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Apache Chukwa VS Apache Avro

Compare Apache Chukwa VS Apache Avro and see what are their differences

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Apache Chukwa logo Apache Chukwa

Big Data Processing and Distribution

Apache Avro logo Apache Avro

Apache Avro is a comprehensive data serialization system and acting as a source of data exchanger service for Apache Hadoop.
  • Apache Chukwa Landing page
    Landing page //
    2021-09-17
  • Apache Avro Landing page
    Landing page //
    2022-10-21

Apache Chukwa features and specs

No features have been listed yet.

Apache Avro features and specs

  • Schema Evolution
    Avro supports seamless schema evolution, allowing you to add fields and change data types without impacting existing data. This flexibility is advantageous in environments where data structures frequently change.
  • Compact Binary Format
    Avro uses a compact binary format for data serialization, leading to efficient storage and faster data transmission compared to text-based formats like JSON or XML.
  • Language Agnostic
    Avro is designed to be language agnostic, with support for multiple programming languages, including Java, Python, C++, and more. This makes it easier to integrate with various systems.
  • No Code Generation Required
    Unlike other serialization frameworks such as Protocol Buffers and Thrift, Avro does not require generating code from the schema, simplifying the development process.
  • Self Describing
    Each Avro data file contains its schema, making the data self-describing. This helps maintain consistency between data producers and consumers.

Possible disadvantages of Apache Avro

  • Lack of Human Readability
    Avro's binary format is not human-readable, making it challenging to debug or inspect data without specialized tools.
  • Schema Management Overhead
    While Avro supports schema evolution, managing and maintaining these schemas across multiple services can become complex and require additional coordination.
  • Limited Support for Complex Data Types
    Avro has limitations when it comes to the representation of certain complex data types, which might necessitate workarounds or transformations that add complexity.
  • Learning Curve
    Users who are new to Apache Avro may face a learning curve to understand schema creation, evolution, and integration within their data pipelines.
  • Dependency on Schema Registry
    Using Avro effectively often requires integrating with a schema registry, adding an extra layer of infrastructure and potential points of failure.

Apache Chukwa videos

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Apache Avro videos

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CCA 175 : Apache Avro Introduction

More videos:

  • Review - End to end Data Governance with Apache Avro and Atlas

Category Popularity

0-100% (relative to Apache Chukwa and Apache Avro)

Apache Chukwa

Apache Avro

Big Data
100 100%
Big Data
0% 0
Development
0 0%
Development
100% 100
Data Dashboard
44 44%
Data Dashboard
56% 56
Databases
35 35%
Databases
65% 65

User comments

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Social recommendations and mentions

Based on our record, Apache Avro seems to be a lot more popular than Apache Chukwa. While we know about 14 links to Apache Avro, we've tracked only 1 mention of Apache Chukwa. 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 Chukwa mentions (1)

Apache Avro mentions (14)

  • Pulumi Gestalt 0.0.1 released
    A schema.json converter for easier ingestion (likely supporting Avro and Protobuf). - Source: dev.to / 3 months ago
  • Why Data Security is Broken and How to Fix it?
    Security Aware Data Metadata Data schema formats such as Avro and Json currently lack built-in support for data sensitivity or security-aware metadata. Additionally, common formats like Parquet and Iceberg, while efficient for storing large datasets, don’t natively include security-aware metadata. At Jarrid, we are exploring various metadata formats to incorporate data sensitivity and security-aware attributes... - Source: dev.to / 8 months ago
  • Open Table Formats Such as Apache Iceberg Are Inevitable for Analytical Data
    Apache AVRO [1] is one but it has been largely replaced by Parquet [2] which is a hybrid row/columnar format [1] https://avro.apache.org/. - Source: Hacker News / over 1 year ago
  • Generating Avro Schemas from Go types
    The most common format for describing schema in this scenario is Apache Avro. - Source: dev.to / over 1 year ago
  • gRPC on the client side
    Other serialization alternatives have a schema validation option: e.g., Avro, Kryo and Protocol Buffers. Interestingly enough, gRPC uses Protobuf to offer RPC across distributed components:. - Source: dev.to / over 2 years ago
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What are some alternatives?

When comparing Apache Chukwa and Apache Avro, you can also consider the following products

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Apache Ambari - Ambari is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Hadoop clusters.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Apache HBase - Apache HBase – Apache HBase™ Home

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.

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

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