Amundsen vs Apache Kudu

Overview

Apache Kudu

Stacks71

Followers259

Votes10

GitHub Stars828

Forks282

Amundsen

Stacks17

Followers42

Votes0

Apache Kudu vs Amundsen: What are the differences?

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; Amundsen: A metadata driven application for improving the productivity of data analysts, data scientists and engineers. It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

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

Apache Kudu and Amundsen are both open source tools. Amundsen with 889 GitHub stars and 163 forks on GitHub appears to be more popular than Apache Kudu with 815 GitHub stars and 277 GitHub forks.

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Detailed Comparison

Apache Kudu	Amundsen
A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.	It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
-	Datasets (Tables) schema and usage frequency/popularity; Users bookmark, owner, frequent user; Dashboard popularity, lineage to datasets
Statistics
GitHub Stars 828	GitHub Stars -
GitHub Forks 282	GitHub Forks -
Stacks 71	Stacks 17
Followers 259	Followers 42
Votes 10	Votes 0
Pros & Cons
Pros 10 Realtime Analytics Cons 1 Restart time	No community feedback yet
Integrations
Hadoop	Google BigQuery Snowflake AWS Glue Superset Apache Hive

What are some alternatives to Apache Kudu, Amundsen?

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.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

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.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

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.