Modern data engineers and Pythonistas are spoiled for choice when it comes to querying and analyzing large volumes of data. With the recent introduction of ES|QL (Elasticsearch Query Language) and deeper support for Apache Arrow, Elasticsearch has taken a major step toward blending the world of search, analytics, and fast, tabular query execution.

In this talk, we’ll dive into how ES|QL transforms the developer and SRE experience when working with structured logs and observability data of any signal type in Elasticsearch. We'll explore how it borrows the familiarity of SQL and the expressiveness of pipelines, while leveraging Apache Arrow under the hood for speed and interoperability. But more importantly - we’ll go hands-on and bridge that with the Python ecosystem using Pandas DataFrames.

This talk demonstrates, through practical examples, how to:

• Use ES|QL to write rich, composable queries against your Elasticsearch data
• Harness the power of Apache Arrow to serialize and stream query results
• Easily work with these results in Pandas, turning search into a Python-native analysis workflow

Whether you're an SRE, DevOps engineer, Data Scientist or a data-curious developer, you’ll leave with practical techniques to bring Elasticsearch insights directly into your Jupyter notebooks, alert tuning pipelines, and incident port-mortem analysis.