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Splunk VS Apache Spark

Compare Splunk VS Apache Spark and see what are their differences

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Splunk logo Splunk

Splunk's operational intelligence platform helps unearth intelligent insights from machine data.

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Splunk Landing page
    Landing page //
    2023-10-20
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Splunk features and specs

  • Powerful Data Analysis
    Splunk provides robust data indexing and search capabilities, allowing users to analyze large volumes of log data with high flexibility and efficiency.
  • Real-Time Processing
    Splunk enables real-time monitoring and analysis of data, which helps in immediate detection of anomalies and operational issues.
  • Scalability
    Splunk can efficiently scale to handle large amounts of data from various sources, making it suitable for both small businesses and large enterprises.
  • Wide Range of Integrations
    Splunk offers extensive integration options with numerous third-party applications and services, enhancing its functionalities and utility.
  • User-Friendly Interface
    Splunk comes with a highly intuitive and user-friendly interface, which makes it easier for users to navigate and leverage its features without extensive training.
  • Advanced Security Features
    Splunk includes comprehensive security features such as access controls, data encryption, and auditing capabilities to ensure data protection and compliance.

Possible disadvantages of Splunk

  • High Cost
    Splunk is considered expensive compared to other data analytics tools, which can be a significant constraint for smaller organizations or those with limited budgets.
  • Complex Licensing
    The licensing model for Splunk can be complex and confusing, often leading to issues in predicting costs and understanding the full extent of required licensing.
  • Steep Learning Curve
    Despite its user-friendly interface, there is a steep learning curve associated with mastering Splunk’s advanced features and query language, which can be challenging for new users.
  • Resource Intensive
    Splunk can be resource-intensive, requiring substantial computational power and storage, which might necessitate additional hardware investments.
  • Custom App Development
    Building custom apps and dashboards in Splunk may require specialized knowledge of its proprietary language, which can limit customization for users without technical expertise.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Splunk videos

+ Add

"Splunk Product Overview"

More videos:

  • Tutorial - Splunk in 60 Minutes | Splunk Tutorial For Beginners | Splunk Training | Splunk Tutorial | Edureka
  • Demo - Splunk Incident Review Demo

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Splunk and Apache Spark)

Splunk

Apache Spark

Monitoring Tools
100 100%
Monitoring Tools
0% 0
Databases
0 0%
Databases
100% 100
Log Management
100 100%
Log Management
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using Splunk and Apache Spark. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Splunk and Apache Spark

Splunk Reviews

The 10 Best Nagios Alternatives in 2024 (Paid and Open-source)
What sets Splunk apart, likely contributing to its premium price, is its expansive network. With over 2,200 partners spanning various industries, Splunk ensures users can fully leverage the platform. Additionally, boasting a community of over 18,000 active members and 1,800 Splunk experts, users have access to support for problem-solving, architecture, deployment, and...
Source: betterstack.com
Top 10 Grafana Alternatives in 2024
Splunk’s security and data monitoring features make it a potent alternative to Grafana. The platform helps collect and store extensive data from multiple platforms like databases, messaging systems, and network devices.
Source: middleware.io
Top 11 Grafana Alternatives & Competitors [2024]
Splunk's strengths lie in its adeptness at handling large-scale data ingestion and robust analytics capabilities. This makes it invaluable for organizations with complex data monitoring needs, particularly in enterprise security and observability.
Source: signoz.io
10 Best Grafana Alternatives [2023 Comparison]
Splunk is a well-known log management solution that’s been around forever. It offers a variety of observability solutions, making it an ideal Grafana alternative in terms of functionality. Splunk offers users Log Management, Synthetic monitoring, Infrastructure Monitoring, APM, Security Monitoring, and more.
Source: sematext.com
Top 11 Best SIEM Tools in 2022 For Real-Time Incident Response and Security
Splunk provides improved security operations like customizable dashboards, asset investigator, statistical analysis, and incident review, classification, and investigation. It has features of alerts management, risk scores, etc. It provides security services to the public sectors, financial services, and healthcare.
Source: www.softwaretestinghelp.com

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Splunk. It has been mentiond 70 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Splunk mentions (19)

  • Ask HN: Who is hiring? (April 2025)
    Splunk (A Cisco Company) [https://splunk.com] | Principal Software Engineer, Backend | Remote (Vancouver / Toronto, Canada) Splunk is here to build a safer and more resilient digital world. The world's leading enterprises use our unified security and observability platform to keep their digital systems secure and reliable. The role I'm hiring for is to lead the effort for a new API that will power Splunk's new... - Source: Hacker News / 2 months ago
  • Can I install apps on Free 60 day Enterprise?
    I'm using the free 60day Enterprise license and tried to install different apps from the "Browse more apps" menu in Splunk Enterprise, but it doesn't accept my credentials when I try to log in. I tried my username and password from splunk.com(which I'm sure it works, because I tried it straight away on the official website). Also I tried using my username and password with which I'm accessing Splunk Enterprise,... Source: over 1 year ago
  • Can someone explain this before I go a little crazy? xD
    I'm noticing a questionable trend in Splunk question/answer structure for these free courses on splunk.com So I go to an exam dump to try and compare to something I have studied thus far. (Prepping for entry level 1002). Source: over 1 year ago
  • Where exactly can I start to learn?
    With your splunk.com username, you can login to Splunk trainings portals as well https://www.splunk.com/en_us/training.html .. There are lots of free trainings available. Enroll yourself, complete them, you will gain more confidence. Source: almost 2 years ago
  • VAST 3.0 released. Open-Source Security Data Pipelines with Kusto-like syntax
    VAST is an open-source SecDataOps project for working with data from open-source security tools. Version 3.0 adds a pipeline syntax similar to splunk, Kusto, PRQL, and Zed. Source: over 2 years ago
View more

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 2 months ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 2 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 3 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 4 months ago
View more

What are some alternatives?

When comparing Splunk and Apache Spark, you can also consider the following products

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.

Hadoop - Open-source software for reliable, scalable, distributed computing

Grafana - Data visualization & Monitoring with support for Graphite, InfluxDB, Prometheus, Elasticsearch and many more databases

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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