Ambari vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Ambari

Stacks45

Followers74

Votes2

Ambari vs Apache Spark: What are the differences?

Introduction

Apache Ambari and Apache Spark are two popular open-source software used in big data processing and management. While both serve different purposes, they play a significant role in the big data ecosystem. This article aims to highlight the key differences between Ambari and Spark.

Deployment and Management: Ambari is primarily used for cluster management and deployment of Apache Hadoop-based ecosystems. It provides a web-based interface to monitor, provision, and manage Hadoop clusters. On the other hand, Apache Spark focuses on distributed computing and processing large-scale datasets. It offers an easy-to-use API for data manipulation and analysis, but it does not provide cluster management capabilities like Ambari.
Ecosystem Support: Ambari is designed to manage the complete Hadoop ecosystem, including Hadoop Distributed File System (HDFS), Apache Hive, Apache HBase, and more. It provides a unified platform for managing and monitoring various components of the Hadoop stack. Conversely, Apache Spark is a standalone processing engine that can be integrated with different data processing frameworks like Hadoop, Cassandra, and more. It can leverage the functionalities of these ecosystems but does not have built-in management capabilities for them.
Processing Framework: Ambari focuses on managing and monitoring data processing activities across the Hadoop ecosystem. It provides tools for managing data ingestion, batch processing, and stream processing tasks. Apache Spark, on the other hand, is a powerful and fast distributed computing system that specializes in processing large-scale data. It provides a unified processing framework that supports batch processing, interactive queries, machine learning, and real-time streaming.
Language Support: Ambari is primarily used through its web-based user interface, making it language-independent. Users can interact with Ambari using any web browser. On the contrary, Apache Spark supports multiple programming languages, including Java, Scala, Python, and R. This language flexibility allows developers to choose the language they are most comfortable with for writing Spark applications.
Execution Model: Ambari provides a centralized management platform that coordinates the execution of tasks across the Hadoop ecosystem. It ensures that jobs are distributed and executed efficiently across the cluster. Apache Spark, on the other hand, follows a distributed computing model called Resilient Distributed Datasets (RDDs). RDDs enable fault tolerance and parallel processing by dividing the data into smaller partitions and processing them in parallel across a cluster of machines.
Real-time Processing: Apache Spark has built-in support for real-time streaming data processing. It provides a high-level streaming API that allows developers to process and analyze streaming data in near real-time. Ambari, on the other hand, primarily focuses on batch processing and does not have native support for real-time stream processing.

In summary, Ambari is a cluster management tool for Apache Hadoop-based ecosystems, providing deployment, monitoring, and management capabilities. Apache Spark, on the other hand, is a powerful distributed computing engine that supports batch processing, interactive queries, machine learning, and real-time streaming.

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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

Apache Spark	Ambari
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.	This project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. It provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs.
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	Alerts; Ambari Python Libraries; Automated Kerberizaton; Blueprints; Configurations; Service Dashboards; Metrics
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 45
Followers 3.5K	Followers 74
Votes 140	Votes 2
Pros & Cons
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	Pros 2 Ease of use
Integrations
No integrations available	Hadoop Ubuntu Debian

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Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

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Zabbix

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Graphite

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Related Comparisons

Ambari vs Apache Spark: What are the differences?

Introduction

Deployment and Management: Ambari is primarily used for cluster management and deployment of Apache Hadoop-based ecosystems. It provides a web-based interface to monitor, provision, and manage Hadoop clusters. On the other hand, Apache Spark focuses on distributed computing and processing large-scale datasets. It offers an easy-to-use API for data manipulation and analysis, but it does not provide cluster management capabilities like Ambari.
Ecosystem Support: Ambari is designed to manage the complete Hadoop ecosystem, including Hadoop Distributed File System (HDFS), Apache Hive, Apache HBase, and more. It provides a unified platform for managing and monitoring various components of the Hadoop stack. Conversely, Apache Spark is a standalone processing engine that can be integrated with different data processing frameworks like Hadoop, Cassandra, and more. It can leverage the functionalities of these ecosystems but does not have built-in management capabilities for them.
Processing Framework: Ambari focuses on managing and monitoring data processing activities across the Hadoop ecosystem. It provides tools for managing data ingestion, batch processing, and stream processing tasks. Apache Spark, on the other hand, is a powerful and fast distributed computing system that specializes in processing large-scale data. It provides a unified processing framework that supports batch processing, interactive queries, machine learning, and real-time streaming.
Language Support: Ambari is primarily used through its web-based user interface, making it language-independent. Users can interact with Ambari using any web browser. On the contrary, Apache Spark supports multiple programming languages, including Java, Scala, Python, and R. This language flexibility allows developers to choose the language they are most comfortable with for writing Spark applications.
Execution Model: Ambari provides a centralized management platform that coordinates the execution of tasks across the Hadoop ecosystem. It ensures that jobs are distributed and executed efficiently across the cluster. Apache Spark, on the other hand, follows a distributed computing model called Resilient Distributed Datasets (RDDs). RDDs enable fault tolerance and parallel processing by dividing the data into smaller partitions and processing them in parallel across a cluster of machines.
Real-time Processing: Apache Spark has built-in support for real-time streaming data processing. It provides a high-level streaming API that allows developers to process and analyze streaming data in near real-time. Ambari, on the other hand, primarily focuses on batch processing and does not have native support for real-time stream processing.

Ambari vs Apache Spark

Overview

Ambari vs Apache Spark: What are the differences?