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Apache Kylin

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Apache Impala

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Apache Kylin vs Impala: What are the differences?

Introduction

Apache Kylin and Impala are both powerful tools used for data analytics in big data environments. While they share some similarities, there are several key differences between them that make them suitable for different use cases.

  1. Data Processing Methodology: Apache Kylin uses a pre-calculated cube model for processing and aggregating data. It employs the concept of multi-dimensional data models, known as OLAP (Online Analytical Processing), which allows for fast query responses. On the other hand, Impala follows an on-demand processing methodology called SQL-on-Hadoop, which enables users to run queries on raw or unindexed data in real-time.

  2. Query Performance: Apache Kylin is specifically designed for low-latency queries and excels in scenarios where users require sub-second query response times. Its pre-calculated cubes and parallel processing capabilities help minimize query latency. In contrast, Impala provides excellent query performance with a high degree of interactivity, making it suitable for ad-hoc queries or exploratory data analysis in a Hadoop ecosystem.

  3. Data Type Support: Apache Kylin is mainly geared towards processing structured data and dimensional models through its cube-based approach. It provides extensive support for working with dimensions, hierarchies, and measures. On the other hand, Impala supports a wide range of data types, including complex types like arrays and maps, making it more versatile for handling both structured and semi-structured data.

  4. Compatibility: Apache Kylin is tightly integrated with Apache Hadoop and Apache Hive, leveraging their infrastructure for storage and data retrieval. It heavily relies on columnar storage format like Apache Parquet or Apache ORC. In contrast, Impala is Apache-licensed open-source software, which means it can work closely with other components in the Hadoop ecosystem but can also be used independently without any dependencies.

  5. Scalability: Apache Kylin provides excellent scalability for high-volume workloads by leveraging distributed computing across multiple nodes. Its pre-aggregated cubes and parallel processing capabilities allow it to handle large datasets efficiently. Impala also offers high scalability with its MPP (Massively Parallel Processing) architecture, allowing it to process large volumes of data in parallel across a cluster of nodes.

  6. Ease of Use: Apache Kylin provides a user-friendly web-based interface called Kylin Console, which simplifies the cube building, monitoring, and querying tasks. It also offers a SQL-like query language with extensions for OLAP operations. Impala, on the other hand, provides a SQL-like interface that is familiar to most data analysts and SQL developers. It seamlessly integrates with SQL development tools and platforms, making it easy to adopt for users already familiar with SQL.

In summary, Apache Kylin and Impala differ in their data processing methodology, query performance, data type support, compatibility, scalability, and ease of use. Choosing between them depends on the specific requirements of the use case, such as real-time querying, data complexity, integration with Hadoop ecosystem components, or familiarity with SQL.

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Pros of Apache Kylin
Pros of Apache Impala
  • 7
    Star schema and snowflake schema support
  • 5
    Seamless BI integration
  • 4
    OLAP on Hadoop
  • 3
    Easy install
  • 3
    Sub-second latency on extreme large dataset
  • 2
    ANSI-SQL
  • 11
    Super fast
  • 1
    Massively Parallel Processing
  • 1
    Load Balancing
  • 1
    Replication
  • 1
    Scalability
  • 1
    Distributed
  • 1
    High Performance
  • 1
    Open Sourse

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What is Apache Kylin?

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

What is Apache Impala?

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

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What are some alternatives to Apache Kylin and Apache Impala?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Presto
Distributed SQL Query Engine for Big Data
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
AtScale
Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
Clickhouse
It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
See all alternatives