Software Alternatives, Accelerators & Startups

Hadoop VS Apache ORC

Compare Hadoop VS Apache ORC and see what are their differences

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing

Apache ORC logo Apache ORC

Apache ORC is a columnar storage for Hadoop workloads.
  • Hadoop Landing page
    Landing page //
    2021-09-17
  • Apache ORC Landing page
    Landing page //
    2022-09-18

Hadoop features and specs

  • Scalability
    Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
  • Cost-Effective
    It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
  • Fault Tolerance
    Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
  • Flexibility
    It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
  • Parallel Processing
    Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
  • Community Support
    As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

  • Complexity
    Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
  • Overhead
    The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
  • Security
    While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
  • Hardware Requirements
    Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
  • Lack of Real-Time Processing
    Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
  • Data Integrity
    Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

Apache ORC features and specs

  • Efficient Compression
    ORC provides highly efficient compression, which reduces the storage footprint of data and enhances performance by decreasing I/O operations.
  • Columnar Storage
    The columnar storage format significantly improves read performance by allowing for selective access to necessary columns while ignoring others.
  • Predicate Pushdown
    ORC supports predicate pushdown, enabling the query engine to skip over non-relevant data, thus enhancing query performance.
  • Type Richness
    ORC supports complex types (like structs and maps), making it suitable for diverse data storage and query needs.
  • Schema Evolution
    It facilitates seamless schema evolution, allowing easier adjustments to the dataset over time without breaking existing queries.
  • Built-in Indexes
    Indexes such as bloom filters and min/max values are built-in, accelerating query processing by enabling quicker data lookup.

Possible disadvantages of Apache ORC

  • Complexity
    The intricacies of its features may introduce additional complexity in implementation and maintenance, potentially increasing the learning curve.
  • Write Performance
    While ORC is optimized for read-heavy workloads, its write performance can be less efficient compared to other formats like Parquet.
  • Compatibility
    ORC may not be as widely supported as other formats, limiting the choice of tools and environments that can leverage its full capabilities.
  • Compression Overhead
    The process of compressing and decompressing data can introduce a computational overhead, affecting performance in some scenarios.

Analysis of Hadoop

Overall verdict

  • Hadoop is a robust and powerful data processing platform that is well-suited for organizations that need to manage and analyze large-scale data. Its resilience, scalability, and open-source nature make it a popular choice for big data solutions. However, it may not be the best fit for all use cases, especially those requiring real-time processing or where ease of use is a priority.

Why this product is good

  • Hadoop is renowned for its ability to store and process large datasets using a distributed computing model. It is scalable, cost-effective, and efficient in handling massive volumes of data across clusters of computers. Its ecosystem includes a wide range of tools and technologies like HDFS, MapReduce, YARN, and Hive that enhance data processing and analysis capabilities.

Recommended for

  • Organizations dealing with vast amounts of data needing efficient batch processing.
  • Businesses that require scalable storage solutions to manage their data growth.
  • Companies interested in leveraging a diverse ecosystem of data processing tools and technologies.
  • Technical teams that have the expertise to manage and optimize complex distributed systems.

Hadoop videos

What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

Apache ORC videos

No Apache ORC videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Hadoop and Apache ORC)
Databases
89 89%
11% 11
Big Data
81 81%
19% 19
Data Dashboard
0 0%
100% 100
Relational Databases
100 100%
0% 0

User comments

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

Reviews

These are some of the external sources and on-site user reviews we've used to compare Hadoop and Apache ORC

Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is...

Apache ORC Reviews

We have no reviews of Apache ORC yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Hadoop should be more popular than Apache ORC. It has been mentiond 26 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.

Hadoop mentions (26)

  • JuiceFS 1.3 Beta 2 Integrates Apache Ranger for Fine-Grained Access Control
    To simplify โ€‹โ€‹fine-grained permission managementโ€‹โ€‹ and enable centralized โ€‹โ€‹web-based administrationโ€‹โ€‹, JuiceFS now supports โ€‹โ€‹Apache Rangerโ€‹โ€‹, a widely adopted security framework in the Hadoop ecosystem. - Source: dev.to / 4 months ago
  • Apache Hadoop: Open Source Business Model, Funding, and Community
    This post provides an inโ€depth look at Apache Hadoop, a transformative distributed computing framework built on an open source business model. We explore its history, innovative open funding strategies, the influence of the Apache License 2.0, and the vibrant community that drives its continuous evolution. Additionally, we examine practical use cases, upcoming challenges in scaling big data processing, and future... - Source: dev.to / 5 months ago
  • What is Apache Kafka? The Open Source Business Model, Funding, and Community
    Modular Integration: Thanks to its modular approach, Kafka integrates seamlessly with other systems including container orchestration platforms like Kubernetes and third-party tools such as Apache Hadoop. - Source: dev.to / 5 months ago
  • India Open Source Development: Harnessing Collaborative Innovation for Global Impact
    Over the years, Indian developers have played increasingly vital roles in many international projects. From contributions to frameworks such as Kubernetes and Apache Hadoop to the emergence of homegrown platforms like OpenStack India, India has steadily carved out a global reputation as a powerhouse of open source talent. - Source: dev.to / 5 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 7 months ago
View more

Apache ORC mentions (3)

  • Java Serialization with Protocol Buffers
    The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto. - Source: dev.to / almost 3 years ago
  • AWS EMR Cost Optimization Guide
    Data formatting is another place to make gains. When dealing with huge amounts of data, finding the data you need can take up a significant amount of your compute time. Apache Parquet and Apache ORC are columnar data formats optimized for analytics that pre-aggregate metadata about columns. If your EMR queries column intensive data like sum, max, or count, you can see significant speed improvements by reformatting... - Source: dev.to / almost 4 years ago
  • Apache Hudi - The Streaming Data Lake Platform
    The following stack captures layers of software components that make up Hudi, with each layer depending on and drawing strength from the layer below. Typically, data lake users write data out once using an open file format like Apache Parquet/ORC stored on top of extremely scalable cloud storage or distributed file systems. Hudi provides a self-managing data plane to ingest, transform and manage this data, in a... - Source: dev.to / about 4 years ago

What are some alternatives?

When comparing Hadoop and Apache ORC, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Apache Parquet - Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

BlueData - BlueData's software platform makes it easier, faster and more cost-effective for organizations to deploy Big Data infrastructure on-premises.