The shift from simple cron jobs to orchestrated AI-powered workflows is reshaping how startups scale. For a small team, these transitions come with unique challenges and big opportunities.

In this episode, Naseem Shah, Head of Engineering at Xena Intelligence, shares how he built data pipelines from scratch, adopted Apache Airflow and transformed Amazon review analysis with LLMs.

Key Takeaways:

00:00 Introduction.

03:28 The importance of building initial products that support growth and investment.

06:16 The process of adopting new tools to improve reliability and efficiency.

09:29 Approaches to learning complex technologies through practice and fundamentals.

13:57 Trade-offs small teams face when balancing performance and costs.

18:40 Using AI-driven approaches to generate insights from large datasets.

22:38 How unstructured data can be transformed into actionable information.

25:55 Moving from manual tasks to fully automated workflows.

28:05 Orchestration as a foundation for scaling advanced use cases.

Resources Mentioned:

Naseem Shah

https://www.linkedin.com/in/naseemshah/

Xena Intelligence | LinkedIn

https://www.linkedin.com/company/xena-intelligence/

Xena Intelligence | Website

https://xenaintelligence.com/

Apache Airflow

https://airflow.apache.org/

Google Cloud Composer

https://cloud.google.com/composer

Techstars

https://www.techstars.com/

Docker

https://www.docker.com/

AWS SQS

https://aws.amazon.com/sqs/

PostgreSQL

https://www.postgresql.org/

Thanks for listening to “The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.

#AI #Automation #Airflow #MachineLearning