Apache Flink vs Splunk

Overview

Splunk

Stacks772

Followers1.0K

Votes20

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Splunk: What are the differences?

Apache Flink vs Splunk

Apache Flink and Splunk are both powerful tools used for processing and analyzing data, but they have key differences that set them apart. Here are the main differences between Apache Flink and Splunk:

Architecture: Apache Flink is a distributed stream processing framework that focuses on real-time data processing and event-driven applications. It is designed to handle large streams of data in a fault-tolerant and highly available manner. On the other hand, Splunk is a software platform that specializes in collecting, indexing, and analyzing machine-generated big data. It provides a centralized and scalable solution for search, monitoring, and data visualization.
Data Processing Model: Apache Flink follows a stream processing model, where data is processed as it arrives, enabling low-latency, continuous, and real-time analytics. It supports stateful processing, allowing users to maintain and update state while processing the data streams. Splunk, on the other hand, uses a batch processing model, where data is indexed and stored before being queried and analyzed. It is well-suited for log management and retrospective analysis of historical data.
Programming Languages: Apache Flink provides support for multiple programming languages, including Java, Scala, and Python. It offers a rich set of APIs and libraries that enable developers to build complex streaming applications with ease. Splunk, on the other hand, uses its own proprietary search processing language called SPL, which is specifically designed for handling machine-generated data. It also provides integration with other programming languages through its SDKs.
Scalability and Flexibility: Apache Flink is built to scale horizontally, allowing users to leverage the power of distributed computing by running Flink jobs on multiple machines. It provides automatic fault tolerance and efficient resource management, which makes it suitable for large-scale data processing. Splunk, on the other hand, is known for its scalability and flexibility in handling big data. It can handle large volumes of data from various sources and supports distributed search to improve performance.
Use Cases: Apache Flink is commonly used in scenarios where real-time analytics and event-driven processing are required, such as fraud detection, clickstream analysis, and IoT data processing. It excels in handling continuous streams of data and provides low-latency processing capabilities. Splunk, on the other hand, is often used for log management, security information and event management (SIEM), and IT operations analytics. It helps organizations gain insights from machine-generated data and enables proactive monitoring and troubleshooting.
Ecosystem and Community: Apache Flink has a thriving open-source community and a rich ecosystem of connectors, libraries, and tools that support various use cases. It integrates well with other Apache projects like Kafka, Hadoop, and Spark, allowing users to build end-to-end data processing pipelines. Splunk, on the other hand, has a proprietary ecosystem with its own marketplace for apps and add-ons. It provides a wide range of integrations with popular enterprise systems and offers a comprehensive set of features specifically designed for log analysis and monitoring.

In summary, Apache Flink is a distributed stream processing framework that focuses on real-time data processing, while Splunk is a software platform for collecting, indexing, and analyzing machine-generated big data. Flink is known for its low-latency, continuous processing capabilities, while Splunk excels in log management and retrospective analysis.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Splunk, 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.

576k views576k

Comments

Detailed Comparison

Splunk	Apache Flink
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.	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.
Predict and prevent problems with one unified monitoring experience; Streamline your entire security stack with Splunk as the nerve center; Detect, investigate and diagnose problems easily with end-to-end observability	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 772	Stacks 534
Followers 1.0K	Followers 879
Votes 20	Votes 38
Pros & Cons
Pros 3 Alert system based on custom query results 3 API for searching logs, running reports 2 Splunk language supports string, date manip, math, etc 2 Query engine supports joining, aggregation, stats, etc 2 Dashboarding on any log contents Cons 1 Splunk query language rich so lots to learn	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 Splunk, Apache Flink?

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

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.

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

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.

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

Related Comparisons

Apache Flink vs Splunk: What are the differences?

Apache Flink vs Splunk

Architecture: Apache Flink is a distributed stream processing framework that focuses on real-time data processing and event-driven applications. It is designed to handle large streams of data in a fault-tolerant and highly available manner. On the other hand, Splunk is a software platform that specializes in collecting, indexing, and analyzing machine-generated big data. It provides a centralized and scalable solution for search, monitoring, and data visualization.
Data Processing Model: Apache Flink follows a stream processing model, where data is processed as it arrives, enabling low-latency, continuous, and real-time analytics. It supports stateful processing, allowing users to maintain and update state while processing the data streams. Splunk, on the other hand, uses a batch processing model, where data is indexed and stored before being queried and analyzed. It is well-suited for log management and retrospective analysis of historical data.
Programming Languages: Apache Flink provides support for multiple programming languages, including Java, Scala, and Python. It offers a rich set of APIs and libraries that enable developers to build complex streaming applications with ease. Splunk, on the other hand, uses its own proprietary search processing language called SPL, which is specifically designed for handling machine-generated data. It also provides integration with other programming languages through its SDKs.
Scalability and Flexibility: Apache Flink is built to scale horizontally, allowing users to leverage the power of distributed computing by running Flink jobs on multiple machines. It provides automatic fault tolerance and efficient resource management, which makes it suitable for large-scale data processing. Splunk, on the other hand, is known for its scalability and flexibility in handling big data. It can handle large volumes of data from various sources and supports distributed search to improve performance.
Use Cases: Apache Flink is commonly used in scenarios where real-time analytics and event-driven processing are required, such as fraud detection, clickstream analysis, and IoT data processing. It excels in handling continuous streams of data and provides low-latency processing capabilities. Splunk, on the other hand, is often used for log management, security information and event management (SIEM), and IT operations analytics. It helps organizations gain insights from machine-generated data and enables proactive monitoring and troubleshooting.
Ecosystem and Community: Apache Flink has a thriving open-source community and a rich ecosystem of connectors, libraries, and tools that support various use cases. It integrates well with other Apache projects like Kafka, Hadoop, and Spark, allowing users to build end-to-end data processing pipelines. Splunk, on the other hand, has a proprietary ecosystem with its own marketplace for apps and add-ons. It provides a wide range of integrations with popular enterprise systems and offers a comprehensive set of features specifically designed for log analysis and monitoring.

Apache Flink vs Splunk

Overview

Apache Flink vs Splunk: What are the differences?