Apache Spark vs Hue

Overview

Hue

Stacks55

Followers98

Votes0

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Hue: What are the differences?

Apache Spark vs Hue

Introduction:

Apache Spark and Hue are both popular tools used in big data processing tasks. However, they have significant differences in terms of their functionality and purpose. Below are the key differences between Apache Spark and Hue:

Data Processing Paradigm: Apache Spark is a distributed computing system that focuses on data processing tasks, such as data manipulation, analytics, and machine learning. It provides a programming interface that allows users to write complex data processing workflows. In contrast, Hue is a web-based interface that simplifies the use and management of Apache Hadoop and related big data technologies. It provides a graphical user interface for various Hadoop ecosystem applications, including Spark, Hive, and Impala.
Ease of Use: Despite its powerful capabilities, Apache Spark requires users to have programming knowledge and skills to write Spark applications. It provides APIs in various programming languages, such as Scala, Java, and Python. On the other hand, Hue offers a user-friendly web-based interface that allows users to perform various big data tasks without writing code. It provides a point-and-click interface for data exploration, query execution, and job scheduling.
Scope of Tasks: Apache Spark is designed for processing large-scale datasets and performing complex analytics tasks. It can handle a wide range of data processing tasks, including batch processing, real-time streaming, and iterative algorithms. In contrast, Hue is primarily focused on providing an easy-to-use interface for querying and analyzing data stored in Hadoop. It allows users to write SQL queries, create visualizations, and manage workflows within the Hadoop ecosystem.
Integration with Hadoop Ecosystem: Apache Spark is a part of the Hadoop ecosystem and can seamlessly integrate with other Hadoop components, such as HDFS, YARN, and Hive. It can leverage the distributed storage and processing capabilities provided by Hadoop. Hue, on the other hand, serves as a comprehensive web-based interface for managing and accessing various Hadoop ecosystem components. It provides integration with popular technologies like Spark, Hive, Impala, and HBase.
Cluster Management: Apache Spark includes built-in cluster management capabilities through its standalone mode, YARN, or Apache Mesos. It allows users to easily scale their Spark applications to run on a cluster of machines. Hue, on the other hand, focuses on providing a centralized interface for managing and monitoring Hadoop clusters. It allows users to view and manage cluster resources, monitor job progress, and configure cluster settings.
Use Case Scenarios: Apache Spark is commonly used in scenarios where there is a need for large-scale data processing, advanced analytics, and machine learning tasks. It is suitable for industries such as finance, healthcare, and e-commerce, which deal with vast amounts of data. On the other hand, Hue is often used in scenarios where the focus is on data exploration, ad hoc querying, and data visualization. It is popular in data analysis teams and organizations that require user-friendly tools for interacting with Hadoop.

In summary, Apache Spark is a distributed computing system that focuses on large-scale data processing, analytics, and machine learning, while Hue provides a user-friendly interface for managing and accessing various Hadoop ecosystem components, including Spark.

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Advice on Hue, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Hue	Apache Spark
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.	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.
-	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars -	GitHub Stars 42.2K
GitHub Forks -	GitHub Forks 28.9K
Stacks 55	Stacks 3.1K
Followers 98	Followers 3.5K
Votes 0	Votes 140
Pros & Cons
No community feedback yet	Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Hue, Apache Spark?

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.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs Hue: What are the differences?

Apache Spark vs Hue

Introduction:

Data Processing Paradigm: Apache Spark is a distributed computing system that focuses on data processing tasks, such as data manipulation, analytics, and machine learning. It provides a programming interface that allows users to write complex data processing workflows. In contrast, Hue is a web-based interface that simplifies the use and management of Apache Hadoop and related big data technologies. It provides a graphical user interface for various Hadoop ecosystem applications, including Spark, Hive, and Impala.
Ease of Use: Despite its powerful capabilities, Apache Spark requires users to have programming knowledge and skills to write Spark applications. It provides APIs in various programming languages, such as Scala, Java, and Python. On the other hand, Hue offers a user-friendly web-based interface that allows users to perform various big data tasks without writing code. It provides a point-and-click interface for data exploration, query execution, and job scheduling.
Scope of Tasks: Apache Spark is designed for processing large-scale datasets and performing complex analytics tasks. It can handle a wide range of data processing tasks, including batch processing, real-time streaming, and iterative algorithms. In contrast, Hue is primarily focused on providing an easy-to-use interface for querying and analyzing data stored in Hadoop. It allows users to write SQL queries, create visualizations, and manage workflows within the Hadoop ecosystem.
Integration with Hadoop Ecosystem: Apache Spark is a part of the Hadoop ecosystem and can seamlessly integrate with other Hadoop components, such as HDFS, YARN, and Hive. It can leverage the distributed storage and processing capabilities provided by Hadoop. Hue, on the other hand, serves as a comprehensive web-based interface for managing and accessing various Hadoop ecosystem components. It provides integration with popular technologies like Spark, Hive, Impala, and HBase.
Cluster Management: Apache Spark includes built-in cluster management capabilities through its standalone mode, YARN, or Apache Mesos. It allows users to easily scale their Spark applications to run on a cluster of machines. Hue, on the other hand, focuses on providing a centralized interface for managing and monitoring Hadoop clusters. It allows users to view and manage cluster resources, monitor job progress, and configure cluster settings.
Use Case Scenarios: Apache Spark is commonly used in scenarios where there is a need for large-scale data processing, advanced analytics, and machine learning tasks. It is suitable for industries such as finance, healthcare, and e-commerce, which deal with vast amounts of data. On the other hand, Hue is often used in scenarios where the focus is on data exploration, ad hoc querying, and data visualization. It is popular in data analysis teams and organizations that require user-friendly tools for interacting with Hadoop.

Apache Spark vs Hue

Overview

Apache Spark vs Hue: What are the differences?