Apache Kafka vs. Datadog

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Kafka
Score 8.4 out of 10
N/A
Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.N/A
Datadog
Score 8.4 out of 10
N/A
Datadog is a monitoring service for IT, Dev and Ops teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.
$0
Up to 5 hosts
Pricing
Apache KafkaDatadog
Editions & Modules
No answers on this topic
Free
$0
Up to 5 hosts
Log Management
$1.27
Per Million Log Events
Standard
$15/host
Up to 500 hosts
Infrastructure
$15.00
Per Host Per Month
APM
$31.00
Per Host Per Month
Enterprise
Custom
500+ hosts
Offerings
Pricing Offerings
Apache KafkaDatadog
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Best Alternatives
Apache KafkaDatadog
Small Businesses

No answers on this topic

InfluxDB
InfluxDB
Score 8.5 out of 10
Medium-sized Companies
IBM MQ
IBM MQ
Score 9.0 out of 10
IBM Instana
IBM Instana
Score 8.9 out of 10
Enterprises
IBM MQ
IBM MQ
Score 9.0 out of 10
IBM Instana
IBM Instana
Score 8.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache KafkaDatadog
Likelihood to Recommend
8.3
(18 ratings)
9.1
(22 ratings)
Likelihood to Renew
9.0
(2 ratings)
-
(0 ratings)
Usability
10.0
(1 ratings)
10.0
(1 ratings)
Support Rating
8.4
(4 ratings)
8.9
(6 ratings)
User Testimonials
Apache KafkaDatadog
Likelihood to Recommend
Apache
Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.
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Datadog
DataDog Is well suited to all of the Infrastructure Monitoring Solutions, DB monitoring, and other Network monitoring also. It's not well suited because it cannot give perfect Infrastructure recommendations for our use case but also For example: If we are using AWS DB to monitor performance insights then Datadog is less effective there because AWS gives very niche recommendations.
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Pros
Apache
  • Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
  • Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
  • Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).
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Datadog
  • APIs, the ability to interact with the data we pull into data dog is key. We port the information over to Servicenow, so the ability to pull everything into DataDog, then Servicenow, is a key component of our success here at Wayfair.
  • Simple Interface - clean, useful, effective. Allows users to use DataDog for one reason, get work done.
  • Lightweight agent on hosts
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Cons
Apache
  • Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
  • Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
  • Learning curve around creation of broker and topics could be simplified
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Datadog
  • We had a couple "integrations" that had some issues during setup, but Support addressed them very quickly
  • Unnecessary alerts about DataDog components...by the time I see them, they're almost always also fixed
  • I wish there was a DataDog mobile app that would have dedicated alerts (configurable per alert to override Do Not Disturb setting) instead of relying on emails notifications that could be overlooked in the midst of many incoming emails around the same time.
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Likelihood to Renew
Apache
Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet
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Datadog
No answers on this topic
Usability
Apache
Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file
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Datadog
The user interface is quite intuitive with the exception of the network map. As a deployer of software, it is trivial to setup.
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Support Rating
Apache
Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.
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Datadog
The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.
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Alternatives Considered
Apache
I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond to other needs.
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Datadog
We are still trying other products, but people still like Datadog. After setting up a dashboard, it's great for monitoring instances on Datadog. Also, the DevOps team had a good time setting up Datadog. It means Datadog was way easier to set up compared to those others.
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Return on Investment
Apache
  • Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
  • Positive: it's scalable so we can develop small and scale for real-world scenarios
  • Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.
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Datadog
  • Visibility into website issues and performance problems has improved our company communication.
  • Handling and detecting site issues faster has improved customer satisfaction and retention.
  • Configuration of the Datadog site can take a bit of time and we lost a bit of developer time during that process.
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ScreenShots

Datadog Screenshots

Screenshot of Out-of-the-box and easily customizable monitoring dashboards.Screenshot of Datadog is built to give visibility across teams. You can discuss issues in-context with production data, annotate changes and notify your team, see who responded to that alert before, and remember what was done to fix it.Screenshot of Datadog seamlessly unifies traces, metrics, and logs—the three pillars of observability.Screenshot of Collect monitoring data from across your entire stack with Datadog's 400+ built-in integrations.Screenshot of Datadog's Service Map decomposes your application into all its component services and draws the observed dependencies between these services in real timeScreenshot of Centralize log data from any source.