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Apache Kylin VS Impala

Compare Apache Kylin VS Impala and see what are their differences

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Contents:
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Apache Kylin logo Apache Kylin

OLAP Engine for Big Data

Impala logo Impala

Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.
  • Apache Kylin Landing page
    Landing page //
    2023-06-29
  • Impala Landing page
    Landing page //
    2023-04-02

Apache Kylin features and specs

  • High Query Performance
    Apache Kylin is designed for high-performance, low-latency analytics on large datasets. Its OLAP engine pre-computes and stores aggregated queries, which speeds up query responses significantly.
  • Scalability
    Kylin can handle massive volumes of data, making it suitable for large scale data warehousing needs. It is designed to scale out by distributing the workload across a cluster of servers.
  • Integration with Hadoop Ecosystem
    Kylin integrates seamlessly with the Hadoop ecosystem, leveraging tools like Hive, HBase, and Spark to facilitate data processing and storage, thereby enhancing its functionality and compatibility.
  • Support for Multi-dimensional Analysis
    It provides strong multidimensional analysis capabilities, allowing for complex queries using well-known BI tools like Tableau and Power BI.

Possible disadvantages of Apache Kylin

  • Complex Setup
    Setting up and configuring Apache Kylin can be complex and time-consuming, requiring a deep understanding of the Hadoop ecosystem and its components.
  • Resource Intensity
    The pre-computation of data cubes and their storage can be resource-intensive, consuming significant memory and storage capacity.
  • Limited Flexibility in Querying
    Pre-aggregated cube-based analysis may not cover all ad-hoc queries. Kylin's strength lies in pre-aggregated queries but may fall short in handling highly dynamic, on-the-fly queries.
  • Maintenance Overhead
    Maintaining Kylin’s precomputed cubes can become cumbersome, particularly as data evolves or changes frequently, requiring updates or recalculations of cubes.

Impala features and specs

No features have been listed yet.

Apache Kylin videos

+ Add

Extreme OLAP Analytics with Apache Kylin - Big Data Application Meetup

More videos:

  • Review - Apache Kylin: OLAP Cubes for NoSQL Data stores
  • Review - Installing Apache Kylin in Cloudera Quickstart VM Sandbox

Impala videos

+ Add

2016 Chevrolet Impala - Review and Road Test

More videos:

  • Review - 2020 Chevrolet Impala Review | The Final Year
  • Review - Is it the END of the road for the 2019 Chevy Impala?

Category Popularity

0-100% (relative to Apache Kylin and Impala)

Apache Kylin

Impala

Databases
100 100%
Databases
0% 0
Big Data
53 53%
Big Data
47% 47
Relational Databases
100 100%
Relational Databases
0% 0
Data Management
0 0%
Data Management
100% 100

User comments

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

Based on our record, Apache Kylin seems to be more popular. It has been mentiond 1 time 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 Kylin mentions (1)

  • Apache Kafka Use Cases: When To Use It & When Not To
    A Kafka-based data integration platform will be a good fit here. The services can add events to different topics in a broker whenever there is a data update. Kafka consumers corresponding to each of the services can monitor these topics and make updates to the data in real-time. It is also possible to create a unified data store through the same integration platform. Developers can implement a unified store either... - Source: dev.to / about 3 years ago

Impala mentions (0)

We have not tracked any mentions of Impala yet. Tracking of Impala recommendations started around Mar 2021.

What are some alternatives?

When comparing Apache Kylin and Impala, you can also consider the following products

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

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

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

Microsoft Azure HDInsight - Azure HDInsight is an Apache Hadoop distribution powered by the cloud.

Spring Batch - Level up your Java code and explore what Spring can do for you.

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

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