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Apache Pig VS Protobuf

Compare Apache Pig VS Protobuf and see what are their differences

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Apache Pig logo Apache Pig

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

Protobuf logo Protobuf

Protocol buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data.
  • Apache Pig Landing page
    Landing page //
    2021-12-31
  • Protobuf Landing page
    Landing page //
    2023-08-29

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.

Protobuf features and specs

  • Efficient Serialization
    Protobuf is known for its high efficiency in serializing structured data. It is faster and produces smaller size messages compared to JSON or XML, making it ideal for bandwidth-limited and resource-constrained environments.
  • Language Support
    Protobuf supports multiple programming languages including Java, C++, Python, Ruby, and Go. This makes it versatile and useful in heterogeneous environments.
  • Versioning Support
    It natively supports schema evolution without breaking existing implementations. Fields can be added or removed over time, ensuring backward and forward compatibility.
  • Type Safety
    Being a strongly typed data format, Protobuf ensures that data is correctly typed across different systems, preventing serialization and deserialization errors common with loosely typed formats.

Possible disadvantages of Protobuf

  • Learning Curve
    Protobuf requires learning and understanding its schema definitions and compiler usage, which might be a challenge for new developers.
  • Lack of Human Readability
    Serialized Protobuf data is in a binary format, making it less readable and debuggable compared to JSON or XML without specialized tools.
  • Limited Built-in Support for Complex Data Types
    By default, Protobuf does not provide comprehensive support for handling complex data types like maps or unions compared to some other data serialization formats, requiring workarounds.
  • Tooling Requirement
    Using Protobuf necessitates a compilation step where `.proto` files are converted into code, requiring additional tooling and build system integration.

Apache Pig videos

+ Add

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

More videos:

  • Review - Simple Data Analysis with Apache Pig

Protobuf videos

+ Add

StreamBerry, part 2 : introduction to Google ProtoBuf

Category Popularity

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User comments

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Social recommendations and mentions

Based on our record, Protobuf seems to be a lot more popular than Apache Pig. While we know about 83 links to Protobuf, we've tracked only 2 mentions of Apache Pig. 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

Protobuf mentions (83)

  • JSON vs Protocol Buffers vs FlatBuffers: A Deep Dive
    Protocol Buffers, developed by Google, is a compact and efficient binary serialization format designed for high-performance data exchange. - Source: dev.to / about 2 months ago
  • Developing games on and for Mac and Linux
    Protocol Buffers: https://developers.google.com/protocol-buffers. - Source: dev.to / about 2 years ago
  • Adding Codable conformance to Union with Metaprogramming
    ProtocolBuffers’ OneOf message addresses the case of having a message with many fields where at most one field will be set at the same time. - Source: dev.to / over 2 years ago
  • Logcat is awful. What would you improve?
    That's definitely the bigger thing. I think something like Protocol Buffers (Protobuf) is what you're looking for there. Output the data and consume it by something that can handle the analysis. Source: over 2 years ago
  • Bitcoin is the "narrow waist" of internet-based value
    These protocols prevent an O(N x M) explosion of code that have to solve for many cases. For example, since JSON is an almost ubiquitous format for wire transfer (although other things do exist like protobufs), if I had N data formats that I want to serialize, I only need to write N serializers/deserializers (SerDes). If there was no such narrow waist and there were M alternatives to JSON in wide usage, I would... Source: over 2 years ago
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What are some alternatives?

When comparing Apache Pig and Protobuf, 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.

gRPC - Application and Data, Languages & Frameworks, Remote Procedure Call (RPC), and Service Discovery

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.

Messagepack - An efficient binary serialization format.

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

Apache Thrift - An interface definition language and communication protocol for creating cross-language services.

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