Overview

Hue

Stacks55

Followers98

Votes0

Apache Kudu

Stacks71

Followers259

Votes10

GitHub Stars828

Forks282

Apache Kudu vs Hue: What are the differences?

## Apache Kudu vs. Hue

<Write Introduction here>

1. **Data Storage**: Apache Kudu is a columnar storage manager, while Hue is a web-based interface for analyzing data. Kudu provides efficient storage for read-heavy workloads, while Hue focuses on providing a user-friendly platform for data exploration and visualization.
   
2. **Use Case**: Apache Kudu is commonly used for real-time analytics and interactive analytics scenarios where low latency access to data is crucial. On the other hand, Hue is used as a centralized interface for various components of the Hadoop ecosystem, including HDFS, Hive, Impala, and Spark, making it a versatile tool for data processing and visualization.
   
3. **Architecture**: Apache Kudu follows a master-slave architecture with a distributed consensus algorithm for maintaining data consistency and availability. In contrast, Hue utilizes a client-server architecture to interact with different services and frameworks within the Hadoop ecosystem.
   
4. **Data Formats**: While Apache Kudu stores data in a columnar format optimized for analytical queries, Hue supports various data formats like JSON, CSV, Parquet, and Avro allowing users to work with different types of data structures and files seamlessly.
   
5. **Security**: Apache Kudu offers robust security features such as authentication, authorization, and data encryption to ensure data protection and compliance with security standards. Hue also provides security features like LDAP integration, SSL support, and role-based access control for secure data handling and user management.
   
6. **Community Support**: Apache Kudu has a dedicated open-source community that actively contributes to its development and maintenance, ensuring regular updates and improvements. On the other hand, Hue is supported by the Cloudera community and maintained as part of the Cloudera distribution of Hadoop, benefiting from enterprise-grade support and integration with other Cloudera tools.

In Summary, Apache Kudu and Hue differ in their primary use cases, architecture, data storage mechanisms, security features, and community support, making them suitable for different aspects of data processing and analysis within the Hadoop ecosystem.

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Detailed Comparison

Hue	Apache Kudu
It is open source and lets regular users import their big data, query it, search it, visualize it and build dashboards on top of it, all from their browser.	A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.
Statistics
GitHub Stars -	GitHub Stars 828
GitHub Forks -	GitHub Forks 282
Stacks 55	Stacks 71
Followers 98	Followers 259
Votes 0	Votes 10
Pros & Cons
No community feedback yet	Pros 10 Realtime Analytics Cons 1 Restart time
Integrations
No integrations available	Hadoop

What are some alternatives to Hue, Apache Kudu?

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.