Apache Spark vs Splunk

Overview

Splunk

Stacks772

Followers1.0K

Votes20

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Splunk: What are the differences?

Apache Spark vs Splunk

Apache Spark and Splunk are two popular big data processing platforms used for analyzing and processing large volumes of data. While both platforms have similar capabilities in terms of data processing and analytics, there are key differences that set them apart.

Data Processing Model: Apache Spark is a distributed computing platform that operates on an in-memory data processing model. It allows users to process large datasets in parallel across a cluster of machines, resulting in faster and more efficient data processing. On the other hand, Splunk follows a log-based data processing model, which means it ingests data logs generated by various sources and indexes them for search and analysis.
Data Source Compatibility: Apache Spark supports a wide range of data sources, including structured, semi-structured, and unstructured data from various file formats and databases. It can handle data in real-time streaming and batch mode. Splunk, on the other hand, specializes in ingesting and analyzing log data from applications, systems, and network devices. It provides out-of-the-box support for a wide range of log formats and protocols.
Query Language: Apache Spark provides a unified programming model and supports multiple programming languages, including Scala, Java, Python, and R. It also offers a rich set of high-level APIs for data manipulation and analysis. Splunk, on the other hand, uses its proprietary search language called SPL (Splunk Processing Language) for querying and analyzing data. SPL provides powerful search capabilities and allows users to extract valuable insights from log data.
Scalability and Performance: Apache Spark offers excellent scalability and performance by performing data processing operations in parallel across a cluster of machines. It can handle large-scale data processing tasks efficiently and provides fault tolerance mechanisms for handling failures. Splunk, on the other hand, is designed to handle high volumes of log data and provides scalability through distributed indexing and search capabilities. It is optimized for analyzing log data in real-time.
Data Visualization and Reporting: Apache Spark provides various libraries and tools for data visualization, including integration with popular visualization libraries like Matplotlib and D3.js. It also supports interactive data exploration and provides visualization capabilities within its notebooks. Splunk, on the other hand, offers powerful data visualization and reporting features out-of-the-box. It provides customizable dashboards, charts, and graphs to visualize and analyze log data effectively.
Deployment and Management: Apache Spark can be deployed in various environments, including on-premises data centers and cloud platforms. It provides a flexible cluster manager that allows users to deploy and manage Spark clusters efficiently. Splunk, on the other hand, provides a centralized management platform for deploying and configuring Splunk instances across an organization. It offers granular control over user access and permissions and provides extensive monitoring and reporting capabilities.

In summary, Apache Spark and Splunk differ in their data processing models, data source compatibility, query languages, scalability and performance characteristics, data visualization and reporting capabilities, and deployment and management options.

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Advice on Splunk, 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

Splunk	Apache Spark
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.	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.
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	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 772	Stacks 3.1K
Followers 1.0K	Followers 3.5K
Votes 20	Votes 140
Pros & Cons
Pros 3 Alert system based on custom query results 3 API for searching logs, running reports 2 Ability to style search results into reports 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 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Splunk, Apache Spark?

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.

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.

Fluentd

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure.

Related Comparisons

Apache Spark vs Splunk: What are the differences?

Apache Spark vs Splunk

Data Processing Model: Apache Spark is a distributed computing platform that operates on an in-memory data processing model. It allows users to process large datasets in parallel across a cluster of machines, resulting in faster and more efficient data processing. On the other hand, Splunk follows a log-based data processing model, which means it ingests data logs generated by various sources and indexes them for search and analysis.
Data Source Compatibility: Apache Spark supports a wide range of data sources, including structured, semi-structured, and unstructured data from various file formats and databases. It can handle data in real-time streaming and batch mode. Splunk, on the other hand, specializes in ingesting and analyzing log data from applications, systems, and network devices. It provides out-of-the-box support for a wide range of log formats and protocols.
Query Language: Apache Spark provides a unified programming model and supports multiple programming languages, including Scala, Java, Python, and R. It also offers a rich set of high-level APIs for data manipulation and analysis. Splunk, on the other hand, uses its proprietary search language called SPL (Splunk Processing Language) for querying and analyzing data. SPL provides powerful search capabilities and allows users to extract valuable insights from log data.
Scalability and Performance: Apache Spark offers excellent scalability and performance by performing data processing operations in parallel across a cluster of machines. It can handle large-scale data processing tasks efficiently and provides fault tolerance mechanisms for handling failures. Splunk, on the other hand, is designed to handle high volumes of log data and provides scalability through distributed indexing and search capabilities. It is optimized for analyzing log data in real-time.
Data Visualization and Reporting: Apache Spark provides various libraries and tools for data visualization, including integration with popular visualization libraries like Matplotlib and D3.js. It also supports interactive data exploration and provides visualization capabilities within its notebooks. Splunk, on the other hand, offers powerful data visualization and reporting features out-of-the-box. It provides customizable dashboards, charts, and graphs to visualize and analyze log data effectively.
Deployment and Management: Apache Spark can be deployed in various environments, including on-premises data centers and cloud platforms. It provides a flexible cluster manager that allows users to deploy and manage Spark clusters efficiently. Splunk, on the other hand, provides a centralized management platform for deploying and configuring Splunk instances across an organization. It offers granular control over user access and permissions and provides extensive monitoring and reporting capabilities.

Apache Spark vs Splunk

Overview

Apache Spark vs Splunk: What are the differences?