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Apache Kudu

58
198
+ 1
9
Apache Drill

62
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+ 1
14
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Apache Kudu vs Apache Drill: What are the differences?

What is Apache Kudu? Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform. 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 Drill? Schema-Free SQL Query Engine for Hadoop and NoSQL. Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.

Apache Kudu can be classified as a tool in the "Big Data Tools" category, while Apache Drill is grouped under "Database Tools".

"Realtime Analytics" is the primary reason why developers consider Apache Kudu over the competitors, whereas "NoSQL and Hadoop" was stated as the key factor in picking Apache Drill.

Apache Kudu is an open source tool with 828 GitHub stars and 282 GitHub forks. Here's a link to Apache Kudu's open source repository on GitHub.

HelloFresh, Kaspersky Lab, and Cedato are some of the popular companies that use Apache Kudu, whereas Apache Drill is used by Compile Inc, Clarisights, and Alpha Vertex. Apache Kudu has a broader approval, being mentioned in 5 company stacks & 51 developers stacks; compared to Apache Drill, which is listed in 3 company stacks and 52 developer stacks.

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Pros of Apache Kudu
Pros of Apache Drill
  • 9
    Realtime Analytics
  • 4
    NoSQL and Hadoop
  • 3
    Free
  • 3
    Lightning speed and simplicity in face of data jungle
  • 1
    Well documented for fast install
  • 1
    Nested Data support
  • 1
    Read Structured and unstructured data
  • 1
    V1.10 released - https://drill.apache.org/

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Cons of Apache Kudu
Cons of Apache Drill
  • 0
    Restart time
    Be the first to leave a con

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    - No public GitHub repository available -

    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 Drill?

    Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.

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    What companies use Apache Kudu?
    What companies use Apache Drill?
    See which teams inside your own company are using Apache Kudu or Apache Drill.
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    What tools integrate with Apache Kudu?
    What tools integrate with Apache Drill?
    What are some alternatives to Apache Kudu and Apache Drill?
    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.
    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.
    See all alternatives