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Apache Kudu

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15
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Apache Kudu vs HBase: What are the differences?

Introduction: Apache Kudu and HBase are two popular distributed storage systems used for real-time big data processing.

1. **Data Storage**: Apache Kudu stores data in columns, similar to a traditional RDBMS, allowing for fast analytical queries and efficient storage utilization. On the other hand, HBase stores data in rows, making it suitable for real-time read and write operations.

2. **Consistency Model**: Apache Kudu offers strong consistency guarantees, ensuring that all clients see the same data at any given time. In contrast, HBase provides eventual consistency, which may result in temporary inconsistencies across data replicas.

3. **Data Update Support**: Apache Kudu supports in-place updates and deletes, allowing for efficient updates without the need to rewrite entire rows of data. HBase, however, is optimized for sequential write operations and does not offer native support for in-place updates.

4. **Scan Performance**: Apache Kudu offers superior scan performance due to its columnar storage format and ability to push down predicates to minimize data access. HBase, on the other hand, may experience performance issues with large scans due to its row-oriented storage model.

5. **Use Cases**: Apache Kudu is well-suited for use cases requiring real-time analytics and interactive querying, thanks to its low latency and high throughput capabilities. HBase, on the other hand, is commonly used for scalable, distributed storage of sparse data sets with low-latency access requirements.

6. **Integration with Ecosystem Tools**: Apache Kudu seamlessly integrates with Apache Impala for real-time analytics and Apache Spark for data processing, making it suitable for modern data pipelines. HBase, on the other hand, is often integrated with Apache Hadoop ecosystem tools such as Apache Hive and Apache Pig for batch processing tasks.

In Summary, Apache Kudu and HBase differ in their data storage models, consistency guarantees, support for updates, performance characteristics, use cases, and integration with ecosystem tools.
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    Realtime Analytics
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Cons of Apache Kudu
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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 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.

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    Blog Posts

    Jun 24 2020 at 4:42PM

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    What are some alternatives to Apache Kudu and HBase?
    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.
    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.
    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.
    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