Splunk vs StreamSets

Overview

Splunk

Stacks772

Followers1.0K

Votes20

StreamSets

Stacks53

Followers133

Votes0

StreamSets vs Splunk: What are the differences?

Developers describe StreamSets as "Where DevOps Meets Data Integration". The industry's first data operations platform for full life-cycle management of data in motion. On the other hand, Splunk is detailed as "Search, monitor, analyze and visualize machine data". It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

StreamSets can be classified as a tool in the "Message Queue" category, while Splunk is grouped under "Big Data Tools".

Some of the features offered by StreamSets are:

Build Batch & Streaming Pipelines in Hours
Map and Monitor Runtime Performance
Protect Sensitive Data as it Arrives

On the other hand, Splunk provides the following key features:

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

Nubank, Yelp, and Intuit are some of the popular companies that use Splunk, whereas StreamSets is used by Leveris, FairVentures, and bigspark. Splunk has a broader approval, being mentioned in 67 company stacks & 170 developers stacks; compared to StreamSets, which is listed in 3 company stacks and 12 developer stacks.

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Detailed Comparison

Splunk	StreamSets
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
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	Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
Statistics
Stacks 772	Stacks 53
Followers 1.0K	Followers 133
Votes 20	Votes 0
Pros & Cons
Pros 3 API for searching logs, running reports 3 Alert system based on custom query results 2 Splunk language supports string, date manip, math, etc 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	Cons 2 No user community 1 Crashes
Integrations
No integrations available	HBase Databricks Amazon Redshift MySQL gRPC Google BigQuery Amazon Kinesis Cassandra Hadoop Redis

What are some alternatives to Splunk, StreamSets?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

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.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

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.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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.