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  1. Stackups
  2. Application & Data
  3. NoSQL Databases
  4. NOSQL Database As A Service
  5. Google Cloud Bigtable vs Serverless

Google Cloud Bigtable vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Google Cloud Bigtable
Google Cloud Bigtable
Stacks173
Followers363
Votes25
Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K

Google Cloud Bigtable vs Serverless: What are the differences?

Introduction

Bigtable and Serverless are two different cloud computing services offered by Google that serve different purposes. Bigtable is a NoSQL service designed for handling large amounts of structured data, while Serverless is a compute service that allows developers to build and run applications without the need to manage servers. While both services are part of the Google Cloud platform, they have distinct features and use cases that set them apart.

  1. Scalability and Performance: Google Cloud Bigtable is specifically designed to handle massive scale and high-performance workloads. It can seamlessly scale horizontally to handle an increasing amount of data and requests, making it suitable for scenarios where fast read and write operations are crucial. On the other hand, Serverless offers automatic scaling capabilities, but it may not be as well-suited for extremely large-scale applications that require the performance and scalability of Bigtable.

  2. Data Structure: Bigtable is a wide-column NoSQL database that allows flexible schema design, making it suitable for storing structured data with varying types of columns. It can handle large amounts of data and supports efficient querying using row keys. In contrast, Serverless provides a more traditional relational database structure, allowing for the storage and retrieval of structured data in the form of tables. It is better suited for applications that require a relational database model.

  3. Pricing Model: Bigtable pricing is based on the amount of data stored, network egress, and provisioned throughput capacity, offering cost predictability for managing large-scale workloads. Serverless, on the other hand, follows a pay-per-use model, where users are billed based on the actual resources consumed during the execution of their functions. This model can be more cost-effective for applications with sporadic or unpredictable workloads.

  4. Management and Administration: Bigtable requires more administrative effort and expertise for tasks such as capacity planning, instance creation, and data replication. It provides more control and customization options for managing the database and its performance. In contrast, Serverless abstracts away the management of servers, allowing developers to focus solely on coding and deploying their applications without the need to handle infrastructure-related tasks.

  5. Integration with Other Services: Bigtable integrates well with other Google Cloud services, such as BigQuery, Dataflow, and Pub/Sub, enabling seamless data ingestion, processing, and analytics workflows. It can be a powerful component in building data pipelines and real-time processing systems. Serverless, on the other hand, integrates with various services within the Google Cloud ecosystem, providing a serverless environment for running functions that can be triggered by events from these services.

  6. Use Case Focus: Google Cloud Bigtable is commonly used for applications that require real-time analytics, time-series data analysis, ad tech, and large-scale operational data stores. It excels in use cases where low-latency reads and writes are crucial. Serverless, on the other hand, is ideal for building lightweight applications, event-driven workflows, and microservices that can scale automatically without the need for manual infrastructure management.

In summary, Google Cloud Bigtable and Serverless differ in terms of scalability, data structure, pricing model, management complexity, integration capabilities, and use case focus, making them suitable for distinct application scenarios within the Google Cloud ecosystem.

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Advice on Google Cloud Bigtable, Serverless

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments

Detailed Comparison

Google Cloud Bigtable
Google Cloud Bigtable
Serverless
Serverless

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.

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

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
-
GitHub Stars
46.9K
GitHub Forks
-
GitHub Forks
5.7K
Stacks
173
Stacks
2.2K
Followers
363
Followers
1.2K
Votes
25
Votes
28
Pros & Cons
Pros
  • 11
    High performance
  • 9
    Fully managed
  • 5
    High scalability
Pros
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    Auto scale
  • 1
    Openwhisk
Integrations
Heroic
Heroic
Hadoop
Hadoop
Apache Spark
Apache Spark
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway

What are some alternatives to Google Cloud Bigtable, Serverless?

AWS Lambda

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

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.

Azure Cosmos DB

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

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.

Azure Functions

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Cloudant

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.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

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