Apache Flink vs Hue

Overview

Hue

Stacks55

Followers98

Votes0

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Hue: What are the differences?

Introduction: Apache Flink and Hue are two popular tools in the big data processing and analytics landscape. While both tools offer capabilities for data processing and management, there are key differences between them that cater to different use cases and requirements.

1. Programming Paradigm: One key difference between Apache Flink and Hue is the programming paradigm they support. Apache Flink is designed for stream processing and supports complex event processing with support for high-throughput and low-latency data processing. On the other hand, Hue is more focused on providing a user-friendly interface for managing Hadoop clusters and executing queries in Hive, Impala, and other Hadoop ecosystem tools.

2. Processing Model: Apache Flink employs a dataflow processing model, which enables efficient parallel processing of data streams with fault tolerance and high throughput. Conversely, Hue facilitates batch processing primarily and provides an interface for running queries and managing large datasets on Hadoop clusters.

3. Real-time Processing: Apache Flink excels in real-time processing scenarios by offering low-latency data processing capabilities and support for event time processing. In contrast, Hue is more suited for batch processing tasks where the focus is on executing queries or jobs on Hadoop clusters.

4. Data Visualization: While both Apache Flink and Hue offer some level of data visualization, Hue provides a more interactive and user-friendly interface for visualizing data through charts and graphs. Apache Flink, on the other hand, is more focused on data processing and analysis rather than visualization capabilities.

5. Job Monitoring and Management: Apache Flink provides robust job monitoring and management features for tracking the progress of data processing tasks, managing checkpoints, and handling failures effectively. In comparison, Hue offers a centralized platform for managing Hadoop clusters, executing queries, and accessing various data sources through a single interface.

6. Integration with Ecosystem: Apache Flink integrates well with various data sources and sinks, supporting connectors for popular systems like Kafka, HDFS, and Elasticsearch. Meanwhile, Hue is tightly integrated with the Hadoop ecosystem, providing seamless access to HDFS, Hive, Impala, and other components within the Hadoop ecosystem.

Summary: In summary, Apache Flink excels in real-time stream processing with a dataflow model and low-latency capabilities, while Hue focuses on providing a user-friendly interface for managing Hadoop clusters and executing queries primarily in batch processing scenarios.

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Advice on Hue, Apache Flink

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 Flink
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.	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.
-	Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars -	GitHub Stars 25.4K
GitHub Forks -	GitHub Forks 13.7K
Stacks 55	Stacks 534
Followers 98	Followers 879
Votes 0	Votes 38
Pros & Cons
No community feedback yet	Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency
Integrations
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Hue, Apache Flink?

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.

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.

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Apache Flink vs Hue: What are the differences?

Apache Flink vs Hue

Overview