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Apache Kudu
Apache Kudu

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AWS Glue
AWS Glue

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AWS Glue vs Apache Kudu: What are the differences?

Developers describe AWS Glue as "Fully managed extract, transform, and load (ETL) service". A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. On the other hand, Apache Kudu is detailed as "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.

AWS Glue and Apache Kudu can be primarily classified as "Big Data" tools.

Apache Kudu is an open source tool with 801 GitHub stars and 268 GitHub forks. Here's a link to Apache Kudu's open source repository on GitHub.

Auto Trader, Postmates, and SparkPost are some of the popular companies that use AWS Glue, whereas Apache Kudu is used by Sensel Telematics, HelloFresh, and Kaspersky Lab. AWS Glue has a broader approval, being mentioned in 23 company stacks & 23 developers stacks; compared to Apache Kudu, which is listed in 5 company stacks and 21 developer stacks.

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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 AWS Glue?

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
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      What are some alternatives to Apache Kudu and AWS Glue?
      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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