The Need for Speed in Data Processing

Capitalism vs. Socialism, Coca-Cola vs. Pepsi, Batch Ingestion vs. Real-time - these are all classic examples of conflict in the world and everyone has an opinion on what is better. I’m not a political ideologist nor a soft-drink drinker, but I am a Data Engineer and therefore experience the dichotomy between batch and real-time data ingestion on a daily basis. 

In today's fast-paced digital world, real-time data ingestion has become a crucial component for businesses to stay ahead of the curve, and it is simply impossible to do everything as batch processes. If companies wish to compete at the top level with their data ecosystems, they need to implement either real-time data ingestion or near real-time data ingestion to leverage the power of live analytics to get a second-by-second view of their business.

In this article, we'll explore what real-time data ingestion is, the tools available on the market today, and how they can help you streamline your data pipelines. Let's dive in!

What is Real-time Data Ingestion?

Real-time data ingestion is all about processing data as soon as it's generated, without any noticeable delay. Humans are doing real-time data ingestion and analytics constantly - when you listen to someone speaking you are ‘ingesting’ their sound data in real-time and processing it instantaneously before deciding how to respond. Similarly, in the data and analytics world, some systems require us to ingest data from a source in real time and process it on the fly.

Real-time systems are designed to handle data immediately as it comes in, which can be crucial for applications where every millisecond matters. This real-time data could be coming from dynamic systems that are constantly changing like sensors on a wind turbine, signals from the stock market, or traffic management systems.

What is Near Real-time Data Ingestion?

Some data systems also implement near real-time data ingestion which is a bit slower than real-time, but still pretty fast! It means we implement a small, but acceptable delay between when the data is generated and when it's processed. It's like when you're texting someone and they respond within seconds – not quite instant, but close enough.

This small delay might not be a big deal for some applications, like monitoring website analytics or social media feeds, and the cost saving from implementing a near real-time ingestion system like this outweighs the benefits of implementing a pure real-time system.

I have offered some examples of the differences between near real-time and real-time ingestion but often the choice between which you should pick is not as clear cut. Often there are many factors to consider like cost, ease of implementation, and what your analytics use case is.

So even if you are a super-keen engineer that can’t wait to design a brand new shiny real-time system, make sure you are liaising with product owners, data scientists, and machine learning engineers to make sure you have a strong use case!

Now let’s explore some of the best tools on the market to help you upgrade your data arsenal and take on your next streaming challenge!

Upgrading Your Arsenal: Real-time Data Ingestion Tools

In this section, I will be exploring the best tools on the market for real-time data ingestion to help you level up your real-time data ingestion capabilities 

Overall,  I will try to present a fair and balanced summary of each tool to help you learn more about how they work and what will be best for your particular use case. It is worth noting that all of these tools are attempting to fill the same gap in the market therefore there will be some overlap of features - I will therefore try to focus on the key differentiating factors that might swing you one way or another when selecting a tool.

We will kick things off with a legend of the real-time ingestion industry…

Apache Kafka: The Gold Standard for Real-Time Data Ingestion

Apache Kafka sets itself apart from the crowd as the Gold Standard for real-time data ingestion. It has become such an obvious choice for so many people as it provides a high-throughput, distributed, and fault-tolerant platform for streaming data between applications and systems, and is used by companies such as LinkedIn, Netflix, and Uber to power their data pipelines. These titans of the industry often make good technological choices so is a good litmus test to show how well renowned it is.

Kafka comes with a plethora of benefits that make it a versatile choice to handle any problem you could possibly throw at it. Some of the key benefits can be found below:

High Throughput: Kafka is capable of handling millions of events per second which is frankly absurd. This makes it more than suitable for processing large volumes of data in real time. Its high performance is primarily due to its distributed architecture and efficient message passing.

Scalability: Kafka's distributed architecture allows for seamless horizontal scaling. Systems rarely exist in a steady state and as your data processing needs grow, you can easily add more broker nodes to your Kafka cluster to increase throughput and storage capacity.

Integration & Ecosystem: Kafka integrates seamlessly with other stream processing frameworks, such as Apache Flink or Kafka Streams, enabling you to perform complex event processing, data enrichment, and transformations in real time. 

