Mastering Search & Observability

This book's got a bunch of handy recipes for data science pros to get them through the most common challenges they face when using Python tools and libraries. Each recipe shows you exactly how to do something step-by-step. You can load CSVs directly from a URL, flatten nested JSON, query SQL and NoSQL databases, import Excel sheets, or stream large files in memory-safe batches.

Once the data's loaded, you'll find simple ways to spot and fill in missing values, standardize categories that are off, clip outliers, normalize features, get rid of duplicates, and extract the year, month, or weekday from timestamps. You'll learn how to run quick analyses, like generating descriptive statistics, plotting histograms and correlation heatmaps, building pivot tables, creating scatter-matrix plots, and drawing time-series line charts to spot trends. You'll learn how to build polynomial features, compare MinMax, Standard, and Robust scaling, smooth data with rolling averages, apply PCA to reduce dimensions, and encode high-cardinality fields with sparse one-hot encoding using feature engineering recipes.

As for machine learning, you'll learn to put together end-to-end pipelines that handle imputation, scaling, feature selection, and modeling in one object, create custom transformers, automate hyperparameter searches with GridSearchCV, save and load your pipelines, and let SelectKBest pick the top features automatically. You'll learn how to test hypotheses with t-tests and chi-square tests, build linear and Ridge regressions, work with decision trees and random forests, segment countries using clustering, and evaluate models using MSE, classification reports, and ROC curves. And you'll finally get a handle on debugging and integration: fixing pandas merge errors, correcting NumPy broadcasting mismatches, and making sure your plots are consistent.

Key Learnings

• You can load remote CSVs directly into pandas using read_csv, so you don't have to deal with manual downloads and file clutter.
• Use json_normalize to convert nested JSON responses into simple tables, making it a breeze to analyze.
• You can query relational and NoSQL databases directly from Python, and the results will merge seamlessly into Pandas.
• Find and fill in missing values using IGNSA(), forward-fill, and median strategies for all of your data over time.
• You can free up a lot of memory by turning string columns into Pandas' Categorical dtype.
• You can speed up computations with NumPy vectorization and chunked CSV reading to prevent RAM exhaustion.
• You can build feature pipelines using custom transformers, scaling, and automated hyperparameter tuning with GridSearchCV.
• Use regression, tree-based, and clustering algorithms to show linear, nonlinear, and group-specific vaccination patterns.
• Evaluate models using MSE, R², precision, recall, and ROC curves to assess their performance.
• Set up automated data retrieval with scheduled API pulls, cloud storage, Kafka streams, and GraphQL queries.

Table of Content

1. Data Ingestion from Multiple Sources
2. Preprocessing and Cleaning Complex Datasets
3. Performing Quick Exploratory Analysis
4. Optimizing Data Structures and Performance
5. Feature Engineering and Transformation
6. Building Machine Learning Pipelines
7. Implementing Statistical and Machine Learning Techniques
8. Debugging and Troubleshooting
9. Advanced Data Retrieval and Integration

A hands-on, recipe-driven book that puts OpenTelemetry into immediate use. This cookbook is for IT folks like developers, Linux admins, cloud engineers, backend pros, networking experts, and security practitioners. It's for anyone who wants a proven, hands-on way to keep an eye on, trace, and understand modern systems.

This book gives you step-by-step easy solutions to everyday observability challenges, so you can integrate, configure, and operate OpenTelemetry in dynamic environments. Each chapter focuses on solving problems that are directly relevant to production teams. These problems include installing and bootstrapping the Collector on Linux, wiring telemetry pipelines for traces, metrics, logs, and baggage, and integrating with the platforms that organizations trust for analysis and alerting.

• Get the OpenTelemetry Collector up and running on Linux, Docker, and Kubernetes.
• Make and adjust pipelines to collect, process, and send out different telemetry signals.
• Use it to develop apps across Python, Go, Java, and Node.js.
• Boost signals with semantic info and resource detectors for more context.
• Integrate with AWS CloudWatch, X-Ray, Elastic, Prometheus, Splunk, Datadog, New Relic, Grafana Tempo, Loki, and Jaeger.
• Make a visual plan for telemetry and set up real-time alerts.

There's no need to get lost in theoretical jargon because OpenTelemetry Cookbook gets right to the meat and potatoes of implementation. Every recipe gives you a clear problem statement, a step-by-step solution, and practical validation. If you're just starting out with observability or want to level up your skills, this book's got you covered with clear steps to understand distributed, cloud-native, and hybrid systems.

This book builds a solid foundation for strong, easy-to-spot infrastructure and application settings, one step at a time. This book isn't about offering quick fixes or magic solutions. It gives you a full set of tools and techniques that help professionals improve visibility, performance, and reliability in their own technical landscapes.

1. Bootstrapping OpenTelemetry
2. Building Telemetry Pipelines
3. Instrumenting Applications with OpenTelemetry SDKs
4. Code-Based Instrumentation and Auto-Instrumentation
5. Telemetry with Attributes and Resource Detectors
6. Advanced Signal Processing and Filtering
7. Observability in Kubernetes
8. Integrations with Cloud and Observability Platforms
9. Visualizing and Alerting Telemetry Data

Well, that's the promise of ES|QL. It's the modern piped query language that's at the heart of this book. To get started, you just need to pick a source, chain each step with a pipe, and read your query from top to bottom like a sentence. The amount of JSON needed for this kind of work is much less than it used to be. This book is for data analysts, security professionals, and developers who work with large datasets, and it takes you from your very first query to production-ready integration. You'll be shaping and summarising data, cleaning messy logs, joining across indices, ranking results by relevance, and building semantic and hybrid AI-powered search.

Plus, you'll be visualising your findings in Kibana dashboards and running ES|QL directly from Python, JavaScript, and automation.This book is all about getting you hands-on with real-life examples in an online retail setting, so you can put each concept into practice and get writing ES|QL straight away.

• Write piped ES|QL queries that can filter, transform, and aggregate in one readable flow.
• Retrieve rows and columns with precise filtering and shaping.
• Compute new columns using math, string, date, and conditional functions inside queries.
• Summarise millions of rows into totals, averages, and trends using STATS.
• Clean multi-value fields and parse unstructured logs into typed, query-ready columns.
• Correlate data across indices using ENRICH policies and LOOKUP JOIN.
• Rank results with full-text MATCH and relevance scoring.
• Build semantic, hybrid, and AI-assisted search with vectors and inference.
• Turn queries into Kibana charts and interactive dashboards.
• Run ES|QL from Python, JavaScript, with further automation too.

1. Running ES|QL Queries
2. Retrieve Desired Data
3. Compute and Transform Values
4. Summarize Data with Aggregations
5. Clean and Reshape Messy Data
6. Correlate Data across Indices
7. Search by Relevance
8. Build AI-Powered Search
9. Visualize and Build Dashboards
10. Integrate ES|QL into Applications