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The Future of Distributed Messaging and Streaming: 8 Platforms You Should Know About

As you already know, many businesses went online to facilitate their growth and expansion. However, all that growth in the online sphere led to copious amounts of data being generated. 

To make smarter business decisions, provide unparalleled user experience, and use data to empower ML and AI, you need to find the best possible ways to build and manage data pipelines and store pub/sub data. 

That’s where distributed messaging and streaming platforms come in. There are many players in this industry, but only a handful of companies managed to offer complete solutions. Today, we will provide you with a competitive overview of the top players in steaming, pub/sub, and message queuing.

Confluent

Confluent is a completely managed Kafka service. In fact, it’s created by the devs who initially worked on Apache Kafka. It‘s an event stream processing platform aimed to cater to the needs of large enterprises. It supports real-time data streaming for AWS, GCP, and Azure. 

Confluent is useful for companies that have to manage a continually updating stream of events. Instead of taking snapshots of the stream, Confluent helps companies leverage data as it continuously updates. Confluent is built to help Dev-ops create event-driven applications without breaking a sweat.

Confluent enables companies to centralize their event streaming efforts. Users can integrate data from different sources and locations and build real-time data pipelines. 

The entire platform is built on top of Apache Kafka, which remains one of the most popular open-source distributed streaming solutions used by many Fortune 500 companies. Confluent comes with all the features of Kafka, including the two most important ones – Kafka Brokers and Kafka Java Client APIs.

Developers of Confluent found a sweet spot in the market. They used Kafka as the core of their solution to create an easy-to-use and understand platform. With data persistency and Connect API, organizations can build reliable and fast data pipelines connecting their other data systems with Confluent. 

IBM Streams

IBM Streams is IBMs turn on data streams. Streams is a robust software platform enabling developers to build apps that use information in data streams. IBM Streams is built to support different types of data in the data stream ranging from text and video to geospatial and sensor data. 

The IBM Streams offer is somewhat unique when compared to other top players in the niche. IBM offers development support to software architects who decide to use IBM Streams. It comes with a feature-rich visual IDE based on the famous Eclipse so that developers can build apps visually with this tool. It supports all the popular programming languages such as Java, Python, and Scala. 

Streams and Confluent have the same data connection capabilities, allowing you to connect Streams with any data source. It doesn’t matter whether it is a streaming, structured, or unstructured data source. Streams platform supports integration with complex data infrastructures as well, such as Spark and Hadoop.

IBM Streams excels at enabling architects to easily create and reuse modules that serve as a service interface between apps, device drivers, and other modules, which can be changed from the user level. Users can present any service interface to a stream user process or interchange modules with other service interfaces. 

TIBCO

TIBCO is another top platform in the distributed messages niches. It’s a reputable world-class software company that specializes in delivering solutions for monitoring, managing, and integrating enterprise apps and information delivery. While they offer many different solutions, the one that interests us is the platform for managing distributed systems. 

It helps businesses identify and leverage opportunities hidden in their real-time data streams. TIBCO’s team did a marvelous job making the building and management of data pipelines as seamless as possible. 

TIBCO’s platform for managing distributed systems is flexible, scalable, and reliable. TIBCO Messaging resolves the issue of integrating incompatible distributed systems, thus unifying data streams into several easy to manage data pipelines. 

It supports diverse digital infrastructures, which may encompass everything running from enterprise and cloud to mobile and IoT. TIBCO Messaging is the central piece of the TIBCO Connected Intelligence Platform, and it also comes with complete support for Apache Kafka and Apache Pulsar. TIBCO Messaging decouples message distribution to facilitate the creation of state-of-the-art event-enabled operations.

At the moment, the TIBCO Messaging solution consists of 6 components: TIBCO Enterprise Message Service, TIBCO FTL, TIBCO eFTL, TIBCO Messaging Eclipse Mosquito Distribution, Apache Kafka, and Apache Pulsar.

TIBCO also offers TIBCO Hawk – a sophisticated event-based monitoring system that enables real-time visibility and control of all distributed applications and systems.

Amazon Kinesis

Amazon created Kinesis to help businesses collect, manage, and process big chunks of data. Amazon Kinesis is equipped to help developers store huge data despite its type, including IoT telemetry data. This data can then be used for analytics, ML, or used by other apps. 

Through Kinesis, Amazon offers companies infrastructure and software to reduce the workload and cut down expenses. The data can be hosted on Amazon servers, but Kinesis also supports integration with other storage service providers, such as S3, Redshift, and DynamoDB. 

Kinesis offers excellent processing power and can comb through high capacity data pipes in seconds. That’s extremely useful for enterprises that work with large sets of data. Fast data processing also enables real-time data monitoring. 

The scaling with Kinesis is also streamlined. The platform can scale up and down when needed making it useful for companies with fluctuations in their data pipelines. The scalability also encompasses data sources as users can add or remove sources when needed. 

Kinesis also delivers hands-free management, so users don’t have to manage infrastructures – it’s completely managed by Amazon and can be custom-tailored to support any application. 

Finally, Kinesis is equipped to process video data for analytic or machine learning purposes. Thanks to real-time data processing, developers can leverage Kinesis to build real-time apps, synchronize data from IoT, and update machine learning models on the go.

