Software Alternatives, Accelerators & Startups
Register | Login

Apache Pig VS JSON

Compare Apache Pig VS JSON and see what are their differences

ASocks
Clear, Fast & Unlimited. Residential & Mobile Proxies For Best Price. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Apache Pig logo Apache Pig

Pig is a high-level platform for creating MapReduce programs used with Hadoop.

JSON logo JSON

(JavaScript Object Notation) is a lightweight data-interchange format
  • Apache Pig Landing page
    Landing page //
    2021-12-31
  • JSON Landing page
    Landing page //
    2021-09-28

Apache Pig features and specs

  • Simplicity
    Apache Pig provides a high-level scripting language called Pig Latin that is much easier to write and understand than complex MapReduce code, enabling faster development time.
  • Abstracts Hadoop Complexity
    Pig abstracts the complexity of Hadoop, allowing developers to focus on data processing rather than worrying about the intricacies of Hadoop’s underlying mechanisms.
  • Extensibility
    Pig allows user-defined functions (UDFs) to process various types of data, giving users the flexibility to extend its functionality according to their specific requirements.
  • Optimized Query Execution
    Pig includes a rich set of optimization techniques that automatically optimize the execution of scripts, thereby improving performance without needing manual tuning.
  • Error Handling and Debugging
    The platform has an extensive error handling mechanism and provides the ability to make debugging easier through logging and stack traces, making it simpler to troubleshoot issues.

Possible disadvantages of Apache Pig

  • Performance Limitations
    While Pig simplifies writing MapReduce operations, it may not always offer the same level of performance as hand-optimized, low-level MapReduce code.
  • Limited Real-Time Processing
    Pig is primarily designed for batch processing and may not be the best choice for real-time data processing requirements.
  • Steeper Learning Curve for SQL Users
    Developers who are already familiar with SQL might find Pig Latin to be less intuitive at first, resulting in a steeper learning curve for building complex data transformations.
  • Maintenance Overhead
    As Pig scripts grow in complexity and number, maintaining and managing these scripts can become challenging, particularly in large-scale production environments.
  • Growing Obsolescence
    With the rise of more versatile and performant Big Data tools like Apache Spark and Hive, Pig’s relevance and community support have been on the decline.

JSON features and specs

  • Simplicity
    JSON is easy to read and write due to its straightforward syntax, making it a convenient data format for both humans and machines.
  • Language Independence
    JSON is supported by many programming languages, making it a versatile choice for data interchange across different environments.
  • Lightweight
    JSON's compact format allows for efficient data transfer, which is particularly beneficial in web applications where bandwidth is a concern.
  • Integration
    JSON easily integrates with modern web technologies and APIs, making it a preferred choice for RESTful services and web applications.
  • Data Structure
    JSON supports complex data structures, including objects and arrays, providing flexibility in representing various data forms.

Possible disadvantages of JSON

  • Limited Data Types
    JSON supports a limited set of data types, which may require additional handling when working with more complex data structures found in other formats.
  • No Comments
    JSON lacks a native mechanism for including comments within the data, which can be a limitation for documentation and readability purposes.
  • Security Concerns
    Parsing JSON can introduce security vulnerabilities if not properly handled, such as malicious data execution through insecure deserialization.
  • Verbosity
    Although lightweight, JSON can become verbose for highly nested structures, which can impact readability and processing performance.
  • Error Handling
    JSON's lack of detailed error handling mechanisms can make debugging more difficult when dealing with malformed data or parsing errors.

Analysis of Apache Pig

Overall verdict

  • Apache Pig is a valuable tool for data professionals working within a Hadoop environment, especially those who prefer or require a language more accessible than Java. However, its utility might be overshadowed by newer technologies such as Apache Spark, which offers more extensive functionality and faster processing speeds.

Why this product is good

  • Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. It simplifies the processing of large data sets by providing a scripting language known as Pig Latin, which is easier to use compared to Java MapReduce. Pig is designed to handle both structured and unstructured data and is particularly effective for tasks involving data manipulation, transformation, and analysis. Its ability to optimize code execution through pig-specific optimizations and automatic transformations makes it a powerful tool for those familiar with Hadoop ecosystems.

