With a bunch of real-time data streaming tools and technologies available today, businesses are spoilt for choice. Processing huge amounts of data has now become as easy as snapping your fingers.

Take Twitter for example: millions of people tweet, retweet, like, and comment every waking hour of the day. And we get to react, respond, witness, and participate in real time!

And why just Twitter, real-time data streaming tools, and technologies made a sizable impact across industries and across the world? From social media to healthcare, from retail to the energy sector, real-time data processing has transformed the way enterprises run their businesses. Any business not utilizing real-time data processing tools risks a massive setback in the foreseeable future.

Did you know that Netflix saved roughly $1 billion by using data streaming platforms?

Although a tiny blog article can hardly cover the expansive topic at hand, in this article we’ll try to cover the basics as best as possible, an overview, if you will.

So, let’s dive straight in!

What is Real-time Data Processing?

Real-time data processing is accurately processing huge amounts of rapidly changing data in a relatively short period of time. Although it can also be in real-time where required (for example: stock market), real-time data processing also comprises cases where a slight delay of a few seconds (or even a few minutes) is involved (for example: Google maps). It deals with data captured in real-time and can provide an automated response based on the streams of data received.

Real-time Data Processing Tools and Technologies

There are a number of Real-Time Data Streaming Tools available today.

Some tools are better suited than others for your specific business needs. So, a thorough analysis of the business needs is required to ensure you opt for the most apt tool for your organization. Choosing the right stack for you will dictate how much you will really gain by going real-time. These different technologies were built for specific use cases. Understanding your use case well and what use case each technology was built for is required before you make up your mind and kick-start implementation. 

The right tool can help you save time, costs, and other resources and drive your business upwards, the wrong tool not so much maybe!.

Here’s an overview of the best real-time data processing tools available today:

Flink

Ideal for low-latency applications, Flink is an open-source, unified stream-processing and batch-processing framework developed by the Apache Software Foundation. 

Written in Java and Scala, Flink executes arbitrary dataflow programs in a data-parallel and pipelined manner making it ideal for processing huge amounts of data in real-time.

Advantages:

• Flink provides out-of-the-box checkpointing and state management, two features that make it easy to manage enormous amounts of data with relative ease.
• The event processing function, the filter function, and the mapping function are other features that make handling a large amount of data easy.
• Flink also comes with real-time indicators and alerts which make a big difference when it comes to data processing and analysis.

Disadvantages:

• Incompatibility with all machine learning languages
• Less than impressive reporting system

Kafka

• Kafka has been able to establish itself as a favorite, go-to enterprise event streaming platform of many. Boasts of being used by more than 80% of Fortune 100 companies. It is widely adopted as an enterprise message broker.  Kafka is a user-friendly open-source software platform written in Scala and Java. Its function is to provide a unified, high-throughput, low-latency platform for handling high-velocity and high-volume real-time data feeds.

Advantages:

• Kafka is able to support message throughput of thousands of messages per second.
• One of the best advantages of Kafka is its Fault Tolerance. It is inherently resistant to node/machine failure within a cluster.
• Kafka is extremely flexible when it comes to integrating it with a number of consumers written in a variety of languages.
• Kafka is excellent at handling real-time data pipelines.

Disadvantages:

• Slightly lower efficiency when the queue increases, 
• Does not support wildcard topic selection
• For certain cases, some of the messaging paradigms are missing in Kafka, such as request/reply and point-to-point queues

Apache Spark

Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

Advantages:

• Widely popular owing to its blazing speed, Apache Spark is way faster than Hadoop for large-scale data processing. Apache Spark uses an in-memory (RAM) computing system allowing it to handle multiple petabytes of clustered data of more than 8000 nodes at a time.
• Carries easy-to-use APIs for operating on large datasets. It offers over 80 high-level operators that make it easy to build parallel apps.
• Supports many languages for code writing such as Python, Java, and Scala.
• Has a huge community for support. 

