JSON
JSON is a standardized, language-independent text format for representing structured data using objects, arrays, strings, numbers, Boolean values, and null.
Grokking AI Applications
JSON is a text-based data-interchange format that represents structured values using objects, arrays, and primitive values.
- How it is used
- Packages selected customer fields as labeled LLM input for personalized email generation.
- How deep it goes
- Defines JSON objects, braces, brackets, and key-value pairs, then demonstrates creating one JSON field in KNIME.
- Examples
- Uses a Name-and-Age record and selected customer-profile columns for personalized emails.
- What you can do after
- Operate/configure it by selecting customer columns and combining them into a JSON object in KNIME.
A Pythonic Adventure: From Python basics to a working web app
JSON is a text-based data-interchange format that represents structured data using objects, arrays, and primitive values.
- How it is used
- Stores structured coffee-shop orders in files and supports exchanging information between programs.
- How deep it goes
- Defines structured data and demonstrates Python file writing, reading, indentation, and multiple-order lists.
- Examples
- Uses coffee orders containing name, drink, flavor, and topping, saved individually or as a list.
- What you can do after
- Operate Python's json module to write dictionaries or lists to JSON files and load saved data.
Read how A Pythonic Adventure: From Python basics to a working web app explains JSON →
Terraform in Depth: Infrastructure as Code with Terraform and OpenTofu
JSON is a text-based data interchange format that represents structured values and objects.
- How it is used
- Supports generated Terraform configuration and variable values, machine-readable command results, and inspection of local state.
- How deep it goes
- Explains configuration object structure, file loading, output shapes, state fields, and comment handling; directs wrapper authors to current field documentation.
- Examples
- Shows a JSON variable file, a provider alias represented as an array, and a local JSON state file.
- What you can do after
- Build it from scratch as generated .tf.json configuration, or parse Terraform CLI results as single objects or streamed JSONL records.
Read how Terraform in Depth: Infrastructure as Code with Terraform and OpenTofu explains JSON →
Coding with AI
JSON is a text-based standard format for representing structured data with objects, arrays, and primitive values.
- How it is used
- Supports persistent high-score storage and specifies structured AI output for APIs or Python data-processing tasks.
- How deep it goes
- Demonstrates a high-score persistence workflow and JSON output constraints without explaining JSON parsing internals.
- Examples
- Uses high_scores.json for a top-five table and process_json_data for filtering inactive entries.
- What you can do after
- Apply it via Cursor to persist high scores and request JSON-formatted output under explicit constraints.
Building Web APIs with ASP.NET Core
JSON is a text-based data interchange format that represents structured values using objects, arrays, and primitive values.
- How it is used
- Supports client data exchange through DTOs in a board-game CRUD API and serves as a REST data-format choice.
- How deep it goes
- Identifies JSON and its practical role, but stops short of payload syntax, serialization behavior, or configuration steps.
- Examples
- Uses a board-game API’s planned DTO-based client exchanges as context, without showing a JSON payload.
- What you can do after
- Evaluate/understand JSON as a REST format and as data exchanged through DTOs; implementation details remain unspecified.
Read how Building Web APIs with ASP.NET Core explains JSON →
Learn Go with Pocket-Sized Projects
JSON is a text-based data interchange format that represents structured values such as objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON as input for a Go CLI that reads book collections and finds books shared between readers.
- How deep it goes
- Defines values, ordering, tags, and decoding, but defers detailed parsing until HTTP response handling.
- Examples
- Demonstrates nested bookworm collections and an error response represented as {"error":"game over"}.
- What you can do after
- Build JSON file loaders with encoding/json, tagged Go structs, json.Decoder, and explicit error handling.
Read how Learn Go with Pocket-Sized Projects explains JSON →
Just Use Postgres!
A JSON document is structured data represented using the JavaScript Object Notation format.
- How it is used
- Stores and queries flexible application payloads while supporting hybrid relational models in PostgreSQL.
- How deep it goes
- Explains storage choices, querying, updates, removal, and indexing, but defers detailed operator and index implementations.
- Examples
- Uses pizza orders, nested toppings arrays, configuration settings, metadata, preferences, telemetry events, and message payloads.
- What you can do after
- Evaluate/understand when to choose text, json, jsonb, or hybrid relational storage for application data.
