ELK vs Gravwell

Overview

ELK

Stacks863

Followers941

Votes23

Gravwell

Stacks5

Followers9

Votes11

ELK vs Gravwell: What are the differences?

Introduction:

ELK and Gravwell are both popular tools used for log management and analysis. However, there are key differences between the two that can influence the choice of tool for specific use cases.

Architecture: ELK (Elasticsearch, Logstash, Kibana) stack follows a traditional architecture where logs are collected by Logstash, indexed and stored in Elasticsearch, and visualized in Kibana. On the other hand, Gravwell uses a unique architecture where data is indexed in real-time and stored as text, allowing for ad-hoc searches without the need for an indexing step.
Data Ingestion: ELK requires users to define parsing rules in Logstash for each log source to ensure proper indexing, which can be time-consuming and complex. Gravwell, on the other hand, uses a universal parser that automatically detects and indexes log data without the need for manual configuration, simplifying the data ingestion process.
Query Language: ELK uses the Lucene query language for searching and filtering data, which can be powerful but has a steep learning curve for beginners. Gravwell utilizes a query language inspired by Unix pipelines, making it more intuitive for users familiar with command-line operations to perform complex searches and data analysis.
Scalability: ELK is well-suited for large-scale deployments and can handle high volumes of logs efficiently with horizontal scalability options. Gravwell, while scalable, may require more hardware resources due to its real-time indexing approach, making it potentially less cost-effective in certain scenarios.
Integration: ELK stack is widely supported by various plugins and integrations, making it easy to connect with different systems and tools for extended functionality. Gravwell, while offering integrations with common data sources, may have limited support compared to ELK's extensive ecosystem.
Pricing Model: ELK stack, being open-source, is free to use but may incur costs for additional features, support, and scaling in enterprise environments. Gravwell, on the other hand, follows a subscription-based pricing model that includes all features and support, making it a more straightforward choice for organizations looking for comprehensive log management solutions.

In Summary, ELK and Gravwell differ in architecture, data ingestion process, query language, scalability, integration options, and pricing model, making each tool suitable for specific use cases based on individual requirements.

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CLI (Node.js)

Manual

Detailed Comparison

ELK	Gravwell
It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.	It is the most flexible full-stack analytics platform in the world. We excel at fusing disparate data sources such as firewall logs, end point event logs, network traffic, OT IDS logs, OT process data, threat feed data, etc. to create a central source of knowledge. Created in the IoT age we know modern data insights demand unlimited ingest and analysis capability for cybersecurity, IoT, business analytics, and more. We support a wide range of customers, from energy production, energy delivery, government, finance, and insurance to health and beauty products.
-	Ability for deployment in cloud, on-premises, or in an isolated on-premises network lacking outside network connectivity; Capable of collecting disparate unstructured time-series data sources into a queryable data lake; Enable data scientists to create custom analysis code/tools to be executed as part of a search pipeline or query system; Analysts and data scientists have access to raw entry records for retroactive analysis and application of machine learning that did not exist at the time of collection; Capable of data separation and fine-grained access controls for multi-tenancy; Data collectors or agents are modifiable by the customer to enable processing, filtering, or enrichment before forwarding to the central store; Massive scalability. Over 100 Terabytes a day is no problem. ; Unlimited data ingestion; Unlimited retention; Live Dashboards; Secure and Proprietary; Offline ("Cold") and online ("Hot") replication; Region-aware redundancy; Multi-tenancy Permissions & Unlimited user seats; Binary data support; Configurable data retention and automatic age-out; Distributed web frontends; Unlimited search count
Statistics
Stacks 863	Stacks 5
Followers 941	Followers 9
Votes 23	Votes 11
Pros & Cons
Pros 14 Open source 4 Can run locally 3 Good for startups with monetary limitations 1 External Network Goes Down You Aren't Without Logging 1 Easy to setup Cons 5 Elastic Search is a resource hog 3 Logstash configuration is a pain 1 Bad for startups with personal limitations	Pros 1 Ingest native/raw data and query later 1 Highly scalable and performant 1 No storage-based pricing 1 Multi-tenancy 1 Rapid deployment Cons 1 Query language is a lot to learn

What are some alternatives to ELK, Gravwell?

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.

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.

Sumo Logic

Cloud-based machine data analytics platform that enables companies to proactively identify availability and performance issues in their infrastructure, improve their security posture and enhance application rollouts. Companies using Sumo Logic reduce their mean-time-to-resolution by 50% and can save hundreds of thousands of dollars, annually. Customers include Netflix, Medallia, Orange, and GoGo Inflight.

Splunk

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

Related Comparisons

ELK vs Gravwell: What are the differences?

Introduction:

ELK and Gravwell are both popular tools used for log management and analysis. However, there are key differences between the two that can influence the choice of tool for specific use cases.

Architecture: ELK (Elasticsearch, Logstash, Kibana) stack follows a traditional architecture where logs are collected by Logstash, indexed and stored in Elasticsearch, and visualized in Kibana. On the other hand, Gravwell uses a unique architecture where data is indexed in real-time and stored as text, allowing for ad-hoc searches without the need for an indexing step.
Data Ingestion: ELK requires users to define parsing rules in Logstash for each log source to ensure proper indexing, which can be time-consuming and complex. Gravwell, on the other hand, uses a universal parser that automatically detects and indexes log data without the need for manual configuration, simplifying the data ingestion process.
Query Language: ELK uses the Lucene query language for searching and filtering data, which can be powerful but has a steep learning curve for beginners. Gravwell utilizes a query language inspired by Unix pipelines, making it more intuitive for users familiar with command-line operations to perform complex searches and data analysis.
Scalability: ELK is well-suited for large-scale deployments and can handle high volumes of logs efficiently with horizontal scalability options. Gravwell, while scalable, may require more hardware resources due to its real-time indexing approach, making it potentially less cost-effective in certain scenarios.
Integration: ELK stack is widely supported by various plugins and integrations, making it easy to connect with different systems and tools for extended functionality. Gravwell, while offering integrations with common data sources, may have limited support compared to ELK's extensive ecosystem.
Pricing Model: ELK stack, being open-source, is free to use but may incur costs for additional features, support, and scaling in enterprise environments. Gravwell, on the other hand, follows a subscription-based pricing model that includes all features and support, making it a more straightforward choice for organizations looking for comprehensive log management solutions.

ELK vs Gravwell

Overview