Data Engineering Infrastructure Platforms | Parse

Data Engineering Infrastructure Platforms in AI answers

Data as of Oct 16, 2026Based on 91 AI responsesReviewed by Dimitry Apollonsky

AI summary

Temporal and Camunda are nearly tied, with Temporal favored for code-first durable execution and Camunda for visual BPMN orchestration. For learners seeking structured data engineering coursework over execution engines, IBM is the usual answer.

Most-mentioned brands

Rank and Mentioned in · since Jul 7

#	Brand	Recommended for	Mentioned in
1	Temporal 47% code-first durable workflows and distributed applications requiring high reliability	code-first durable workflows and distributed applications requiring high reliability	47%
2	Camunda 46% enterprise orchestration requiring BPMN-compliant visual process modeling	enterprise orchestration requiring BPMN-compliant visual process modeling	46%
3	IBM 35% comprehensive professional certificates covering ETL, big data tools, and pipelines	comprehensive professional certificates covering ETL, big data tools, and pipelines	35%
4	AWSAmazon product line 33% cloud infrastructure hosting managed orchestration and data processing services	cloud infrastructure hosting managed orchestration and data processing services	33%
5	AWS Step FunctionsAmazon product line 29% serverless state machine workflows deeply integrated within the AWS ecosystem	serverless state machine workflows deeply integrated within the AWS ecosystem	29%
6	Apache AirflowApache Software Foundation product line 29% orchestrating scheduled batch ETL tasks and heavy data pipelines	orchestrating scheduled batch ETL tasks and heavy data pipelines	29%
7	DataCamp 27% interactive online learning programs focused on data engineering tools	interactive online learning programs focused on data engineering tools	27%
8	Inngest 24% event-driven workflow execution without requiring dedicated infrastructure management	event-driven workflow execution without requiring dedicated infrastructure management	24%
9	Udacity 20% online nanodegree programs focused on practical data engineering skills	online nanodegree programs focused on practical data engineering skills	20%
10	DataTalks.Club 20% community-driven courses covering open-source data engineering technologies	community-driven courses covering open-source data engineering technologies	20%
11	Coursera 16%		16%
12	Conductor OSS 16%		16%
13	Restate 13%		13%
14	Google CloudAlphabet product line 12%		12%
15	Orkes 10%		10%
16	Databricks AcademyDatabricks product line 10%		10%
17	Microsoft AzureMicrosoft product line 10%		10%
18	Dagster 10%		10%
19	Prefect 10%		10%
20	Apache SparkApache Software Foundation product line 9%		9%
21	Trigger.dev 9%		9%
22	Databricks 9%		9%
23	Camunda ZeebeCamunda product line 8%		8%
24	Udemy 8%		8%
25	Argo Workflows 8%		8%
26	Celery 8%		8%
27	Snowflake 8%		8%
28	DeepLearning.AI 7%		7%
29	Apache KafkaApache Software Foundation product line 7%		7%
30	AWS Certified Data Engineer – Associate 7%		7%
31	Google Cloud PlatformAlphabet product line 5%		5%
32	Dataquest 5%		5%
33	Google Cloud Data Engineering Professional Certificate 5%		5%
34	Databricks Data Intelligence PlatformDatabricks product line 5%		5%
35	BullMQ 5%		5%
36	Sidekiq 5%		5%
37	DataExpert 4%		4%
38	Google Cloud Skills BoostAlphabet product line 4%		4%
39	Data Engineer Camp 4%		4%
40	Databricks Certified Data Engineer Associate 4%		4%
41	dbt 4%		4%
42	Amazon RedshiftAmazon product line 4%		4%
43	Google Cloud BigQueryAlphabet product line 4%		4%
44	Delta Lake 4%		4%
45	MIT xPROMassachusetts Institute of Technology product line 3%		3%
46	Snowflake Data Engineering Professional Certificate 3%		3%
47	Edx 3%		3%
48	AWS GlueAmazon product line 3%		3%
49	Data Engineering Professional CertificateCoursera product line 2%		2%
50	Udacity Data Engineer NanodegreeUdacity product line 2%		2%
51	Data Engineer Academy 2%		2%
52	DataEngineer.io 2%		2%
53	Duke University 2%		2%
54	Cadence Workflow 2%		2%
55	Dapr 2%		2%
56	Microsoft Big Data Management and AnalyticsCoursera product line 2%		2%
57	Azure Durable Task 2%		2%
58	Argo Project 2%		2%
59	Apache FlinkApache Software Foundation product line 2%		2%
60	Databricks Delta Live TablesDatabricks product line 2%		2%
61	Apache HadoopApache Software Foundation product line 2%		2%
62	Hangfire 2%		2%
63	Snowflake AISnowflake product line 2%		2%
64	AWS Skill Builder 2%		2%
65	Open Source Data Engineering with Spark, dbt & AirflowCoursera product line 1%		1%
66	NYC Data Science Academy 1%		1%
67	Springboard Data Engineering Career TrackSpringboard product line 1%		1%
68	Building Smarter Data Pipelines: SQL, Spark, Kafka & GenAI SpecializationCoursera product line 1%		1%
69	Data Engineer in PythonDataCamp product line 1%		1%
70	Terraform 1%		1%
71	TechVidvan 1%		1%
72	Microsoft LearnMicrosoft product line 1%		1%
73	Astronomer AcademyAstronomer product line 1%		1%
74	Amazon S3Amazon product line 1%		1%
75	Springboard 1%		1%
76	n8n 1%		1%
77	Hands-On Data EngineeringUCSC Extension product line 1%		1%
78	Flyte 1%		1%
79	Data Engineering BootcampTakeo product line 1%		1%
80	DataCamp Data Engineering Career TrackDataCamp product line 1%		1%
81	University of San Diego Professional & Continuing EducationUniversity of San Diego product line 1%		1%
82	Data Engineering on AWSCoursera product line 1%		1%
83	Massachusetts Institute of Technology 1%		1%
84	AWS LambdaAmazon product line 1%		1%
85	Penn State World CampusPenn State product line 1%		1%
86	Kestra 1%		1%
87	dbt Learndbt product line 1%		1%
88	University of Washington Professional & Continuing EducationUniversity of Washington product line 1%		1%
89	Takeo 1%		1%
90	Zapier 1%		1%
91	Upstash 1%		1%
92	AWS Lambda Durable FunctionsAmazon product line 1%		1%
93	Snowflake SnowPro Core CertificationSnowflake product line 1%		1%
94	Mongodb 1%		1%
95	Cloud Data Engineer Professional CertificateGoogle Cloud Data Engineering Professional Certificate product line 1%		1%
96	Stephane Maarek 1%		1%
97	Azure Synapse AnalyticsMicrosoft product line 1%		1%
98	Apache IcebergApache Software Foundation product line 1%		1%
99	Mage 1%		1%
100	Redis 1%		1%