Recommended for

    Apache Pig is recommended for data engineers and analysts who are working in Apache Hadoop environments and need to perform ETL (Extract, Transform, Load) operations on large datasets. It is also suitable for teams looking to leverage existing Hadoop infrastructures without delving into complex Java MapReduce programming or when migrating legacy processing scripts based on Pig Latin.

Analysis of JSON

Overall verdict

  • Yes, JSON is generally considered a good choice for data interchange, especially in web applications, due to its simplicity, wide support across programming languages, and ease of use.

Why this product is good

  • JSON is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate. Due to its simplicity and flexibility, it has become a widely adopted standard for data exchange on the web.

Recommended for

  • Web APIs and services
  • Applications needing a lightweight data format
  • Communication between server and client
  • Configuration files
  • Data interchange between diverse systems

Apache Pig videos

+ Add

Pig Tutorial | Apache Pig Script | Hadoop Pig Tutorial | Edureka

More videos:

  • Review - Simple Data Analysis with Apache Pig

JSON videos

+ Add

Parsing JSON Review - Part 1

More videos:

  • Review - Parsing JSON Review - Part 2
  • Review - JSon Foreign Vol.1 Review

Category Popularity

0-100% (relative to Apache Pig and JSON)

Apache Pig

JSON

Data Dashboard
100 100%
Data Dashboard
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Database Tools
100 100%
Database Tools
0% 0
Software Development
0 0%
Software Development
100% 100

User comments

Share your experience with using Apache Pig and JSON. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, JSON should be more popular than Apache Pig. It has been mentiond 13 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Pig mentions (2)

  • In One Minute : Hadoop
    Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / over 2 years ago
  • Spark is lit once again
    In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark. Since then Spark has became one of the most popular data processing engines. It is very easy to start using Lighter on YARN deployments. Just run a docker with proper configuration... - Source: dev.to / over 3 years ago

JSON mentions (13)

  • The Last Breaking Change | JSON Schema Blog
    The YAML 0.1 spec was sent to a public user group in May 2001. JSON was named in a State Software internal discussion. State Software was founded in March 2001. json.org was launched in 2002. Therefore you’re just wrong: YAML came out before JSON. Source: about 2 years ago
  • Why does wine give warnings about using 64bit prefixes, or has 32bit packages? Hasn't the world moved on from 32 bit a century ago?
    How come that doesn't apply to other libraries? For example, when I write Java or Node.js programs, I don't need to make sure packages like json.org or express.js have a 32bit or 64bit environment. What makes windows libs different than NPM libs? Source: almost 3 years ago
  • “Ignore the f'ing haters ” And other lessons learned from creating a popular
    The first two sentences of the text on http://json.org are "JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write." It's a primary goal of JSON, it's fair to question whether it's successful at it. Personally, I'd much rather write TOML or S expressions. I don't like YAML at all, the whitespace sensitivity drives me nuts. - Source: Hacker News / almost 3 years ago
  • Recording your JSON data to MCAP, a file format that support multiple serialization formats
    To help you make the transition, we’ve written a tutorial on how to write an MCAP writer in Python to record JSON data to an MCAP file. Source: almost 3 years ago
  • replace \" with "
    What you need to probably do is to step back and learn the format for JSON, and the core data structures that you will find in most languages:. Source: almost 3 years ago
View more

What are some alternatives?

When comparing Apache Pig and JSON, you can also consider the following products

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

LibreOffice - Base - Base, database, database frontend, LibreOffice, ODF, Open Standards, SQL, ODBC

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Brilliant Database - Create a personal or business desktop database fast and easily using this simple all-in-one database software. Free 30 day trial.

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

Microsoft Office Access - Access is now much more than a way to create desktop databases. It’s an easy-to-use tool for quickly creating browser-based database applications.

Loading...