What is Google Cloud Bigtable and what are its top alternatives?
Top Alternatives to Google Cloud Bigtable
Google Cloud Datastore
Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries. ...
It is an easy-to-use tool for creating business applications, from templates or from scratch. With its rich and intuitive design tools, it can help you create appealing and highly functional applications in a minimal amount of time. ...
Google Cloud Spanner
It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover. ...
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. ...
Google Cloud Storage
Google Cloud Storage allows world-wide storing and retrieval of any amount of data and at any time. It provides a simple programming interface which enables developers to take advantage of Google's own reliable and fast networking infrastructure to perform data operations in a secure and cost effective manner. If expansion needs arise, developers can benefit from the scalability provided by Google's infrastructure. ...
Google Cloud SQL
MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs. ...
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. ...
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. ...
Google Cloud Bigtable alternatives & related posts
- High scalability7
- Ability to query any property2
- Pay for what you use1
related Google Cloud Datastore posts
related Microsoft Access posts
related Google Cloud Spanner posts
- Document-oriented storage826
- No sql591
- Ease of use548
- High performance407
- Open source215
- Replication & high availability143
- Easy to maintain110
- Easy scalability38
- High availability36
- Document database27
- Full index support25
- Easy setup25
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise Support6
- Great NoSQL DB5
- Drivers support is good3
- Aggregation Framework3
- Support for many languages through different drivers3
- Managed service2
- Easy to Scale2
- Acid Compliant1
- Very slowly for connected models that require joins5
- Not acid compliant3
- Proprietary query language1
related MongoDB posts
Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.
We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient
Based on the above criteria, we selected the following tools to perform the end to end data replication:
We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.
We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.
In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.
Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.
In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!
We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.
As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).
When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.
- More praticlal and easy1
related Google Cloud Storage posts
In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.
For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.
For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.
We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.
Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.
- Fully managed13
- Backed by Google10
- Encryption at rest and transit3
- Replication across multiple zone by default3
- Automatic Software Patching3
related Google Cloud SQL posts
We use Go for the first-off due to our knowledge of it. Second off, it's highly performant and optimized for scalability. We run it using dockerized containers for our backend REST APIs.
For Frontend, we use React with Next.js at vercel. We use NextJS here mostly due to our need for Server Side Rendering and easier route management.
For Database, we use MySQL as it is first-off free and always has been in use with us. We use Google Cloud SQL from GCP that manages its storage and versions along with HA.
All stacks are free to use and get the best juice out of the system. We also use Redis for caching for enterprise-grade apps where data retrieval latency matters the most.
As far as the backend goes, we first had to decide which database will power most of Daily services. Considering relational databases vs document datbases, we decided that the relational model is a better fit for Daily as we have a lot of connections between the different entities. At the time MySQL was the only service available on Google Cloud SQL so this was out choice. In terms of #backend development Node.js powers most of our services, thanks to its amazing ecosystem there are a lot of modules publicly available to shorten the development time. Go is for the light services which are all about performance and delivering quickly the response, such as our redirector service.
- Predictable performance and cost62
- Native JSON Support35
- AWS Free Tier21
- No sql3
- To store data3
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
- Only sequential access for paginate data4
- Document Limit Size1
related Amazon DynamoDB posts
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
Uploadcare has built an infinitely scalable infrastructure by leveraging AWS. Building on top of AWS allows us to process 350M daily requests for file uploads, manipulations, and deliveries. When we started in 2011 the only cloud alternative to AWS was Google App Engine which was a no-go for a rather complex solution we wanted to build. We also didn’t want to buy any hardware or use co-locations.
Our stack handles receiving files, communicating with external file sources, managing file storage, managing user and file data, processing files, file caching and delivery, and managing user interface dashboards.
At its core, Uploadcare runs on Python. The Europython 2011 conference in Florence really inspired us, coupled with the fact that it was general enough to solve all of our challenges informed this decision. Additionally we had prior experience working in Python.
We chose to build the main application with Django because of its feature completeness and large footprint within the Python ecosystem.
All the communications within our ecosystem occur via several HTTP APIs, Redis, Amazon S3, and Amazon DynamoDB. We decided on this architecture so that our our system could be scalable in terms of storage and database throughput. This way we only need Django running on top of our database cluster. We use PostgreSQL as our database because it is considered an industry standard when it comes to clustering and scaling.
- Cloud Storage14
- Easy to use14
- Easy setup11
- Realtime Database11
- Super fast9
- Realtime listeners6
- Google Analytics integration5
- Could Messaging5
- Performance Monitoring4
- Crash Reporting4
- Test Lab for Android3
- Sharing App via invites3
- Adwords, Admob integration3
- Dynamic Links (Deeplinking support)2
- Robust ALI0
- Doesn't support FullTextSearch natively6
related Cloud Firestore posts
Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.
Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.
Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.
Git + GitHub + Visual Studio Code is a killer stack.
We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?