Build a Website with ChatGPT
JSON is a text-based data interchange format that represents structured data using objects, arrays, property-value pairs, strings, numbers, and other supported values.
- How it is used
- Supplies spreadsheet-derived records to an interactive course catalog for display, searching, filtering, and sorting.
- How deep it goes
- Defines object and array syntax, then demonstrates Excel conversion and JavaScript loading without requiring direct JSON editing.
- Examples
- Uses course records, an Excel range, conversion prompts, and a fetched college_courses.json file.
- What you can do after
- Apply it via ChatGPT to convert Excel data into a downloadable JSON file and populate a web page with JavaScript.
.NET in Action, Second Edition
JSON is a text-based data-interchange format that represents structured values such as objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON to move structured data through file-processing applications and HTTP API responses.
- How deep it goes
- Works through stream-based DOM reading, valid output, and typed serialization, without developing a full JSON grammar.
- Examples
- Builds a category-filtered joke file and a comic-metadata search using JSON from an HTTP API.
- What you can do after
- Build it from scratch: read, filter, and write JSON in a C# console application using System.Text.Json.
Acing the CCNA Exam: Volume 2 Advanced Networking and Security
JSON is a text-based data-interchange format that represents structured data using objects, arrays, values, and name-value pairs.
- How it is used
- Supports communication, storage, and transmission of structured data between applications and REST APIs.
- How deep it goes
- Explains JSON types, objects, arrays, nesting, interpretation, and syntax errors, stopping short of full RFC details.
- Examples
- Uses interface, router, switch, firewall, access-token, and Catalyst Center inventory JSON examples.
- What you can do after
- Evaluate simple JSON data, recognize its components, and diagnose common syntax errors.
Read how Acing the CCNA Exam: Volume 2 Advanced Networking and Security explains JSON →
Effective Data Analysis
JSON is a text-based data-interchange format that represents structured values using objects, arrays, and primitive values.
- How it is used
- Supports retrieving external API data, inspecting its structure, and preparing selected fields for analysis.
- How deep it goes
- Explains human-readable structure, nested objects, key inspection, and tabular conversion without treating JSON syntax comprehensively.
- Examples
- Uses a New York City weather forecast with nested periods, selected weather fields, cleaned wind speeds, and daily aggregation.
- What you can do after
- Apply JSON through Python requests and pandas to inspect API responses and convert nested forecast records into analyzable tables.
Build Python Web Apps with Streamlit
JSON is a text-based data-interchange format representing structured data as objects, arrays, values, and name-value pairs.
- How it is used
- Uses JSON to exchange API response data and connect external services to application features.
- How deep it goes
- Explains JSON hierarchy, parsing, list and dictionary access, and failure handling without detailing the format's complete specification.
- Examples
- Demonstrates weather data and a joke API returning JSON arrays and objects.
- What you can do after
- Apply it via Python's requests library to parse API responses and extract values for display in an application.
Read how Build Python Web Apps with Streamlit explains JSON →
Python How-To
JSON is a lightweight text-based data interchange format that represents structured values using objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON to exchange task data with external systems through APIs.
- How deep it goes
- Explains JSON syntax, Python type mapping, deserialization, serialization, and custom-class encoding through worked examples.
- Examples
- Uses task API responses, parsed lists of dictionaries, custom task instances, and formatted JSON output.
- What you can do after
- Apply it via Python’s json module to parse API responses, serialize Python data, and supply encoding behavior for custom instances.
Python Workout: 50 ten-minute exercises
A JSON file is a text file whose contents are serialized according to the JSON format.
- How it is used
- Uses JSON to exchange data and preserve Python data structures in files or across programs.
- How deep it goes
- Explains parsing, serialization, file loading, and directory-wide processing, while stopping short of JSON grammar internals.
- Examples
- Works through class-score arrays, directory-wide statistics, filesystem metadata, and CSV-style passwd conversion.
- What you can do after
- Build JSON-processing scripts that load, transform, analyze, and write structured data using Python's standard library.
Read how Python Workout: 50 ten-minute exercises explains JSON →
Functional Programming in Scala, Second Edition
JSON is a text-based data interchange format for representing structured values such as objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON as a concrete target for practicing parser combinators and syntax-tree construction.
- How deep it goes
- Defines JSON values and works through a full parser, stopping at syntax-tree construction rather than further document processing.
