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Apache Kudu

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Apache Impala

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Apache Impala vs Apache Kudu: What are the differences?

Introduction

Apache Impala and Apache Kudu are both open-source technologies aimed at improving the performance and efficiency of data processing and analytics. While they can be used together to achieve better results, they serve different purposes and have distinct features.

  1. Storage Model: Apache Impala is a massively parallel processing SQL query engine designed for high-performance analytics on structured data. It can process data stored in various formats, including Apache Parquet, Apache Avro, and Apache Kudu. On the other hand, Apache Kudu is specifically built for high-performance storage of structured data, allowing fast analytics and inserts/updates on the same dataset.

  2. Data Updates: One key difference between Apache Impala and Apache Kudu is their approach to data updates. Impala allows read and write operations, but it performs best with read-heavy workloads. On the contrary, Apache Kudu is optimized for fast updates and inserts, making it a suitable choice for write-intensive workloads.

  3. Data Encoding: In terms of data encoding, Impala uses a columnar format called Apache Parquet, which provides efficient compression and encoding techniques for high-performance analytics. Kudu, on the other hand, utilizes a unique update-friendly data format that enables both efficient storage and update operations.

  4. Data Storage: Apache Impala is designed to work with various distributed file systems, including Hadoop Distributed File System (HDFS), Amazon S3, and Hadoop compatible storage systems. On the other hand, Apache Kudu provides its own native storage layer, which offers faster access and performance optimizations specifically tailored for its storage model.

  5. Data Consistency: When it comes to data consistency, Impala relies on the underlying storage system for consistency guarantees. In contrast, Apache Kudu guarantees strong consistency for both reads and writes, as it ensures that all replicas of a row are always updated atomically.

  6. Data Access Patterns: Impala is primarily optimized for analytical queries that involve aggregations, joins, and complex calculations on large volumes of data. Apache Kudu, on the other hand, excels at serving random and point queries due to its unique storage model, making it well-suited for real-time analytics and interactive workloads.

In summary, Apache Impala is a distributed SQL query engine optimized for high-performance analytics on structured data, while Apache Kudu is a columnar storage engine designed for fast writes and serves random access queries efficiently. Their differences lie in storage models, data updates, data encoding, data storage, data consistency, and data access patterns.

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Pros of Apache Kudu
Pros of Apache Impala
  • 10
    Realtime Analytics
  • 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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Cons of Apache Kudu
Cons of Apache Impala
  • 0
    Restart time
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    What is Apache Kudu?

    A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

    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 Kudu and Apache Impala?
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    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.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    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.
    See all alternatives