Aerospike vs Druid

Overview

Aerospike

Stacks200

Followers288

Votes48

GitHub Stars1.3K

Forks196

Druid

Stacks376

Followers867

Votes32

Aerospike vs Druid: What are the differences?

Introduction: Both Aerospike and Druid are popular database management systems used for big data analytics and real-time data processing.

Data Model and Structure: Aerospike is a NoSQL database that follows an extensive key-value data model, while Druid is a column-oriented database that organizes data in columns rather than rows, enabling faster analytical queries on large datasets.
Query Processing: Aerospike focuses on real-time transactions and low-latency operations, making it suitable for applications requiring high-speed data processing. In contrast, Druid is designed for analytical queries on large-scale datasets, providing efficient querying capabilities for time-series data.
Scalability and Reliability: Aerospike offers automatic data distribution and replication for high availability and seamless scalability, making it ideal for handling large volumes of data and concurrent operations. Druid, on the other hand, provides distributed indexing and query execution across multiple nodes, ensuring scalability and fault tolerance for analytical workloads.
Consistency Model: Aerospike supports strong consistency guarantees with options for eventual consistency, allowing developers to balance consistency and performance based on application requirements. Druid prioritizes eventual consistency and relaxed isolation levels to optimize query performance and scalability, especially for time-series data analytics.
Data Ingestion: Aerospike provides real-time data ingestion capabilities for capturing and processing incoming data streams efficiently, ensuring up-to-date information for real-time applications. Druid offers batch and real-time ingestion mechanisms, enabling users to ingest and analyze data in near real-time, making it suitable for time-series and event-driven analytics.
Use Cases: Aerospike is commonly used for high-performance applications such as real-time bidding, fraud detection, and recommendation engines that require low latency and high throughput. In contrast, Druid is favored for OLAP (Online Analytical Processing) workloads, including time-series analysis, interactive data exploration, and ad hoc queries on historical data.

In Summary, Aerospike and Druid differ in their data models, query processing approaches, scalability mechanisms, consistency levels, data ingestion capabilities, and use cases.

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Manual

Detailed Comparison

Aerospike	Druid
Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.	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.
99% of reads/writes complete in under 1 millisecond.;Predictable low latency at high throughput – second to none. Read the YCSB Benchmark.;The secret sauce? A thousand things done right. Server code in ‘C’ (not Java or Erlang) precisely tuned to avoid context switching and memory copies. Highly parallelized multi-threaded, multi-core, multi-cpu, multi-SSD execution.;Indexes are always stored in RAM. Pure RAM mode is backed by spinning disks. In hybrid mode, individual tables are stored in either RAM or flash.	-
Statistics
GitHub Stars 1.3K	GitHub Stars -
GitHub Forks 196	GitHub Forks -
Stacks 200	Stacks 376
Followers 288	Followers 867
Votes 48	Votes 32
Pros & Cons
Pros 16 Ram and/or ssd persistence 12 Easy clustering support 5 Easy setup 4 Acid 3 Performance better than Redis	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
Integrations
No integrations available	Zookeeper

What are some alternatives to Aerospike, Druid?

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

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

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

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.

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

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.

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

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Aerospike vs Druid: What are the differences?

Introduction: Both Aerospike and Druid are popular database management systems used for big data analytics and real-time data processing.

Data Model and Structure: Aerospike is a NoSQL database that follows an extensive key-value data model, while Druid is a column-oriented database that organizes data in columns rather than rows, enabling faster analytical queries on large datasets.
Query Processing: Aerospike focuses on real-time transactions and low-latency operations, making it suitable for applications requiring high-speed data processing. In contrast, Druid is designed for analytical queries on large-scale datasets, providing efficient querying capabilities for time-series data.
Scalability and Reliability: Aerospike offers automatic data distribution and replication for high availability and seamless scalability, making it ideal for handling large volumes of data and concurrent operations. Druid, on the other hand, provides distributed indexing and query execution across multiple nodes, ensuring scalability and fault tolerance for analytical workloads.
Consistency Model: Aerospike supports strong consistency guarantees with options for eventual consistency, allowing developers to balance consistency and performance based on application requirements. Druid prioritizes eventual consistency and relaxed isolation levels to optimize query performance and scalability, especially for time-series data analytics.
Data Ingestion: Aerospike provides real-time data ingestion capabilities for capturing and processing incoming data streams efficiently, ensuring up-to-date information for real-time applications. Druid offers batch and real-time ingestion mechanisms, enabling users to ingest and analyze data in near real-time, making it suitable for time-series and event-driven analytics.
Use Cases: Aerospike is commonly used for high-performance applications such as real-time bidding, fraud detection, and recommendation engines that require low latency and high throughput. In contrast, Druid is favored for OLAP (Online Analytical Processing) workloads, including time-series analysis, interactive data exploration, and ad hoc queries on historical data.

In Summary, Aerospike and Druid differ in their data models, query processing approaches, scalability mechanisms, consistency levels, data ingestion capabilities, and use cases.