Alternatives to Azure Service Bus logo

Alternatives to Azure Service Bus

NServiceBus, RabbitMQ, Kafka, MSMQ, and IBM MQ are the most popular alternatives and competitors to Azure Service Bus.
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What is Azure Service Bus and what are its top alternatives?

Azure Service Bus is a cloud-based messaging service that facilitates communication between applications, services, and devices. It provides reliable message queuing and publish-subscribe messaging capabilities, ensuring secure and scalable communication. However, it has limitations such as message size restrictions and higher costs for large message volumes.

  1. RabbitMQ: RabbitMQ is an open-source message broker that supports multiple messaging protocols. Key features include message queuing, routing, and clustering. Pros include high performance and flexibility, while cons include more complex setup and maintenance compared to Azure Service Bus.

  2. Kafka: Apache Kafka is a distributed streaming platform known for its high throughput and fault tolerance. It enables real-time data processing and messaging at scale. Pros include high performance and fault tolerance, but it may require more expertise to operate than Azure Service Bus.

  3. Google Cloud Pub/Sub: Google Cloud Pub/Sub is a fully managed messaging service that offers durable message storage and global message routing. It is highly available and scalable, with features like push and pull subscriptions. Pros include seamless integration with other Google Cloud services, while cons may include vendor lock-in.

  4. Amazon SQS: Amazon Simple Queue Service (SQS) is a fully managed message queuing service that offers reliable and scalable message delivery. It supports standard and FIFO queues with features like dead-letter queues. Pros include easy scalability and reliability, but it may have higher costs for large message volumes.

  5. ActiveMQ: Apache ActiveMQ is an open-source message broker that supports many advanced features like message persistence, clustering, and message filtering. It is known for its performance and scalability. Pros include a rich feature set, but it may require more resources to maintain compared to Azure Service Bus.

  6. IBM MQ: IBM MQ is a messaging middleware platform that provides reliable and secure message transport. It supports various messaging protocols and features like message encryption and transaction management. Pros include enterprise-grade reliability and security, while cons may include higher costs.

  7. NATS: NATS is a lightweight and high-performance messaging system designed for cloud-native applications. It offers features like distributed queuing, publish-subscribe messaging, and security mechanisms. Pros include simplicity and performance, but it may lack some advanced features compared to Azure Service Bus.

  8. RocketMQ: Apache RocketMQ is a distributed messaging and streaming platform known for its low latency and high throughput. It supports features like message batching, filtering, and transaction messaging. Pros include performance and scalability, but it may require more resources to operate than Azure Service Bus.

  9. MuleSoft Anypoint Platform: MuleSoft Anypoint Platform includes a variety of API management and integration tools, including message queuing capabilities. It enables developers to build, integrate, and manage APIs and services. Pros include comprehensive integration features, but it may be overkill for simple messaging needs.

  10. Beanstalkd: Beanstalkd is a simple, fast, and reliable message queue software that offers easy queuing and processing of messages. It is lightweight and easy to set up, making it suitable for small to medium-sized projects. Pros include simplicity and speed, but it may lack some advanced features of Azure Service Bus.

Top Alternatives to Azure Service Bus

  • NServiceBus
    NServiceBus

    Performance, scalability, pub/sub, reliable integration, workflow orchestration, and everything else you could possibly want in a service bus. ...

  • RabbitMQ
    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • MSMQ
    MSMQ

    This technology enables applications running at different times to communicate across heterogeneous networks and systems that may be temporarily offline. Applications send messages to queues and read messages from queues. ...

  • IBM MQ
    IBM MQ

    It is a messaging middleware that simplifies and accelerates the integration of diverse applications and business data across multiple platforms. It offers proven, enterprise-grade messaging capabilities that skillfully and safely move information. ...

  • 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. ...

  • 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. ...

Azure Service Bus alternatives & related posts

NServiceBus logo

NServiceBus

58
2
Enterprise-grade scalability and reliability for your workflows and integrations
58
2
PROS OF NSERVICEBUS
  • 1
    Not as good as alternatives, good job security
  • 1
    Brings on-prem issues to the cloud
CONS OF NSERVICEBUS
    Be the first to leave a con