- Examples
- Uses a Microsoft market-data document and implementation exercises for arrays, objects, literals, separators, whitespace, and end-of-input.
- What you can do after
- Builds a JSON parser from scratch with parser primitives, combinators, recursive grammar rules, and a typed JSON representation.
Read how Functional Programming in Scala, Second Edition explains JSON →
The Quick Python Book, Fourth Edition
JSON is a text-based data interchange format that represents structured values using objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Carries structured API responses and provides the object-like model used to describe MongoDB documents.
- How deep it goes
- Shows raw and parsed API results, then distinguishes JSON-shaped documents from BSON; does not develop a JSON grammar.
- Examples
- Uses a weather response with nested measurements and a crime-data response parsed into a list of records.
- What you can do after
- Apply it via requests to read API responses as Python data and recognize when MongoDB uses BSON instead.
Read how The Quick Python Book, Fourth Edition explains JSON →
100 SQL Server Mistakes and How to Avoid Them
JSON is a lightweight text format for representing structured and semi-structured data using objects, arrays, and name-value pairs.
- How it is used
- Uses JSON to simplify schemas for irregular, infrequently queried data such as international employee addresses.
- How deep it goes
- Explains semistructured modeling, read-write trade-offs, generation, extraction, and shredding without detailing JSON internals.
- Examples
- Uses international employee addresses, an NVARCHAR(MAX) column, nested JSON generation, JSON_VALUE extraction, and OPENJSON shredding.
- What you can do after
- Apply it via SQL Server's FOR JSON, JSON_VALUE, and OPENJSON operations when designing schemas for irregular data.
Read how 100 SQL Server Mistakes and How to Avoid Them explains JSON →
Python Workout, Second Edition
JSON is a text-based data-interchange format that represents structured values such as objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON to exchange data and analyze structured records stored in files.
- How deep it goes
- Explains parsing, serialization, file traversal, nested dictionaries, and a statistics exercise, but not JSON internals.
- Examples
- Uses class-score arrays, nested score dictionaries, CSV-to-JSON conversion, and directory metadata stored as JSON.
- What you can do after
- Build Python programs that read, transform, analyze, and write JSON-formatted data.
Knowledge Graphs and LLMs in Action
JSON is a text-based data-interchange format that represents structured values using objects, arrays, and primitive data types.
- How it is used
- Formats model-generated Cypher responses for predictable downstream processing.
- How deep it goes
- Defines fields, value types, and response constraints, without detailing JSON parsing or serialization internals.
- Examples
- Uses relationships, reasoning, query, and success fields in a structured Cypher-generation response.
- What you can do after
- Apply it via structured output contracts that downstream programs can process consistently.
Read how Knowledge Graphs and LLMs in Action explains JSON →
Everyday Data Visualization
JSON is a text-based data interchange format that represents values, arrays, and objects using a structured notation.
- How it is used
- Explains how nested data groups related values for coding and distinguishes it from tabular data.
- How deep it goes
- Explains arrays, dimensions, and named objects with examples, but does not develop parsing or serialization internals.
- Examples
- Uses pill containers, multidimensional schedules, day-labeled objects, and fruit-ranking records as structural examples.
- What you can do after
- Evaluate/understand how to read nested JSON arrays, objects, and labeled records.
Rust Servers, Services, and Apps
JSON is a text-based data-interchange format that represents structured values using objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Carries order data in a Rust web server and tutor lists between an Actix Web application and REST service.
- How deep it goes
- Demonstrates JSON files, HTTP responses, and typed deserialization, but does not develop the format's grammar or parsing internals.
- Examples
- Uses orders.json records and a tutor-list payload rendered through a Tera template.
- What you can do after
- Apply it via serde_json to deserialize service responses into Vec<Tutor> and serve JSON from a Rust web server.
DuckDB in Action
JSON is a text-based data interchange format representing structured values as objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON to exchange, query, transform, and temporarily bridge structured data between source formats and analytical workflows.
- How deep it goes
- Explains direct loading, nesting, flattening, relational transformation, and command-line conversion, but does not detail JSON syntax.
- Examples
- Demonstrates Stack Overflow XML exports, xidel extraction, jq conversion, DuckDB loading, and compressed CSV output.
