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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Google Cloud Bigtable vs InfluxDB

Google Cloud Bigtable vs InfluxDB

OverviewDecisionsComparisonAlternatives

Overview

InfluxDB
InfluxDB
Stacks1.0K
Followers1.2K
Votes175
Google Cloud Bigtable
Google Cloud Bigtable
Stacks173
Followers363
Votes25

Google Cloud Bigtable vs InfluxDB: What are the differences?

Introduction

  1. Data Model: Google Cloud Bigtable is a NoSQL, wide-column store database, whereas InfluxDB is a time-series database designed specifically for handling time-series data efficiently.

  2. Scalability: Google Cloud Bigtable can scale horizontally by adding more nodes to handle increasing workloads, while InfluxDB can scale vertically by increasing the resources of a single node.

  3. Use Case: Google Cloud Bigtable is well-suited for applications requiring high throughput and low-latency access to large amounts of data, whereas InfluxDB is optimized for storing and querying time-series data such as IoT sensor data, financial metrics, and DevOps monitoring data.

  4. Consistency: Google Cloud Bigtable offers eventual consistency, where reads may not always reflect the most recent write, while InfluxDB supports strong consistency, ensuring that reads reflect the most recent write.

  5. Query Language: InfluxDB uses its own query language, InfluxQL, designed specifically for time-series data querying, while Google Cloud Bigtable uses SQL-like queries with standard SQL syntax.

  6. Integration with Ecosystem: Google Cloud Bigtable seamlessly integrates with other Google Cloud Platform services, making it easier for users to build and deploy applications within the Google Cloud ecosystem, whereas InfluxDB has a rich integration ecosystem with various tools and platforms, including Grafana, Telegraf, and Chronograf.

In Summary, Google Cloud Bigtable and InfluxDB differ in terms of data model, scalability, use case, consistency, query language, and integration with the ecosystem.

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Advice on InfluxDB, Google Cloud Bigtable

Anonymous
Anonymous

Apr 21, 2020

Needs advice

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

381k views381k
Comments
Benoit
Benoit

Principal Engineer at Sqreen

Sep 21, 2019

Decided

I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.

The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)

We are combining this with Grafana for display and Telegraf for data collection

155k views155k
Comments
pionell
pionell

Sep 16, 2020

Needs adviceonMariaDBMariaDB

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

159k views159k
Comments

Detailed Comparison

InfluxDB
InfluxDB
Google Cloud Bigtable
Google Cloud Bigtable

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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.

Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer
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
Stacks
1.0K
Stacks
173
Followers
1.2K
Followers
363
Votes
175
Votes
25
Pros & Cons
Pros
  • 59
    Time-series data analysis
  • 30
    Easy setup, no dependencies
  • 24
    Fast, scalable & open source
  • 21
    Open source
  • 20
    Real-time analytics
Cons
  • 4
    Instability
  • 1
    Proprietary query language
  • 1
    HA or Clustering is only in paid version
Pros
  • 11
    High performance
  • 9
    Fully managed
  • 5
    High scalability
Integrations
No integrations available
Heroic
Heroic
Hadoop
Hadoop
Apache Spark
Apache Spark

What are some alternatives to InfluxDB, Google Cloud Bigtable?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

Amazon DynamoDB

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.

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