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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.
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.
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.
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.
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.
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.
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.
Pros of Apache Kylin
- Star schema and snowflake schema support7
- Seamless BI integration5
- OLAP on Hadoop4
- Easy install3
- Sub-second latency on extreme large dataset3
- ANSI-SQL2
Pros of Apache Impala
- Super fast11
- Massively Parallel Processing1
- Load Balancing1
- Replication1
- Scalability1
- Distributed1
- High Performance1
- Open Sourse1