Apache Kudu
Apache Kudu

46
123
+ 1
8
Druid
Druid

206
391
+ 1
20
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Druid vs Apache Kudu: What are the differences?

Druid: Fast column-oriented distributed data store. 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; 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.

Druid and Apache Kudu can be categorized as "Big Data" tools.

"Real Time Aggregations" is the primary reason why developers consider Druid over the competitors, whereas "Realtime Analytics" was stated as the key factor in picking Apache Kudu.

Druid and Apache Kudu are both open source tools. It seems that Druid with 8.51K GitHub stars and 2.14K forks on GitHub has more adoption than Apache Kudu with 801 GitHub stars and 268 GitHub forks.

Airbnb, Instacart, and Dial Once are some of the popular companies that use Druid, whereas Apache Kudu is used by Sensel Telematics, HelloFresh, and Kaspersky Lab. Druid has a broader approval, being mentioned in 28 company stacks & 69 developers stacks; compared to Apache Kudu, which is listed in 5 company stacks and 21 developer stacks.

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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 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.
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What tools integrate with Apache Kudu?
What tools integrate with Druid?
What are some alternatives to Apache Kudu and Druid?
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.
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