- What you can do after
- Operate/configure JSON conversion pipelines and query or transform JSON data with DuckDB and command-line tools.
Software Testing with Generative AI
JSON is a text-based data-interchange standard representing structured data with objects, arrays, values, and name-value pairs.
- How it is used
- Uses JSON to request structured outputs and generate sample test data.
- How deep it goes
- Demonstrates objects, arrays, mixed types, nesting, and output constraints, while deferring more complex structures.
- Examples
- Uses booking records and room datasets containing strings, numbers, booleans, arrays, nested objects, and constrained values.
- What you can do after
- Apply it via ChatGPT to request structured objects, generate test datasets, and convert data between formats.
Read how Software Testing with Generative AI explains JSON →
Effective Conversational AI
JSON is a text-based data-interchange format that represents structured values using objects, arrays, and primitive values.
- How it is used
- Supports machine-readable conversational transcripts and structured extraction for downstream applications.
- How deep it goes
- Defines JSON as structured output and demonstrates transcript extraction, but stops short of broader syntax or validation details.
- Examples
- Uses claim-status transcripts, identifier extraction, schema examples, metadata, and human-representative handoff summaries.
- What you can do after
- Apply it via structured prompts to produce machine-readable summaries while checking fields, key names, value types, and duplication.
Build and Orchestrator in Go (From Scratch)
JSON is a text-based data interchange format that represents structured values using objects, arrays, names, and values.
- How it is used
- Carries task data between developers, manager APIs, and worker APIs through request and response bodies.
- How deep it goes
- Defines JSON through nested task payloads and API body examples, without descending into parsing or serialization internals.
- Examples
- Uses nested task events, arrays of task records, and POST, GET, and DELETE route tables as concrete examples.
- What you can do after
- Apply it via REST APIs to shape task requests and interpret task responses consistently.
Read how Build and Orchestrator in Go (From Scratch) explains JSON →
Refactoring to Rust
JSON is a text-based data interchange format representing structured values such as objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON to exchange structured data with typed Rust programs.
- How deep it goes
- Explains typed parsing, derived traits, compiler diagnostics, and runtime mismatches, but stops short of advanced format handling.
- Examples
- Demonstrates name-and-value objects, derived Data structs, parsing errors, and a Rust function matching a Python calculation.
- What you can do after
- Apply it via serde_json to deserialize JSON strings into Rust structs and use the resulting fields.
Graph Neural Networks in Action
JSON is a language-independent text format for representing structured data using objects, arrays, values, and name-value pairs.
- How it is used
- Uses JSON as raw graph data and an interchange format before ETL, schema alignment, preprocessing, or training.
- How deep it goes
- Defines key-value relationship storage and demonstrates conversion into graph representations, without detailing JSON syntax or validation.
- Examples
- Uses candidate recommendation relationships, hashed identifiers, Python conversion code, adjacency-list files, and edge-list files.
- What you can do after
- Apply via Python’s json module to load relationship data and produce adjacency-list or edge-list inputs for graph processing.
PostgreSQL Mistakes and How to Avoid Them
JSON is a text-based data interchange format that represents structured values using objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Uses JSON for flexible document storage and warns against replacing well-structured relational data with it.
- How deep it goes
- Explains document modeling, integrity consequences, extraction, casting, joins, and filtering, but stops short of exhaustive JSON reference detail.
- Examples
- Uses account and sales documents, inconsistent dates and timestamps, JSON-only tables, and extraction-heavy join queries.
- What you can do after
- Evaluate JSON designs and distinguish unpredictable ingestion data from stable relational data that should remain queryable columns.
Read how PostgreSQL Mistakes and How to Avoid Them explains JSON →
Road to Kubernetes
JSON is a text-based data interchange format representing structured values such as objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Carries structured responses between web applications and software, especially through REST APIs and microservices.
- How deep it goes
- Explains practical serialization in Python and JavaScript, but stops short of broader format rules or advanced headers.
- Examples
- Uses FastAPI's Hello World dictionary and an Express.js rocket-man object as concrete response examples.
- What you can do after
- Build it from scratch by creating JSON responses in FastAPI and Express.js applications.
Build Your Own Robot: Using Python, CRICKIT, and Raspberry Pi
JSON is a text-based data-interchange format that represents structured values using objects, arrays, strings, numbers, booleans, and null.