    related NServiceBus posts

    RabbitMQ logo

    RabbitMQ

    21.5K
    557
    Open source multiprotocol messaging broker
    21.5K
    557
    PROS OF RABBITMQ
    • 235
      It's fast and it works with good metrics/monitoring
    • 80
      Ease of configuration
    • 60
      I like the admin interface
    • 52
      Easy to set-up and start with
    • 22
      Durable
    • 19
      Standard protocols
    • 19
      Intuitive work through python
    • 11
      Written primarily in Erlang
    • 9
      Simply superb
    • 7
      Completeness of messaging patterns
    • 4
      Reliable
    • 4
      Scales to 1 million messages per second
    • 3
      Better than most traditional queue based message broker
    • 3
      Distributed
    • 3
      Supports MQTT
    • 3
      Supports AMQP
    • 2
      Clear documentation with different scripting language
    • 2
      Better routing system
    • 2
      Inubit Integration
    • 2
      Great ui
    • 2
      High performance
    • 2
      Reliability
    • 2
      Open-source
    • 2
      Runs on Open Telecom Platform
    • 2
      Clusterable
    • 2
      Delayed messages
    • 1
      Supports Streams
    • 1
      Supports STOMP
    • 1
      Supports JMS
    CONS OF RABBITMQ
    • 9
      Too complicated cluster/HA config and management
    • 6
      Needs Erlang runtime. Need ops good with Erlang runtime
    • 5
      Configuration must be done first, not by your code
    • 4
      Slow

    related RabbitMQ posts

    James Cunningham
    Operations Engineer at Sentry · | 18 upvotes · 1.8M views
    Shared insights
    on
    CeleryCeleryRabbitMQRabbitMQ
    at

    As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

    Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

    #MessageQueue

    See more
    Yogesh Bhondekar
    Product Manager | SaaS | Traveller · | 16 upvotes · 465K views

    Hi, I am building an enhanced web-conferencing app that will have a voice/video call, live chats, live notifications, live discussions, screen sharing, etc features. Ref: Zoom.

    I need advise finalizing the tech stack for this app. I am considering below tech stack:

    • Frontend: React
    • Backend: Node.js
    • Database: MongoDB
    • IAAS: #AWS
    • Containers & Orchestration: Docker / Kubernetes
    • DevOps: GitLab, Terraform
    • Brokers: Redis / RabbitMQ

    I need advice at the platform level as to what could be considered to support concurrent video streaming seamlessly.

    Also, please suggest what could be a better tech stack for my app?

    #SAAS #VideoConferencing #WebAndVideoConferencing #zoom #stack

    See more
    Kafka logo

    Kafka

    23.7K
    607
    Distributed, fault tolerant, high throughput pub-sub messaging system
    23.7K
    607
    PROS OF KAFKA
    • 126
      High-throughput
    • 119
      Distributed
    • 92
      Scalable
    • 86
      High-Performance
    • 66
      Durable
    • 38
      Publish-Subscribe
    • 19
      Simple-to-use
    • 18
      Open source
    • 12
      Written in Scala and java. Runs on JVM
    • 9
      Message broker + Streaming system
    • 4
      KSQL
    • 4
      Avro schema integration
    • 4
      Robust
    • 3
      Suport Multiple clients
    • 2
      Extremely good parallelism constructs
    • 2
      Partioned, replayable log
    • 1
      Simple publisher / multi-subscriber model
    • 1
      Fun
    • 1
      Flexible
    CONS OF KAFKA
    • 32
      Non-Java clients are second-class citizens
    • 29
      Needs Zookeeper
    • 9
      Operational difficulties
    • 5
      Terrible Packaging

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    Nick Rockwell
    SVP, Engineering at Fastly · | 46 upvotes · 4.3M views

    When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

    So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

    React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

    Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

    See more
    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.4M views

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

    We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

    #BigData #AWS #DataScience #DataEngineering

    See more
    MSMQ logo

    MSMQ

    33
    3
    A technology for asynchronous messaging
    33
    3
    PROS OF MSMQ
    • 2
      Easy to learn
    • 1
      Cloud not needed
    CONS OF MSMQ
    • 1
      Windows dependency

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    IBM MQ logo

    IBM MQ

    115
    11
    Enterprise-grade messaging middleware
    115
    11
    PROS OF IBM MQ
    • 3
      Reliable for banking transactions
    • 3
      Useful for big enteprises
    • 2
      Secure
    • 1
      Broader connectivity - more protocols, APIs, Files etc
    • 1
      Many deployment options (containers, cloud, VM etc)
    • 1
      High Availability
    CONS OF IBM MQ
    • 2
      Cost

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    Shared insights
    on
    Azure Service BusAzure Service BusIBM MQIBM MQ

    Want to get the differences in features and enhancement, pros and cons, and also how to Migrate from IBM MQ to Azure Service Bus.

