Devo vs Splunk

Overview

Splunk

Stacks772

Followers1.0K

Votes20

Devo

Stacks11

Followers19

Votes0

Devo vs Splunk: What are the differences?

1. **Pricing Model**: Devo offers a consumption-based pricing model, where customers only pay for the volume of data ingested and analyzed. On the other hand, Splunk follows a traditional licensing model that is based on the amount of data indexed. 2. **Search Functionality**: Devo provides faster search capabilities due to its high-performance architecture that is optimized for real-time analysis. Splunk, although powerful, may experience delays in processing large volumes of data in real-time. 3. **Data Ingestion**: Devo supports ingestion from a variety of sources, including logs, metrics, traces, and more, offering a comprehensive data collection solution. Splunk primarily focuses on log data ingestion, with limited support for other data types. 4. **Machine Learning Integration**: Devo has native integration with machine learning algorithms, enabling users to automate data analysis and gain insights through predictive analytics. Splunk requires additional setup and configuration to integrate machine learning capabilities. 5. **User Interface**: Devo's user interface is designed to be user-friendly and intuitive, making it easier for non-technical users to navigate and analyze data. Splunk's interface may have a steeper learning curve due to its complexity and depth of functionality. 6. **Scale and Performance**: Devo is built for high scalability and performance, able to handle petabytes of data and provide real-time analytics at scale. Splunk may face limitations in handling extremely large datasets efficiently.

In Summary, Devo offers a consumption-based pricing model, faster search capabilities, broader data ingestion support, native machine learning integration, user-friendly interface, and high scalability compared to Splunk.

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Detailed Comparison

Splunk	Devo
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.	It delivers real-time operational and business value from analytics on streaming and historical data to operations, IT, security and business teams at the world’s largest organizations.
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	Scalable data analysis tool; Fast data ingestion; Fast real-time queries on Big Data; IT Operations; Security Operations; Log Management; IoT Analytics; Business Analytics
Statistics
Stacks 772	Stacks 11
Followers 1.0K	Followers 19
Votes 20	Votes 0
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	No community feedback yet
Integrations
No integrations available	Amazon WorkSpaces Perl Wildfly Lua Nagios Oracle Linux Microsoft Azure MongoDB Python

What are some alternatives to Splunk, Devo?

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.

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 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.