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JSON vs YAML: What are the differences?

JSON: A lightweight data-interchange format. JavaScript Object Notation is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language; YAML: A straightforward machine parsable data serialization format designed for human readability and interaction. A human-readable data-serialization language. It is commonly used for configuration files, but could be used in many applications where data is being stored or transmitted.

JSON and YAML can be primarily classified as "Languages" tools.

Redsift, Mon Style, and Mercedes-Benz.io GmbH are some of the popular companies that use JSON, whereas YAML is used by Zoteca, Doable, and Skydive project. JSON has a broader approval, being mentioned in 20 company stacks & 104 developers stacks; compared to YAML, which is listed in 5 company stacks and 4 developer stacks.

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JSONJSON

Hi. Currently, I have a requirement where I have to create a new JSON file based on the input CSV file, validate the generated JSON file, and upload the JSON file into the application (which runs in AWS) using API. Kindly suggest the best language that can meet the above requirement. I feel Python will be better, but I am not sure with the justification of why python. Can you provide your views on this?

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Replies (3)
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PythonPython

Python is very flexible and definitely up the job (although, in reality, any language will be able to cope with this task!). Python has some good libraries built in, and also some third party libraries that will help here. 1. Convert CSV -> JSON 2. Validate against a schema 3. Deploy to AWS

  1. The builtins include json and csv libraries, and, depending on the complexity of the csv file, it is fairly simple to convert:
import csv
import json

with open("your_input.csv", "r") as f:
    csv_as_dict = list(csv.DictReader(f))[0]

with open("your_output.json", "w") as f:
    json.dump(csv_as_dict, f)
  1. The validation part is handled nicely by this library: https://pypi.org/project/jsonschema/ It allows you to create a schema and check whether what you have created works for what you want to do. It is based on the json schema standard, allowing annotation and validation of any json

  2. It as an AWS library to automate the upload - or in fact do pretty much anything with AWS - from within your codebase: https://aws.amazon.com/sdk-for-python/ This will handle authentication to AWS and uploading / deploying the file to wherever it needs to go.

A lot depends on the last two pieces, but the converting itself is really pretty neat.

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Max Musing
Founder & CEO at BaseDash · | 1 upvotes · 140.7K views
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Node.jsNode.js
at

This should be pretty doable in any language. Go with whatever you're most familiar with.

That being said, there's a case to be made for using Node.js since it's trivial to convert an object to JSON and vice versa.

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GoGo

I would use Go. Since CSV files are flat (no hierarchy), you could use the encoding/csv package to read each row, and write out the values as JSON. See https://medium.com/@ankurraina/reading-a-simple-csv-in-go-36d7a269cecd. You just have to figure out in advance what the key is for each row.

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    What is JSON?

    JavaScript Object Notation is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language.

    What is YAML?

    A human-readable data-serialization language. It is commonly used for configuration files, but could be used in many applications where data is being stored or transmitted.

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    What are some alternatives to JSON and YAML?
    Protobuf
    Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler.
    Avro
    It is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    OData
    It is an ISO/IEC approved, OASIS standard that defines a set of best practices for building and consuming RESTful APIs. It helps you focus on your business logic while building RESTful APIs without having to worry about the various approaches to define request and response headers, status codes, HTTP methods, URL conventions, media types, payload formats, query options, etc.
    MessagePack
    It is an efficient binary serialization format. It lets you exchange data among multiple languages like JSON. But it's faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves.
    See all alternatives