Amazon Athena vs Splunk

Overview

Splunk

Stacks773

Followers1.0K

Votes20

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Splunk: What are the differences?

Key Differences between Amazon Athena and Splunk

1. Query Language: The query languages used by Amazon Athena and Splunk are significantly different. Amazon Athena uses SQL-like queries, allowing users to leverage their existing SQL skills to query data stored in Amazon S3. On the other hand, Splunk utilizes its own proprietary Splunk Query Language (SPL) which is specifically designed for searching, analyzing, and visualizing machine-generated data.

2. Data Sources: Another crucial difference between Amazon Athena and Splunk is the supported data sources. Amazon Athena is primarily designed to query and analyze data stored in Amazon S3, enabling users to directly query structured, semi-structured, and unstructured data formats without the need for data preprocessing. In contrast, Splunk is built to ingest and analyze machine-generated data from various sources such as logs, metrics, events, and more, offering a wide range of integrations to collect data from different systems, applications, and devices.

3. Deployment Model: Amazon Athena is a serverless service provided by Amazon Web Services (AWS), where users can simply write queries and pay based on the amount of data scanned by their queries. Splunk, on the other hand, is an on-premises and cloud-based solution that requires dedicated infrastructure and resources to deploy and manage. This fundamental difference in deployment models affects factors like scalability, maintenance, and upfront costs.

4. Scalability and Performance: When it comes to scalability and performance, the architectures of Amazon Athena and Splunk differ significantly. Amazon Athena leverages the distributed processing power of AWS infrastructure, automatically executing queries in parallel and handling massive datasets with ease. On the other hand, Splunk's performance is heavily dependent on the resources allocated to it, necessitating proper sizing and optimization to handle large-scale data volumes effectively.

5. Advanced Analytics and Machine Learning: Amazon Athena provides integration with AWS services like Amazon QuickSight and Amazon Machine Learning, enabling users to build visualizations and apply machine learning algorithms to their query results. In contrast, Splunk offers its own suite of advanced analytics and machine learning capabilities, allowing users to perform anomaly detection, predictive analytics, and other data-driven tasks directly within the platform.

6. Cost Structure: Lastly, the cost structures of Amazon Athena and Splunk are different. Amazon Athena follows a pay-per-query model, where users only pay for the data scanned by their queries. In contrast, Splunk's pricing is based on the amount of data ingested and indexed, with additional costs for add-ons and premium features. Understanding the cost implications of each platform is essential for making informed decisions and optimizing the overall expenditure.

In summary, Amazon Athena and Splunk differ in their query languages, supported data sources, deployment models, scalability and performance, advanced analytics capabilities, and cost structures. The choice between the two platforms depends on specific requirements such as the type of data being analyzed, existing infrastructure, budgetary considerations, and integration needs.

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Advice on Splunk, Amazon Athena

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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Comments

Detailed Comparison

Splunk	Amazon Athena
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.	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.
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	-
Statistics
Stacks 773	Stacks 521
Followers 1.0K	Followers 840
Votes 20	Votes 49
Pros & Cons
Pros 3 API for searching logs, running reports 3 Alert system based on custom query results 2 Ability to style search results into reports 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 16 Use SQL to analyze CSV files 8 Glue crawlers gives easy Data catalogue 7 Cheap 6 Query all my data without running servers 24x7 4 No data base servers yay
Integrations
No integrations available	Amazon S3 Presto

What are some alternatives to Splunk, Amazon Athena?

Papertrail

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Loggly

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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

Sematext

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

Fluentd

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure.

Related Comparisons

Amazon Athena vs Splunk: What are the differences?

Key Differences between Amazon Athena and Splunk

Amazon Athena vs Splunk

Overview

Amazon Athena vs Splunk: What are the differences?