Druid vs Heroku Redis

Overview

Druid

Stacks377

Followers867

Votes32

Heroku Redis

Stacks105

Followers163

Votes5

Druid vs Heroku Redis: What are the differences?

Druid: Fast column-oriented distributed data store. Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations; Heroku Redis: Reliable and powerful Redis as a service. Heroku Redis is an in-memory key-value data store, run by Heroku, that is provisioned and managed as an add-on. Heroku Redis is accessible from any language with a Redis driver, including all languages and frameworks supported by Heroku.

Druid can be classified as a tool in the "Big Data Tools" category, while Heroku Redis is grouped under "Redis Hosting".

Druid is an open source tool with 8.22K GitHub stars and 2.05K GitHub forks. Here's a link to Druid's open source repository on GitHub.

According to the StackShare community, Druid has a broader approval, being mentioned in 24 company stacks & 12 developers stacks; compared to Heroku Redis, which is listed in 12 company stacks and 14 developer stacks.

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CLI (Node.js)

Manual

Detailed Comparison

Druid	Heroku Redis
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.	Heroku Redis is an in-memory key-value data store, run by Heroku, that is provisioned and managed as an add-on. Heroku Redis is accessible from any language with a Redis driver, including all languages and frameworks supported by Heroku.
-	Easily Optimize;Vertically Scalable
Statistics
Stacks 377	Stacks 105
Followers 867	Followers 163
Votes 32	Votes 5
Pros & Cons
Pros 15 Real Time Aggregations 6 Batch and Real-Time Ingestion 5 OLAP 3 OLAP + OLTP 2 Combining stream and historical analytics Cons 3 Limited sql support 2 Joins are not supported well 1 Complexity	Pros 5 More reliable than the other Redis add-ons Cons 1 More expensive than the other options
Integrations
Zookeeper	Heroku Redis

What are some alternatives to Druid, Heroku Redis?

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.

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.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

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

Apache Impala

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.

Redis To Go

Redis To Go was created to make the managing Redis instances easier, whether it is just one instance or serveral. Deploying a new instance of Redis is dead simple, whether for production or development.