Aerospike vs Google Cloud Bigtable

Overview

Aerospike

Stacks200

Followers288

Votes48

GitHub Stars1.3K

Forks196

Google Cloud Bigtable

Stacks173

Followers363

Votes25

Aerospike vs Google Cloud Bigtable: What are the differences?

Introduction

Aerospike and Google Cloud Bigtable are two widely used databases, each offering unique features and capabilities for handling large-scale data. In this comparison, we will highlight the key differences between Aerospike and Google Cloud Bigtable.

Data Model: Aerospike follows a key-value data model, where data is stored and retrieved using a primary key. It provides the flexibility to store various types of objects as values, including complex data structures like lists and maps. On the other hand, Google Cloud Bigtable is a wide-column store that organizes data in tables with rows and columns. It is designed for storing massive amounts of structured data with high read and write throughput.
Distribution and Scalability: Aerospike is a distributed database that allows seamless distribution of data across multiple nodes. It provides automatic partitioning and replication for high availability and scalability. Google Cloud Bigtable also offers distribution and scalability, but it leverages Google's infrastructure to handle data across a vast number of nodes. It allows dynamic scaling based on workload demands.
Consistency Model: Aerospike guarantees strong consistency by default, ensuring that all replicas see the same version of the data at any given time. This provides predictable and reliable behavior but may come at the cost of increased latency. Google Cloud Bigtable, on the other hand, follows a eventual consistency model, where data updates may take time to propagate across all replicas. This allows for lower latency but may result in occasional stale reads.
Secondary Indexing: Aerospike supports secondary indexing, allowing efficient querying of data on fields other than the primary key. This enables complex queries and filtering based on various attributes of the stored objects. In contrast, Google Cloud Bigtable does not natively support secondary indexing. To efficiently query data on non-key attributes, additional indexing mechanisms or external tools need to be used.
Query Language: Aerospike provides a declarative query language called SQL-92, allowing users to perform complex queries using SQL-like syntax. This simplifies data retrieval and analysis tasks for users familiar with SQL. On the other hand, Google Cloud Bigtable does not provide a built-in query language. Instead, it offers APIs and integrations with other tools to interact with the database programmatically.
Integration with Ecosystem: Aerospike offers various integration options, including connectors for popular programming languages, analytics frameworks like Apache Spark, and stream processing platforms like Apache Kafka. This enables seamless integration with existing data processing pipelines. Google Cloud Bigtable is tightly integrated with the Google Cloud ecosystem, allowing easy integration with other Google Cloud services like BigQuery, Dataflow, and Pub/Sub. This simplifies the development of end-to-end data pipelines within the Google Cloud platform.

In summary, Aerospike and Google Cloud Bigtable differ in data modeling approach, distribution and scalability mechanisms, consistency guarantees, secondary indexing support, query language availability, and integration options with the wider data ecosystem. Choosing between them depends on specific use cases, requirements, and existing infrastructure.

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Detailed Comparison

Aerospike	Google Cloud Bigtable
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.	Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
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.	Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.;Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google’s big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.;Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable’s total cost of ownership is less than half the cost of its direct competition.;Security: Cloud Bigtable is built with a replicated storage strategy, and all data is encrypted both in-flight and at rest.;Simplicity: Creating or reconfiguring a Cloud Bigtable cluster is done through a simple user interface and can be completed in less than 10 seconds. As data is put into Cloud Bigtable the backing storage scales automatically, so there’s no need to do complicated estimates of capacity requirements.;Maturity: Over the past 10+ years, Bigtable has driven Google’s most critical applications. In addition, the HBase API is a industry-standard interface for combined operational and analytical workloads.
Statistics
GitHub Stars 1.3K	GitHub Stars -
GitHub Forks 196	GitHub Forks -
Stacks 200	Stacks 173
Followers 288	Followers 363
Votes 48	Votes 25
Pros & Cons
Pros 16 Ram and/or ssd persistence 12 Easy clustering support 5 Easy setup 4 Acid 3 Petabyte Scale	Pros 11 High performance 9 Fully managed 5 High scalability
Integrations
No integrations available	Heroic Hadoop Apache Spark

What are some alternatives to Aerospike, Google Cloud Bigtable?

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

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.

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

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

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.

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Related Comparisons

Aerospike vs Google Cloud Bigtable: What are the differences?

Introduction

Data Model: Aerospike follows a key-value data model, where data is stored and retrieved using a primary key. It provides the flexibility to store various types of objects as values, including complex data structures like lists and maps. On the other hand, Google Cloud Bigtable is a wide-column store that organizes data in tables with rows and columns. It is designed for storing massive amounts of structured data with high read and write throughput.
Distribution and Scalability: Aerospike is a distributed database that allows seamless distribution of data across multiple nodes. It provides automatic partitioning and replication for high availability and scalability. Google Cloud Bigtable also offers distribution and scalability, but it leverages Google's infrastructure to handle data across a vast number of nodes. It allows dynamic scaling based on workload demands.
Consistency Model: Aerospike guarantees strong consistency by default, ensuring that all replicas see the same version of the data at any given time. This provides predictable and reliable behavior but may come at the cost of increased latency. Google Cloud Bigtable, on the other hand, follows a eventual consistency model, where data updates may take time to propagate across all replicas. This allows for lower latency but may result in occasional stale reads.
Secondary Indexing: Aerospike supports secondary indexing, allowing efficient querying of data on fields other than the primary key. This enables complex queries and filtering based on various attributes of the stored objects. In contrast, Google Cloud Bigtable does not natively support secondary indexing. To efficiently query data on non-key attributes, additional indexing mechanisms or external tools need to be used.
Query Language: Aerospike provides a declarative query language called SQL-92, allowing users to perform complex queries using SQL-like syntax. This simplifies data retrieval and analysis tasks for users familiar with SQL. On the other hand, Google Cloud Bigtable does not provide a built-in query language. Instead, it offers APIs and integrations with other tools to interact with the database programmatically.
Integration with Ecosystem: Aerospike offers various integration options, including connectors for popular programming languages, analytics frameworks like Apache Spark, and stream processing platforms like Apache Kafka. This enables seamless integration with existing data processing pipelines. Google Cloud Bigtable is tightly integrated with the Google Cloud ecosystem, allowing easy integration with other Google Cloud services like BigQuery, Dataflow, and Pub/Sub. This simplifies the development of end-to-end data pipelines within the Google Cloud platform.

Aerospike vs Google Cloud Bigtable

Overview