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Apache Pinot VS Apache Kafka

Compare Apache Pinot VS Apache Kafka and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Apache Pinot logo Apache Pinot

Apache Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency.

Apache Kafka logo Apache Kafka

Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Not present
  • Apache Kafka Landing page
    Landing page //
    2022-10-01

Apache Pinot features and specs

No features have been listed yet.

Apache Kafka features and specs

  • High Throughput
    Kafka is capable of handling thousands of messages per second due to its distributed architecture, making it suitable for applications that require high throughput.
  • Scalability
    Kafka can easily scale horizontally by adding more brokers to a cluster, making it highly scalable to serve increased loads.
  • Fault Tolerance
    Kafka has built-in replication, ensuring that data is replicated across multiple brokers, providing fault tolerance and high availability.
  • Durability
    Kafka ensures data durability by writing data to disk, which can be replicated to other nodes, ensuring data is not lost even if a broker fails.
  • Real-time Processing
    Kafka supports real-time data streaming, enabling applications to process and react to data as it arrives.
  • Decoupling of Systems
    Kafka acts as a buffer and decouples the production and consumption of messages, allowing independent scaling and management of producers and consumers.
  • Wide Ecosystem
    The Kafka ecosystem includes various tools and connectors such as Kafka Streams, Kafka Connect, and KSQL, which enrich the functionality of Kafka.
  • Strong Community Support
    Kafka has strong community support and extensive documentation, making it easier for developers to find help and resources.

Possible disadvantages of Apache Kafka

  • Complex Setup and Management
    Kafka's distributed nature can make initial setup and ongoing management complex, requiring expert knowledge and significant administrative effort.
  • Operational Overhead
    Running Kafka clusters involves additional operational overhead, including hardware provisioning, monitoring, tuning, and scaling.
  • Latency Sensitivity
    Despite its high throughput, Kafka may experience increased latency in certain scenarios, especially when configured for high durability and consistency.
  • Learning Curve
    The concepts and architecture of Kafka can be difficult for new users to grasp, leading to a steep learning curve.
  • Hardware Intensive
    Kafka's performance characteristics often require dedicated and powerful hardware, which can be costly to procure and maintain.
  • Dependency Management
    Managing Kafka's dependencies and ensuring compatibility between versions of Kafka, Zookeeper, and other ecosystem tools can be challenging.
  • Limited Support for Small Messages
    Kafka is optimized for large throughput and can be inefficient for applications that require handling a lot of small messages, where overhead can become significant.
  • Operational Complexity for Small Teams
    Smaller teams might find the operational complexity and maintenance burden of Kafka difficult to manage without a dedicated operations or DevOps team.

Apache Pinot videos

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Apache Kafka videos

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Apache Kafka Tutorial | What is Apache Kafka? | Kafka Tutorial for Beginners | Edureka

More videos:

  • Review - Apache Kafka - Getting Started - Kafka Multi-node Cluster - Review Properties
  • Review - 4. Apache Kafka Fundamentals | Confluent Fundamentals for Apache Kafka®
  • Review - Apache Kafka in 6 minutes
  • Review - Apache Kafka Explained (Comprehensive Overview)
  • Review - 2. Motivations and Customer Use Cases | Apache Kafka Fundamentals

Category Popularity

0-100% (relative to Apache Pinot and Apache Kafka)

Apache Pinot

Apache Kafka

Big Data
100 100%
Big Data
0% 0
Stream Processing
0 0%
Stream Processing
100% 100
Databases
14 14%
Databases
86% 86
Data Integration
0 0%
Data Integration
100% 100

User comments

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Reviews

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

Apache Pinot Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
The biggest value behind Apache Pinot is that you can index each column, which allows it to process data at a super fast speed. “It’s like taking a pivot table and saving it to disk. So you can get this highly dimensional data with pre-computed aggregations and pull those out in what seems like supernaturally fast time,” says Tim Berglund, Developer Relations at StarTree....
Source: embeddable.com

Apache Kafka Reviews

Best ETL Tools: A Curated List
Debezium is an open-source Change Data Capture (CDC) tool that originated from RedHat. It leverages Apache Kafka and Kafka Connect to enable real-time data replication from databases. Debezium was partly inspired by Martin Kleppmann’s "Turning the Database Inside Out" concept, which emphasized the power of the CDC for modern data pipelines.
Source: estuary.dev
Best message queue for cloud-native apps
If you take the time to sort out the history of message queues, you will find a very interesting phenomenon. Most of the currently popular message queues were born around 2010. For example, Apache Kafka was born at LinkedIn in 2010, Derek Collison developed Nats in 2010, and Apache Pulsar was born at Yahoo in 2012. What is the reason for this?
Source: docs.vanus.ai
Are Free, Open-Source Message Queues Right For You?
Apache Kafka is a highly scalable and robust messaging queue system designed by LinkedIn and donated to the Apache Software Foundation. It's ideal for real-time data streaming and processing, providing high throughput for publishing and subscribing to records or messages. Kafka is typically used in scenarios that require real-time analytics and monitoring, IoT applications,...
Source: blog.iron.io
10 Best Open Source ETL Tools for Data Integration
It is difficult to anticipate the exact demand for open-source tools in 2023 because it depends on various factors and emerging trends. However, open-source solutions such as Kubernetes for container orchestration, TensorFlow for machine learning, Apache Kafka for real-time data streaming, and Prometheus for monitoring and observability are expected to grow in prominence in...
Source: testsigma.com
11 Best FREE Open-Source ETL Tools in 2024
Apache Kafka is an Open-Source Data Streaming Tool written in Scala and Java. It publishes and subscribes to a stream of records in a fault-tolerant manner and provides a unified, high-throughput, and low-latency platform to manage data.
Source: hevodata.com

Social recommendations and mentions

Based on our record, Apache Kafka seems to be more popular. It has been mentiond 144 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 Pinot mentions (0)

We have not tracked any mentions of Apache Pinot yet. Tracking of Apache Pinot recommendations started around May 2025.

Apache Kafka mentions (144)

View more

What are some alternatives?

When comparing Apache Pinot and Apache Kafka, you can also consider the following products

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

RabbitMQ - RabbitMQ is an open source message broker software.

Apache Druid - Fast column-oriented distributed data store

StatCounter - StatCounter is a simple but powerful real-time web analytics service that helps you track, analyse and understand your visitors so you can make good decisions to become more successful online.

DuckDB - DuckDB is an in-process SQL OLAP database management system

Histats - Start tracking your visitors in 1 minute!

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