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  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Gravwell vs Splunk

Gravwell vs Splunk

OverviewComparisonAlternatives

Overview

Splunk
Splunk
Stacks773
Followers1.0K
Votes20
Gravwell
Gravwell
Stacks5
Followers9
Votes11

Gravwell vs Splunk: What are the differences?

Introduction

When deciding between Gravwell and Splunk for log management and analysis, it's essential to understand the key differences between the two platforms to make an informed decision.

  1. Data Ingestion and Flexibility: Gravwell offers more flexibility in data ingestion methods compared to Splunk. With Gravwell, users can ingest data from various sources, including logs, metrics, telemetry data, network data, and custom data types. Additionally, Gravwell supports direct ingestion from different protocols like Syslog, JSON, TCP, UDP, and more, giving users greater control over their data sources.

  2. Pricing Model: One significant difference between Gravwell and Splunk is in their pricing models. Gravwell offers a more transparent and cost-effective pricing structure based on data ingestion volume, making it a preferred choice for organizations with budget constraints. In contrast, Splunk's pricing is often criticized for being complex and expensive, especially as data volume increases.

  3. Search and Query Capabilities: Splunk is known for its powerful search and query capabilities, offering a user-friendly search language and a vast array of functions. While Gravwell also provides robust search and query features, users may find Splunk's interface and syntax more intuitive for complex searches and data analysis tasks.

  4. Scalability and Performance: Splunk is widely recognized for its scalability and performance in handling large volumes of data and concurrent users. Businesses dealing with massive datasets or requiring real-time data analysis may prefer Splunk for its proven scalability. On the other hand, Gravwell also offers good scalability options, but it may require more configuration and optimization for handling extreme workloads compared to Splunk.

  5. Community and Support: Splunk boasts a large and active user community, providing ample resources, forums, and knowledge sharing opportunities for users. The extensive community support often makes it easier to troubleshoot issues, find solutions, and leverage best practices in Splunk deployments. While Gravwell also has a growing user base, the community support and resources may not be as extensive as Splunk's, potentially impacting the overall user experience and troubleshooting process for less common issues.

  6. Deployment Options: Gravwell and Splunk offer different deployment options to suit varying organizational needs. Splunk provides both on-premises and cloud-based deployment choices, allowing users to select based on their preference for data security, compliance, and infrastructure requirements. In contrast, Gravwell primarily focuses on on-premises deployments, which may limit the flexibility for organizations looking for cloud-first or hybrid cloud solutions.

In Summary, when comparing Gravwell and Splunk, key differences exist in data ingestion flexibility, pricing models, search capabilities, scalability, community support, and deployment options, influencing the suitability for different organizational requirements.

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Detailed Comparison

Splunk
Splunk
Gravwell
Gravwell

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

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.

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
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
773
Stacks
5
Followers
1.0K
Followers
9
Votes
20
Votes
11
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
    Custom log parsing as well as automatic parsing
Cons
  • 1
    Splunk query language rich so lots to learn
Pros
  • 1
    Ingest native/raw data and query later
  • 1
    Highly scalable and performant
  • 1
    Indexing on writes
  • 1
    No storage-based pricing
  • 1
    Multi-tenancy
Cons
  • 1
    Query language is a lot to learn

What are some alternatives to Splunk, Gravwell?

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.

Sematext

Sematext

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

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