to parse your dbt project manifest. |
| chatgpt-search | Airbyte | For quickly building custom connectors to internal sources, the strongest options are: * Airbyte — fastest for REST/HTTP APIs: Connector Builder is no-code/low-code; Python CDK handles complex sources. |
| chatgpt-search | Meltano | \[1\] * Meltano + Singer SDK — excellent if you want an open, code-first SDK. |
| perplexity | Airbyte | ...after loading some data, I’d shortlist: \| Platform \| Why it’s good for this failure mode \| Best fit \| \|---\|---\|---\| \| Airbyte \| Makes partial outcomes explicit: a run can be marked *Incomplete* or *Failed* even when a subset of data reached the d... |
| google-ai | Matillion | Matillion * Partial Load Handling: Because it natively understands partitioning and data assets, you can easily inspect the exact subset of data that made it through, fix the root cause in code, and resume or backfill _only_ the fail... |
| chatgpt-search | Dagster | ...lure debugging \| Partial-load recovery \| Lineage / impact analysis \| Best fit \| \| --- \| --- \| --- \| --- \| --- \| \| Dagster \| Excellent \| Excellent \| Excellent \| Engineering-heavy ELT \| \| Databricks / Lakeflow \| Excellent*... |



## Trend

Visibility delta: -0.8
Avg position delta: Not available
Citation count delta: -1