Azure Event Hubs

Azure Event Hubs comes from Microsoft. It is a fully managed big data streaming and event ingestion service. It enables businesses to build custom-tailored dynamic data pipelines and stream millions of events per second. 

Many Fortune 500 companies use Event Hubs for device telemetry streaming, user telemetry processing, data archiving, live dashboarding, analytics pipelines, application logging, and more. Event Hubs is a state-of-the-art, distributed stream processing platform with support for various data types and numerous data sources. 

Event Hubs is a platform-as-a-service managed by a service provider. In this case, it’s Microsoft’s Azure team. It also supports integration with Apache Kafka ecosystems, so you’ll be able to use it without having to handle configurations and management personally. There’s even the auto-inflate feature that enables Event Hubs to meet your specific usage needs automatically.

Event Hubs is built around a partitioned consumer model. Thanks to this mode, the platform enables multiple applications to process the stream of data. Meanwhile, you can decide how much processing power you want to use.

There are two data storage solutions with Azure Event Hubs – you can store your data in real-time in Azure Blob or Azure Data Lake scalable storage solutions. Azure Blob storage is indicated for cloud-native workloads, machine learning, and high-performance computing. It’s perfect for cloud-native and mobile apps.

Azure Data Lake is massively scalable storage. It’s used for high-performance analytics workloads. Azure Data lake allows you to use tiered storage and optimize costs.

Google Pub/Sub

Google also decided to enter the distributed messaging landscape. Its platform is conveniently named Pub/Sub. Google prefers calling it “an asynchronous messaging service.” What does this imply? Google built Pub/Sub so that services that produce events are completely separated from the services that process the events. 

This approach to distributed messaging enables Google’s Pub/Sub platform to be used in different scenarios. It can be excellent messaging-oriented middleware. On the other hand, it can capture the events and deliver them for streaming analytics pipelines.

Google takes advantage of its huge cloud infrastructure to enable Pub/Sub to be a reliable platform able to deliver consistent performance at scale. Developer’s already found different use cases for Google Pub/Sub and managed to reduce operational costs to an absurd amount

Google Pub/Sub is an excellent solution for enterprises with a complex ecosystem and hundreds of microservices and apps. Each of the microservices and apps can work independently and still be a part of the complex system via Google Pub/Sub, where they can exchange data through a dynamic data pipeline.

Furthermore, Google has made the Pub/Sub to support all components of the vast Google Cloud, including Cloud logs, API, Dataflow, Storage, Networking, Engine, and more.

Apache Pulsar

Apache Pulsar is one of the cutting-edge Apache Software Foundation projects. It’s one of the leading distributed messaging and streaming platforms. It comes with all the capabilities of previously mentioned platforms and some more. For instance, it features unified messaging models, different subscription models, multi-layered architecture, and routing modes. 

These features and unique architecture make Apache Pulsar a true enabler of AI and ML, as it seamlessly resolves the issues of scalability and resource allocation. Apache Pulsar can also scale storage and computing separately and dynamically. 

When we look under the hood, we can see that Pulsar’s storage and broker layers are kept separate. It doesn’t store any data on the brokers allowing users to add/remove brokers as needed. It eliminates the need for re-partitioning the data and saves a lot of time and processing power down the line.

Furthermore, Pulsar takes an exciting spin on storage. The platform offers tiered storage to improve efficiencies and reduce operational costs, as it supports all data types. A storage layer consists of data segments and storage nods, and users can build storage layers for their specific distributed messaging needs and decide what data goes onto SSD-based storage and what goes onto HDD-based storage. 

Finally, Pulsar features a quorum-based replication algorithm to reduce latencies in communication and enable seamless information flow between clients and servers.

Pandio

Finally, we have Pandio as the pinnacle of distributed messaging solutions. Pandio is a managed Apache Pulsar service provider. What does that imply? Well, Pandio is a service built on Apache Pulsar. It’s the most competitive platform because it brings a unified messaging system, including pub/sub, message queues, and event streams. At the same time, it reduces complexity and delivers great performance.

Simply put, businesses that chose Pandio over other solutions get all the benefits of using Apache Pulsar. The main one being unparalleled scalability with separation of storage and compute. This feature alone enables Pandio to be used for AI and ML use cases, such as training and deploying new machine learning models. Not to mention the Apache Bookkeeper, which enables businesses to retain and recall data at lightning-fast speeds. 

Pandio also brings Pulsar’s multi-tenancy system. It allows Pandio to provide support for even the most complex distributed messaging systems and to enable easy distribution of topics. Then there is a geo-replication as well, thanks to which businesses can produce and deliver messages in different geo-locations hands-free. 

On top of everything, Pandio is a fully-managed service. The team behind Pandio consists of avid data experts. The company’s goal is to provide dependable service while minimizing the operational burden on their clients. 

These are the top 8 distributed messaging and streaming platforms you should know about. As you can see, many of them share the basic functionality. We have to highlight Apache Pulsar and Pandio as the most promising ones in terms of competitive overview. 

By separating storage and computing, enabling dynamic and on-demand scalability, and delivering great performance, these two platforms are most likely to dominate the landscape in the foreseeable future.

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