Apache Flume vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Flume

Stacks48

Followers120

Votes0

Apache Flume vs Apache Spark: What are the differences?

Introduction

Apache Flume and Apache Spark are both popular tools used in big data processing. While they share some similarities, there are key differences between the two.

Scalability: Apache Flume is designed for high-volume data ingestion and is well-suited for streaming data from various sources. It provides reliable and fault-tolerant data collection, but it lacks advanced processing capabilities. On the other hand, Apache Spark is a general-purpose data processing engine that offers scalability not only for ingestion but also for data transformation and analytics.
Processing Paradigm: Apache Flume follows a pull-based model, where data is collected by agents and pushed to predefined destinations. It focuses on collecting and moving data efficiently. On the contrary, Apache Spark follows a push-based model, where data is processed in-memory using RDD (Resilient Distributed Datasets) or DataFrame APIs. It provides a wide range of data transformations and analytics capabilities.
Real-time Processing: Apache Flume is primarily designed for real-time data ingestion, making it suitable for streaming scenarios. It offers low-latency data collection and supports various sinks like Hadoop Distributed File System (HDFS) and Apache Kafka. In contrast, Apache Spark also supports real-time processing but provides additional batch processing capabilities. It can process both streaming and static data efficiently.
Processing Speed: Apache Flume is optimized for high-speed data collection and delivery, ensuring low-latency data ingestion. It is built to handle data streams in real-time and optimize network bandwidth. On the other hand, Apache Spark's in-memory processing capability enables fast data transformations and analytics. It can process large datasets quickly, thanks to its ability to cache data in memory.
Data Processing Capabilities: Apache Flume is primarily focused on data ingestion and movement, lacking comprehensive data processing capabilities. It provides basic filtering and routing mechanisms but does not offer advanced analytics features like machine learning or graph processing. Apache Spark, on the other hand, provides a wide range of built-in libraries for data manipulation, machine learning, graph processing, and real-time streaming analytics.
Cluster Management: Apache Flume relies on a simple master-slave model for agent coordination, making it suitable for smaller deployments. It can be easily set up and managed. In contrast, Apache Spark comes with built-in cluster management capabilities, allowing it to run on large-scale clusters. It provides fault tolerance, automatic data partitioning, and dynamic allocation of resources.

In summary, Apache Flume is a reliable data ingestion tool with a focus on real-time streaming data, while Apache Spark is a general-purpose data processing engine that offers scalability, advanced analytics, and both real-time and batch processing capabilities.

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Advice on Apache Spark, Apache Flume

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	Apache Flume
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.	It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
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 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 48
Followers 3.5K	Followers 120
Votes 140	Votes 0
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	No community feedback yet

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Related Comparisons

Apache Flume vs Apache Spark: What are the differences?

Introduction

Apache Flume and Apache Spark are both popular tools used in big data processing. While they share some similarities, there are key differences between the two.

Scalability: Apache Flume is designed for high-volume data ingestion and is well-suited for streaming data from various sources. It provides reliable and fault-tolerant data collection, but it lacks advanced processing capabilities. On the other hand, Apache Spark is a general-purpose data processing engine that offers scalability not only for ingestion but also for data transformation and analytics.
Processing Paradigm: Apache Flume follows a pull-based model, where data is collected by agents and pushed to predefined destinations. It focuses on collecting and moving data efficiently. On the contrary, Apache Spark follows a push-based model, where data is processed in-memory using RDD (Resilient Distributed Datasets) or DataFrame APIs. It provides a wide range of data transformations and analytics capabilities.
Real-time Processing: Apache Flume is primarily designed for real-time data ingestion, making it suitable for streaming scenarios. It offers low-latency data collection and supports various sinks like Hadoop Distributed File System (HDFS) and Apache Kafka. In contrast, Apache Spark also supports real-time processing but provides additional batch processing capabilities. It can process both streaming and static data efficiently.
Processing Speed: Apache Flume is optimized for high-speed data collection and delivery, ensuring low-latency data ingestion. It is built to handle data streams in real-time and optimize network bandwidth. On the other hand, Apache Spark's in-memory processing capability enables fast data transformations and analytics. It can process large datasets quickly, thanks to its ability to cache data in memory.
Data Processing Capabilities: Apache Flume is primarily focused on data ingestion and movement, lacking comprehensive data processing capabilities. It provides basic filtering and routing mechanisms but does not offer advanced analytics features like machine learning or graph processing. Apache Spark, on the other hand, provides a wide range of built-in libraries for data manipulation, machine learning, graph processing, and real-time streaming analytics.
Cluster Management: Apache Flume relies on a simple master-slave model for agent coordination, making it suitable for smaller deployments. It can be easily set up and managed. In contrast, Apache Spark comes with built-in cluster management capabilities, allowing it to run on large-scale clusters. It provides fault tolerance, automatic data partitioning, and dynamic allocation of resources.

Apache Flume vs Apache Spark

Overview

Apache Flume vs Apache Spark: What are the differences?