Further to this, it comes with all the benefits of being in the Apache Ecosystem with a  vibrant community of users and contributors. This means you'll have access to a wealth of resources, such as documentation, tutorials, and third-party tools to help you get the most out of the tool

Overall, Apache Kafka is a really good all-round choice when considering choices for your real-time ingestion needs, and due to its popularity, the knowledge around it is highly distributed meaning that there is a lot of free content available to help you get up and running in the shortest time possible.

Apache Flink: High-Performance Stream Processing

Apache Flink is the Porsche of the real-time ingestion world. It offers low-latency and high-throughput data processing which is highly performant and scalable. It excels at real-time due to its stateful computation and native support for event time which make it a formidable tool when dealing with the minefield that is real-time event processing.

Developed by the Apache Foundation, it has gained significant popularity in recent years for its ability to handle both real-time and batch data processing - now they are just showing off! 

Some of Flink’s best features can be found below:

Unified Stream and Batch Processing: The unique selling point of Flink is its ability to seamlessly handle both stream and batch processing, so if you have an integrated system then Flink can handle that with ease. This massively reduces the headache and effort of maintaining independent systems which makes it a hugely versatile tool.

Event Time Processing: Flink supports event time processing, which enables the accurate handling of out-of-order events and late data. In the real world, this is commonplace in highly dynamic systems like sensors on a car or stock market information.

In summary, Apache Flink is a hugely powerful stream-processing framework that demolishes the competition in many facets. Its unique ability to handle both real-time and batch processing, along with its flexible event-driven processing, makes it an enticing choice for building complex data processing pipelines and real-time ingestion applications.

Amazon Kinesis: Managed Streaming Service for the Cloud

Not all people will be looking to spend large amounts of time and money figuring out these complex real-time ingestion systems internally or having to hire contractors to do that for them. In this scenario, it would be much more beneficial to implement a fully managed solution, and that is exactly what Amazon Kinesis offers. 

Kinesis is a fully-managed, cloud-based solution for real-time data ingestion that sits nicely in the AWS Ecosystem which provides a scalable, real-time ingestion service.  Kinesis is therefore an excellent option for businesses already using AWS services, as it will fit like a jig-saw piece into already existing AWS architectures. 

Some of the key features of Kinesis can be seen below:

Kinesis Data Streams: This component allows you to capture, store, and process data streams from various sources, such as logs, social media feeds, and IoT devices. Kinesis Data Streams can handle terabytes of data per hour, so can take on even the heaviest streaming loads you throw at it.

Fully Managed Service: As an AWS-managed service, Amazon Kinesis handles all the underlying infrastructure, scaling, and maintenance, leaving you free to do the things you love like standing at the coffee machine catching up on the weekend's events!

Security: Amazon Kinesis provides various security features, such as data encryption, IAM roles, and VPC endpoints, to help protect your data streams and comply with industry-specific requirements.

In summary, Amazon Kinesis is an obvious choice for those already heavily integrated into the AWS cloud environment. Its real-time ingestion, processing, and analysis capabilities can handle most use cases, and the added bonus of AWS security features means that you can sleep easily at night.

Google Cloud Pub/Sub: Scalable Messaging and Event Streaming

The next tool on our list is Google Cloud Pub/Sub, which is yet another cloud-based real-time data ingestion tool that offers at-least-once delivery of messages and events. Pub/Sub is based on the publish-subscribe pattern, where producers (publishers) send messages to topics, and consumers (subscribers) receive those messages.

Being a Google product, its global, highly scalable infrastructure allows you to build robust, high-performance data pipelines. Much like Kinesis is to Amazon, Pub/Sub offers easy integration into the Google Cloud ecosystem. Therefore, it’s a great option for those already utilizing Google Cloud Platform.

Some of Pub/Sub’s key features can be seen below:

At-least-once delivery: Pub/Sub ensures at-least-once message delivery, which means that messages are guaranteed to be delivered to subscribers even if a failure occurs during message transmission.

Ordering Guarantees: Although Pub/Sub doesn't guarantee global message ordering by default, it provides ordering keys to ensure the order of messages within a specific key. This feature is useful for applications that require strict message ordering.