    See more
    MySQL logo

    MySQL

    126.1K
    3.8K
    The world's most popular open source database
    126.1K
    3.8K
    PROS OF MYSQL
    • 800
      Sql
    • 679
      Free
    • 562
      Easy
    • 528
      Widely used
    • 490
      Open source
    • 180
      High availability
    • 160
      Cross-platform support
    • 104
      Great community
    • 79
      Secure
    • 75
      Full-text indexing and searching
    • 26
      Fast, open, available
    • 16
      Reliable
    • 16
      SSL support
    • 15
      Robust
    • 9
      Enterprise Version
    • 7
      Easy to set up on all platforms
    • 3
      NoSQL access to JSON data type
    • 1
      Relational database
    • 1
      Easy, light, scalable
    • 1
      Sequel Pro (best SQL GUI)
    • 1
      Replica Support
    CONS OF MYSQL
    • 16
      Owned by a company with their own agenda
    • 3
      Can't roll back schema changes

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    Nick Rockwell
    SVP, Engineering at Fastly · | 46 upvotes · 4.3M views

    When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

    So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

    React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

    Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

    See more
    Tim Abbott

    We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

    We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

    And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

    I can't recommend it highly enough.

    See more
    PostgreSQL logo

    PostgreSQL

    98.9K
    3.5K
    A powerful, open source object-relational database system
    98.9K
    3.5K
    PROS OF POSTGRESQL
    • 764
      Relational database
    • 510
      High availability
    • 439
      Enterprise class database
    • 383
      Sql
    • 304
      Sql + nosql
    • 173
      Great community
    • 147
      Easy to setup
    • 131
      Heroku
    • 130
      Secure by default
    • 113
      Postgis
    • 50
      Supports Key-Value
    • 48
      Great JSON support
    • 34
      Cross platform
    • 33
      Extensible
    • 28
      Replication
    • 26
      Triggers
    • 23
      Multiversion concurrency control
    • 23
      Rollback
    • 21
      Open source
    • 18
      Heroku Add-on
    • 17
      Stable, Simple and Good Performance
    • 15
      Powerful
    • 13
      Lets be serious, what other SQL DB would you go for?
    • 11
      Good documentation
    • 9
      Scalable
    • 8
      Free
    • 8
      Reliable
    • 8
      Intelligent optimizer
    • 7
      Transactional DDL
    • 7
      Modern
    • 6
      One stop solution for all things sql no matter the os
    • 5
      Relational database with MVCC
    • 5
      Faster Development
    • 4
      Full-Text Search
    • 4
      Developer friendly
    • 3
      Excellent source code
    • 3
      Free version
    • 3
      Great DB for Transactional system or Application
    • 3
      Relational datanbase
    • 3
      search
    • 3
      Open-source
    • 2
      Text
    • 2
      Full-text
    • 1
      Can handle up to petabytes worth of size
    • 1
      Composability
    • 1
      Multiple procedural languages supported
    • 0
      Native
    CONS OF POSTGRESQL
    • 10
      Table/index bloatings

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    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.9M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
    See more
    Jeyabalaji Subramanian

    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!

    See more
    MongoDB logo

    MongoDB

    94K
    4.1K
    The database for giant ideas
    94K
    4.1K
    PROS OF MONGODB
    • 828
      Document-oriented storage
    • 593
      No sql
    • 553
      Ease of use
    • 464
      Fast
    • 410
      High performance
    • 255
      Free
    • 218
      Open source
    • 180
      Flexible
    • 145
      Replication & high availability
    • 112
      Easy to maintain
    • 42
      Querying
    • 39
      Easy scalability
    • 38
      Auto-sharding
    • 37
      High availability
    • 31
      Map/reduce
    • 27
      Document database
    • 25
      Easy setup
    • 25
      Full index support
    • 16
      Reliable
    • 15
      Fast in-place updates
    • 14
      Agile programming, flexible, fast
    • 12
      No database migrations
    • 8
      Easy integration with Node.Js
    • 8
      Enterprise
    • 6
      Enterprise Support
    • 5
      Great NoSQL DB
    • 4
      Support for many languages through different drivers
    • 3
      Schemaless
    • 3
      Aggregation Framework
    • 3
      Drivers support is good
    • 2
      Fast
    • 2
      Managed service
    • 2
      Easy to Scale
    • 2
      Awesome
    • 2
      Consistent
    • 1
      Good GUI
    • 1
      Acid Compliant
    CONS OF MONGODB
    • 6
      Very slowly for connected models that require joins
    • 3
      Not acid compliant
    • 2
      Proprietary query language

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    Jeyabalaji Subramanian

    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!

    See more
    Robert Zuber

    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.

    See more