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  5. Elasticsearch vs Splunk

Elasticsearch vs Splunk

OverviewDecisionsComparisonAlternatives

Overview

Elasticsearch
Elasticsearch
Stacks35.5K
Followers27.1K
Votes1.6K
Splunk
Splunk
Stacks772
Followers1.0K
Votes20

Elasticsearch vs Splunk: What are the differences?

Introduction

Elasticsearch and Splunk are both popular platforms used for managing and analyzing large volumes of data. However, there are key differences between the two.

  1. Querying and Search Capability: Elasticsearch is a search engine that is optimized for searching, querying, and analyzing structured and unstructured data. It uses inverted indices for fast retrieval of information and supports full-text search. On the other hand, Splunk is a log management and analysis tool that excels at parsing and indexing machine-generated data, making it easier to search and analyze log files and event data.

  2. Data Collection and Indexing: Elasticsearch can index and search data in real-time as it is ingested, making it suitable for use cases that require real-time data analysis. It supports a wide range of data sources and provides flexible indexing capabilities. Splunk, on the other hand, requires data to be indexed before it can be searched and analyzed. It uses an indexing pipeline to parse, extract, and transform data into searchable events.

  3. Scalability and Distributed Architecture: Elasticsearch is designed to be distributed and horizontally scalable, allowing it to handle large volumes of data and high query loads. It can be easily scaled by adding more nodes to the cluster. Splunk, on the other hand, does not have a distributed architecture by default and relies on a single-instance deployment. It does offer distributed search capabilities but requires additional configuration and setup.

  4. Data Visualization and User Interface: Splunk provides a rich set of visualization tools and a user-friendly interface for analyzing and visualizing data. It offers pre-built dashboards, charts, and reports that make it easy to explore and understand data. Elasticsearch, on the other hand, focuses more on providing the underlying search and analytics capabilities. It offers APIs and integrations with other visualization tools like Kibana for data visualization.

  5. Pricing and Licensing: Elasticsearch is open-source and free to use, but it also offers commercial licenses and subscription plans for additional features and support. Splunk, on the other hand, is a commercial product and requires a paid license for enterprise use. Its pricing is typically based on the volume of data ingested and indexed.

  6. Community and Ecosystem: Elasticsearch has a vibrant and active open-source community. It has a wide range of community-contributed plugins and integrations, making it easier to extend and integrate with other systems. Splunk also has a strong community and ecosystem, but it is more focused on its core product offerings.

In summary, Elasticsearch is a powerful search engine optimized for querying and analyzing structured and unstructured data in real-time, while Splunk is a log management and analysis tool that excels at parsing and indexing machine-generated data for easy log file search and analysis. Elasticsearch provides better scalability and distributed architecture, while Splunk offers a more user-friendly interface and visualization capabilities.

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Advice on Elasticsearch, Splunk

Rana Usman
Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceonFirebaseFirebaseElasticsearchElasticsearchAlgoliaAlgolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k
Comments

Detailed Comparison

Elasticsearch
Elasticsearch
Splunk
Splunk

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License
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
Statistics
Stacks
35.5K
Stacks
772
Followers
27.1K
Followers
1.0K
Votes
1.6K
Votes
20
Pros & Cons
Pros
  • 329
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
Cons
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
Pros
  • 3
    API for searching logs, running reports
  • 3
    Alert system based on custom query results
  • 2
    Splunk language supports string, date manip, math, etc
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 2
    Custom log parsing as well as automatic parsing
Cons
  • 1
    Splunk query language rich so lots to learn
Integrations
Kibana
Kibana
Beats
Beats
Logstash
Logstash
No integrations available

What are some alternatives to Elasticsearch, Splunk?

Algolia

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Papertrail

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

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

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

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

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

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

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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.

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