Recommendations by AI engine

ChatGPT SearchGoogle AI Mode

Widest split: Conductor OSS, 6% on ChatGPT Search and 27% on Google AI Mode.

Mentioned in on each AI engine, by brand
Brand	ChatGPT Search	Google AI Mode
Temporal	47%	48%
Camunda	47%	45%
IBM	45%	25%
AWS	26%	41%
AWS Step Functions	30%	27%
Apache Airflow	32%	25%
DataCamp	34%	20%
Inngest	28%	20%
Udacity	32%	Outside Google AI Mode's top 25
DataTalks.Club	13%	27%

Best workflow engine for long-running processes

My default recommendation is Temporal for long-running, code-centric application workflows that must survive crashes, retries, deployments, and waits lasting hours, days, or years. Temporal persists workflow history and reconstructs execution state after failures.

However, if your processes are primarily business workflows with visual modeling, human approvals, and compliance requirements, Camunda may be a better fit.

The shortlist

1. Temporal

(Best default for developers)

Durable execution for distributed applications, long waits, retries, timers, signals, and recovery. Define workflows in application code using supported language SDKs.

Best for: Payments, order lifecycles, distributed transactions, backend jobs, and stateful AI agents.

Trade-off: Requires learning deterministic workflow programming and operating or paying for the workflow service.

Official documentation ·

2. Camunda 8

Best for business processes and human tasks

BPMN-based workflows make processes visible to developers, operations teams, and business stakeholders. Supports timers, event-driven continuation, service tasks, and human approvals.

Best for: Loan origination, insurance claims, customer onboarding, and regulated approval flows.

Trade-off: BPMN modeling and process-version management add complexity for code-heavy workflows.

Official documentation ·

3. AWS Step Functions

Best for AWS-native orchestration

Managed state-machine orchestration with integrations across AWS services, reducing the amount of workflow infrastructure you need to run.

Best for: Lambda pipelines, cloud automation, and service-to-service workflows within AWS.

Trade-off: AWS coupling and state-machine modeling may be less natural than code-first workflows for complex application logic.

Official documentation

4. Azure Durable Functions

Best for Azure serverless applications

Provides durable orchestration patterns for function-based applications, including timers, external events, and human-interaction workflows.

Best for: Teams already invested in Azure Functions and the Microsoft ecosystem.

Trade-off: Its programming model and hosting choices are more closely tied to Azure Functions.

Official documentation

Quick comparison

Requirement	Best fit
Long-running workflows defined in code	Temporal
Human approvals and BPMN diagrams	Camunda
AWS service orchestration	AWS Step Functions
Azure Functions orchestration	Durable Functions
Durable AI agents and tool calls

These are architectural recommendations, not a universal performance ranking. The best choice depends on your workflow semantics, deployment environment, and operating requirements.

What I would evaluate before choosing

For genuinely long-running processes, check five things:

Durability: Can an execution survive worker crashes, service restarts, and infrastructure failures?
External events: Can it wait days or months for a webhook, message, or human response without keeping a process alive?
Retries and consistency: How are duplicate side effects, timeouts, compensation, and idempotency handled?
Versioning: Can you safely deploy new workflow code while old executions are still running?
Operations: Can you inspect, replay or recover failed executions, and manage workflow history and storage at scale?

Temporal is particularly compelling when the workflow is part of your application's business logic. Camunda is compelling when the process itself must be modeled, governed, and understood by people beyond the engineering team.

One question to narrow it down

What kind of long-running process are you building?

1. Workflow type

Backend business logic (payments, orders, distributed jobs)

Business processes with approvals and human tasks

AI agents or multi-step LLM workflows

Data pipelines and batch processing

2. Deployment environment

Cloud-agnostic or self-hosted

AWS

Azure

Google Cloud

3. Typical workflow duration

Seconds to minutes

Hours to days

Weeks to months or longer

[Recommend an engine for my use case]