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Confluent VS Apache ORC

Compare Confluent VS Apache ORC and see what are their differences

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Contents:
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  2. » Videos
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Confluent logo Confluent

Confluent offers a real-time data platform built around Apache Kafka.

Apache ORC logo Apache ORC

Apache ORC is a columnar storage for Hadoop workloads.
  • Confluent Landing page
    Landing page //
    2023-10-22
  • Apache ORC Landing page
    Landing page //
    2022-09-18

Confluent features and specs

  • Scalability
    Confluent is built on Apache Kafka, which allows for smooth scalability to handle growing data needs without significant performance degradation.
  • Real-Time Data Processing
    Confluent enables real-time streaming data processing, which is beneficial for applications requiring immediate data insights and actions.
  • Comprehensive Ecosystem
    Confluent provides a rich set of tools and connectors that integrate seamlessly with various data sources and sinks, making it easier to build and manage data pipelines.
  • Ease of Use
    Confluent offers an intuitive user interface and comprehensive documentation, which simplifies the setup and management of Kafka clusters.
  • Managed Service Option
    Confluent Cloud provides a fully managed Kafka service, reducing the operational burden on the engineering team and allowing businesses to focus on developing applications.
  • Advanced Security Features
    Confluent offers robust security features including encryption, SSL, ACLs, and more, ensuring that data streams are protected.
  • Strong Customer Support
    Confluent offers professional support and consultancy services which can be very helpful for enterprises requiring 24/7 support and expertise.

Possible disadvantages of Confluent

  • Cost
    Confluent can be expensive, especially for small to medium-sized businesses. The costs can grow significantly with scale and additional enterprise features.
  • Complexity
    Despite its ease of use, the underlying system’s complexity can pose a challenge, particularly for teams who are new to Kafka or streaming data technologies.
  • Resource Intensive
    Running Confluent on-premises can be resource-intensive, requiring significant computational and storage resources to maintain optimal performance.
  • Learning Curve
    For those unfamiliar with Kafka and streaming technologies, there is a steep learning curve which can lead to longer implementation times.
  • Vendor Lock-In
    Utilizing Confluent’s proprietary tools and connectors can result in vendor lock-in, making it difficult to switch to alternative solutions without considerable effort and reconfiguration.
  • Dependency on Cloud Provider
    If using Confluent Cloud, dependency on the cloud provider’s infrastructure may introduce compliance and control limitations, particularly for businesses with strict data sovereignty requirements.

Apache ORC features and specs

No features have been listed yet.

Confluent videos

+ Add

1. Intro | Monitoring Kafka in Confluent Control Center

More videos:

  • Review - Jason Gustafson, Confluent: Revisiting Exactly One Semantics (EOS) | Bay Area Apache Kafka® Meetup
  • Review - CLEARER SKIN AFTER 1 USE‼️| Ancient Cosmetics Update✨| CONFLUENT & RETICULATED PAPILLOMATOSIS CURE?😩

Apache ORC videos

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

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Category Popularity

0-100% (relative to Confluent and Apache ORC)

Confluent

Apache ORC

Stream Processing
100 100%
Stream Processing
0% 0
Big Data
82 82%
Big Data
18% 18
Data Dashboard
0 0%
Data Dashboard
100% 100
Data Management
84 84%
Data Management
16% 16

User comments

Share your experience with using Confluent and Apache ORC. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Apache ORC should be more popular than Confluent. It has been mentiond 3 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.

Confluent mentions (1)

  • Spring Boot Event Streaming with Kafka
    We’re going to setup a Kafka cluster using confluent.io, create a producer and consumer as well as enhance our behavior driven tests to include the new interface. We’re going to update our helm chart so that the updates are seamless to Kubernetes and we’re going to leverage our observability stack to propagate the traces in the published messages. Source: over 3 years ago

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 / over 2 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 / over 3 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 / almost 4 years ago

What are some alternatives?

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Impala - Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

SQream - SQream empowers organizations to analyze the full scope of their Massive Data, from terabytes to petabytes, to achieve critical insights which were previously unattainable.

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

Apache Kudu - Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.

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