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JSON vs OData: What are the differences?

1. Data Format: JSON is a data format used for data interchange, while OData is a protocol for building and consuming RESTful APIs. JSON represents data in a readable format, whereas OData provides a set of conventions for creating and consuming web services. 2. Querying Capability: JSON does not have built-in querying capabilities, whereas OData allows for querying data using a standardized URL-based query language. This enables OData to perform more advanced data operations such as filtering, sorting, and pagination. 3. Metadata: JSON does not inherently support metadata, while OData includes metadata that describes the data model and structure of the API. This metadata can be used by consumers to understand and interact with the API more effectively. 4. Standardization: JSON is a general-purpose data format with no strict guidelines or standards, while OData follows a set of industry standards and best practices for building APIs. OData provides a standardized way to expose and consume data over the web. 5. Navigation and Relationships: OData supports the ability to navigate between related entities and establish relationships in the data, whereas in JSON, managing relationships between data entities can be more challenging and requires custom implementation. 6. Payload Size: JSON tends to have smaller payload sizes compared to OData due to its simplicity and lack of additional metadata. OData payloads can be larger due to the inclusion of metadata and additional query options.

In Summary, JSON and OData differ in their fundamental data format, querying capabilities, support for metadata, standardization, navigation of relationships, and payload sizes.

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JSONJSON
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PythonPython

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 · 284.5K 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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GolangGolang

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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Pros of JSON
Pros of OData
  • 5
    Simple
  • 4
    Widely supported
  • 7
    Patterns for paging, sorting, filtering
  • 5
    ISO Standard
  • 4
    Query Language
  • 3
    RESTful
  • 3
    No overfetching, no underfetching
  • 2
    Get many resources in a single request
  • 2
    Self-documenting
  • 2
    Batch requests
  • 2
    Bulk requests ("array upsert")
  • 2
    Ask for what you need, get exactly that
  • 1
    Evolve your API by following the compatibility rules
  • 1
    Resource model defines conventional operations
  • 1
    Resource Modification Language

Sign up to add or upvote prosMake informed product decisions

Cons of JSON
Cons of OData
    Be the first to leave a con
    • 1
      Overwhelming, no "baby steps" documentation

    Sign up to add or upvote consMake informed product decisions

    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 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.

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    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    7
    2551
    What are some alternatives to JSON and OData?
    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.
    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.
    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