- How it is used
- Carries robot-control requests and web-service responses between Python clients and servers.
- How deep it goes
- Demonstrates automatic response serialization, request encoding, byte responses, and json.loads parsing, without detailing the full format specification.
- Examples
- Uses timestamp dictionaries, success-status responses, and speed parameters in robot movement requests.
- What you can do after
- Apply it via Python to parse web responses and send JSON-encoded movement parameters to robot services.
Read how Build Your Own Robot: Using Python, CRICKIT, and Raspberry Pi explains JSON →
Snowflake Data Engineering
JSON is a text-based data interchange format built from objects, arrays, and scalar values.
- How it is used
- Supports semistructured data exchange, Snowflake ingestion and querying, and externalized Snowpark connection configuration.
- How deep it goes
- Explains storage, key selection, type conversion, flattening, and configuration loading, but does not specify a complete JSON grammar.
- Examples
- Uses hotel-to-bakery order data, object-storage ingestion, and a Snowpark credentials file as concrete applications.
- What you can do after
- Apply it via Snowflake SQL and Python to exchange data, query nested values, normalize arrays, and configure Snowpark sessions.
Practical Automation with PowerShell: Effective scripting from the console to the cloud
JSON is a text-based data interchange format that represents structured values using objects, arrays, strings, numbers, Boolean values, and null.
- How it is used
- Drives adaptable PowerShell automations through external, human-readable configuration data.
- How deep it goes
- Explains object conversion and file handling, but stops short of native PowerShell JSON schema validation.
- Examples
- Uses registry configuration checks with key paths, property names, comparison operators, and expected values.
- What you can do after
- Apply it via ConvertTo-Json and ConvertFrom-Json to externalize configuration and reuse automation logic.
D3.js in Action, Third Edition
JSON is a text-based data interchange format representing structured values with objects, arrays, and primitive values.
- How it is used
- Supports storing and loading simple data structures, especially information fetched from API endpoints and used in D3 visualizations.
- How deep it goes
- Defines objects, arrays, and key-value access, but does not develop JSON syntax or complex data modeling.
- Examples
- Uses an employee array containing identifiers, names, positions, and Boolean D3 experience fields.
- What you can do after
- Apply JSON via d3.json() and access object fields with JavaScript dot notation.
Pandas Workout: 200 exercises to make you a stronger data analyst
JSON is a text-based data-interchange format representing structured values such as arrays, objects, strings, numbers, and Boolean values.
- How it is used
- Supports readable data exchange, internet APIs, and direct import into pandas DataFrames.
- How deep it goes
- Explains JSON’s main value types and array-of-object structure, then stops at pandas import and analysis rather than syntax details.
- Examples
- Uses a 1,000-city JSON dataset to compare population statistics, exclude large cities, find latitude extremes, and count states.
- What you can do after
- Apply it via pandas to load structured JSON records and analyze the resulting DataFrame.
Read how Pandas Workout: 200 exercises to make you a stronger data analyst explains JSON →
Investing for Programmers
JSON is a text-based data interchange format that represents structured values using objects, arrays, and primitive values.
- How it is used
- Structures REST API payloads for creating Notion pages, writing blocks, and storing LLM-generated research responses.
- How deep it goes
- Demonstrates nested payload construction, status validation, and chunked writes, but does not detail JSON syntax independently.
- Examples
- Uses page-property payloads, paragraph-block payloads, page-creation requests, and chunked text writes as concrete examples.
- What you can do after
- Operate Notion integrations by constructing JSON-compatible payloads, sending them through REST calls, and handling write failures.
Related concepts
- serializationused by · Serialization is the encoding of structured data or in-memory values into a representation suitable for storage or transmission and later reconstruction.
- protocol bufferalternative to · Protocol Buffers is a language- and platform-neutral schema system and compact binary serialization format for structured data, with support for generated code.
- JSON objecthas part · A JSON object is an unordered collection of members, each consisting of a string name paired with a JSON value and represented within curly braces.
- document databaseused by · A document database is a nonrelational database that stores and retrieves records as self-contained documents, often with flexible or nested structures.
- attributehas part · An attribute is a named property, value, or piece of information associated with an entity, object, or program element.
- GraphQLused by · GraphQL is a query language and execution specification for APIs that lets clients request and manipulate data through a schema.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.