Integration: Google Cloud Pub/Sub integrates seamlessly with other popular GCP services, such as Dataflow and BigQuery, making it easy to build end-to-end data processing and analytics applications on the GCP platform.

In summary, Pub/Sub is a no-brainer for anyone already operating in the GCP cloud - it is fully managed and comes with all the benefits of integrations and security associated with this. Further to this, its real-time, asynchronous communication between independent services reduces the headache for developers working with data across disparate systems.

Estuary Flow: A Powerful New Player in the Space

So far many of the tools discussed offer very similar features in terms of what can be achieved by using them. Something that is also very similar is the level of expertise and time required to set them up and start using them. Unfortunately, not all organizations have the time and resources to use these tools effectively, and relying solely on expert engineers to build pipelines can lead to breakdowns in communication and long lead times in getting a perfect product out.

A tool that sets itself apart from the crowd is Estuary Flow. This tool is much more user-focused as it is a managed service that sits on top of Gazette, a custom and open-source Kafka-like streaming broker, and is designed for all members of your team - not just the engineers.

The robust command-line interface equips backend engineers with exceptional data integration capabilities. Simultaneously, Flow enables data analysts and other user groups to actively participate in and manage the same data pipelines, or data flows, through the user-friendly web application.

Some of the key benefits of Estuary Flow are outlined below:

Fully integrated pipelines​: Flow allows you to create, test, and adapt pipelines that consistently gather, modify, and consolidate data across all your systems. By providing a single tool it can simplify some of the trickier tasks around getting pieces of your jigsaw to work nicely together.

Connectors: Estuary Flow provides a variety of pre-built connectors for popular data sources and sinks, such as databases, message queues, and file systems. This massively reduces the need for developers to rebuild custom connectors for every source of data and can reduce the time to get your pipelines up and running. Further to this, the connectors will work in a consistent and predictable way across systems.

Powerful transformations: Flow processes data using stable micro-transactions, ensuring committed outcomes remain unaltered despite crashes or machine failures. This makes Flow ideal for operational tasks like allocating dynamic inventory to request streams—decisions that must be remembered. It is easy to build on these transformations and new datasets or behaviours can be added without needing to start from scratch, again reducing the time and effort needed to add new capabilities.

Overall, Estuary is a really powerful new tool in the industry and would be a good choice for SMEs with less budget and resources available to get complex streaming pipelines set up. The easy-to-use interface that can be used by all members of the data team, ensures that the workload can be shared and there is full visibility on the status of work.

Honourable Mentions

Whilst I have gone through some of the best tools available on the market it is worth noting that this landscape is constantly changing, and there is a vast array of different options. Some tools I didn’t get the luxury of going through in detail are briefly summarized below:

Apache Nifi

Apache Nifi is a powerful open-source tool designed to automate data flow management, making it an excellent option for real-time data ingestion. With its easy-to-use interface and extensive library of processors, Nifi allows you to quickly design, control, and monitor data flows.

Confluent Platform

Built on top of Apache Kafka, the Confluent Platform provides an enterprise-grade solution for real-time data ingestion. It offers additional features such as schema management, security, and monitoring.

Logstash

Part of the Elastic Stack, Logstash is a versatile real-time data ingestion tool that supports a wide range of data sources, filters, and outputs. Its plugin-driven architecture makes it easy to extend and customize.

Conclusion

In conclusion, the world of real-time data ingestion is brimming with incredibly versatile and powerful tools which will help you to create dynamic and fault-tolerant applications. Whilst, many of the tools have overlapping capabilities, it is important to fully scope your requirements and wishes in your project before you select a tool. It is especially important to consider the maintenance requirements and the impact of changing requirements in the future as often highly custom solutions can be expensive to operate and upgrade.

Nevertheless, by carefully evaluating each tool's strengths and aligning them with your unique use cases, you can build a solid foundation for real-time data ingestion and processing, enabling your organization to stay ahead in the competitive landscape and unlock the true potential of real-time analytics.

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Are you looking to implement real-time analytics in your organization? Estuary Flow provides fantastic out-the-box integrations using a no-code technique to reduce the time and effort of this. You can also benefit from advanced features like job scheduling, monitoring, and error handling. Why don't you try it out today?