Apache Impala vs Splunk

Overview

Apache Impala

Stacks145

Followers301

Votes18

GitHub Stars34

Forks33

Splunk

Stacks772

Followers1.0K

Votes20

Apache Impala vs Splunk: What are the differences?

Apache Impala: Real-time Query for Hadoop. Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time; Splunk: 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.

Apache Impala and Splunk can be primarily classified as "Big Data" tools.

Some of the features offered by Apache Impala are:

Do BI-style Queries on Hadoop
Unify Your Infrastructure
Implement Quickly

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

Apache Impala is an open source tool with 2.27K GitHub stars and 838 GitHub forks. Here's a link to Apache Impala's open source repository on GitHub.

According to the StackShare community, Splunk has a broader approval, being mentioned in 67 company stacks & 170 developers stacks; compared to Apache Impala, which is listed in 18 company stacks and 57 developer stacks.

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Detailed Comparison

Apache Impala	Splunk
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.	It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse	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
GitHub Stars 34	GitHub Stars -
GitHub Forks 33	GitHub Forks -
Stacks 145	Stacks 772
Followers 301	Followers 1.0K
Votes 18	Votes 20
Pros & Cons
Pros 11 Super fast 1 High Performance 1 Massively Parallel Processing 1 Open Sourse 1 Scalability	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
Integrations
Hadoop Mode Redash Apache Kudu	No integrations available

What are some alternatives to Apache Impala, Splunk?

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.