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  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Google Cloud Data Fusion vs Splunk

Google Cloud Data Fusion vs Splunk

OverviewComparisonAlternatives

Overview

Splunk
Splunk
Stacks772
Followers1.0K
Votes20
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

Google Cloud Data Fusion vs Splunk: What are the differences?

# Introduction
In this analysis, we will highlight the key differences between Google Cloud Data Fusion and Splunk.

1. **Deployment and Scalability**: Google Cloud Data Fusion is a managed service that is fully integrated with other Google Cloud services, allowing for easy deployment and scalability. Splunk, on the other hand, is more focused on log management and analysis, requiring additional configuration and setup for deployment and scalability.

2. **Data Transformation and Integration**: Google Cloud Data Fusion provides a visual interface for creating data pipelines and integrating multiple data sources seamlessly. Splunk, while capable of handling large amounts of data, requires more manual configuration for data transformation and integration.

3. **Cost Structure**: Google Cloud Data Fusion follows a pay-as-you-go pricing model based on usage, providing cost efficiency for small to medium-sized businesses. Splunk, however, has a more complex pricing structure that can be costly for organizations with extensive data processing needs.

4. **Machine Learning and AI Capabilities**: Google Cloud Data Fusion offers built-in support for machine learning and AI tasks, enabling users to leverage advanced analytics capabilities. Splunk provides machine learning features through add-ons, requiring additional setup and integration for AI capabilities.

5. **Monitoring and Alerting**: Google Cloud Data Fusion includes monitoring and alerting features as part of the platform, simplifying the process of tracking data pipelines and detecting issues. Splunk offers robust monitoring and alerting capabilities but may require additional configuration for integration with existing systems.

6. **Community Support and Ecosystem**: Google Cloud Data Fusion benefits from Google's extensive cloud ecosystem and community support, providing users with resources for troubleshooting and development. Splunk also has a strong user community but may have a narrower focus on log management and analysis tools.

# In Summary, Google Cloud Data Fusion offers a more integrated and scalable solution for data transformation and analysis, while Splunk is more focused on log management with advanced monitoring and alerting capabilities.

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Detailed Comparison

Splunk
Splunk
Google Cloud Data Fusion
Google Cloud Data Fusion

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

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

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
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
Stacks
772
Stacks
25
Followers
1.0K
Followers
156
Votes
20
Votes
1
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
    Custom log parsing as well as automatic parsing
  • 2
    Dashboarding on any log contents
Cons
  • 1
    Splunk query language rich so lots to learn
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
No integrations available
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to Splunk, Google Cloud Data Fusion?

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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