Disadvantages:

• Doesn’t have any automatic code optimization process
• Dependent on other platforms for file management system
• Does not support record-based window criteria.

Apache Spark Streaming

Apache Spark Streaming is a scalable fault-tolerant streaming processing system that natively supports both batch and streaming workloads. Spark Streaming is an extension of the core Spark API that allows data engineers and data scientists to process real-time data from various sources including Kafka and Amazon Kinesis.

Advantages:

• Great for solving complicated transformative logic
• Easy to program
• Runs at blazing speeds
• Processes large data within a fraction of seconds

Disadvantages:

• Inadequate documentation.
• Could have been simpler to use
• In-memory processing consumes massive memory

Apache Samza

Apache Samza is an open-source, near-real-time, asynchronous computational framework for stream processing developed in conjunction with Apache Kafka.

Advantages:

• Works well with virtual host implementation
• Tutorials are easy-to-read and documentation is intuitive 
• Several examples available online owing to the widespread popularity
• Offers extensive configuration options
• Compatible with third-party modules
• Implementation as a load balancer is particularly good

Disadvantages:

• Certain modules tend to be difficult to install
• Not as flexible as new alternatives

Amazon Kinesis

Amazon Kinesis Data Streams (KDS) is a massively scalable and durable real-time data streaming service. KDS can continuously capture gigabytes of data per second from hundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. The data collected is available in milliseconds to enable real-time analytics use cases such as real-time dashboards, real-time anomaly detection, dynamic pricing, and more.

Advantages:

• Reads and processes data in real time.
• Ideal for scalable projects – provides high performance and potential for very large projects
• Allows analyzing and returning real-time data to users quickly
• Intuitive and compatible with other tools.

Disadvantages:

• Confusing terminology used
• Issues with Lambda functions

Did you know that both Apache Kafka and Apache Samza were originally developed by LinkedIn?

Apache NIFI

Apache NiFi is a popular real-time data ingestion platform, which can transfer and manage data transfer between different sources and destination systems.

Advantages:

• Enables data fetching from remote machines and guarantees data lineage.
• Supports clustering, allowing it to work on multiple nodes with the same flow processing different data. 
• Provides security policies on several levels. 
• Can also run on HTTPS, making it secure. 
• NiFi supports around 188 processors and allows users to create custom plugins to support a wide variety of data systems.
• Supports a wide variety of data formats like logs, geo-location data, social feeds, etc. It also supports many protocols

Disadvantages:

• The file flow.xml becomes invalid when a node gets disconnected from a NiFi cluster while a user is making any changes in it, which needs to be fixed manually. 
• Has state persistence issue in case of primary node switch.

Google Cloud Dataflow

One of several Google data analytics services, Google Cloud’s Dataflow is a fully managed cloud service and programming model that lets users ingest, process, and analyze fluctuating volumes of real-time data with low latency. 

Advantages:

• Enables fast, simplified streaming data pipeline development with lower data latency.
• Economical
• Equipped with ready-to-use real-time AI patterns
• Allows flexible scheduling and pricing for batch processing
• Can dynamically adjust the compute capacity allocated to each worker based on utilization.

Conclusion

It is clear that organizations today need real-time data streaming tools, an efficient data management and analytics platform suited to their business requirements. They need a system in place that can collect, process, manage, and analyze data in real-time to drive insights and enable machine learning, all the while ensuring robust security and data protection. If an organization wants to succeed, it needs to adopt real-time data processing tools and technologies suitable for its line of business. It is also imperative that they make the right choice, as each real-time data processing tool has its pros and cons. 

At Akrity, we work with many real-time data processing tools and technologies. We have a thorough understanding of all the tools available today, which allows us to accurately map the tools according to your business needs. If you have a business looking to utilize real-time data processing and you want an opinion, or even help deciding which tool or technology will be the best fit for your business, give us a call. We’d be more than happy to discuss this at length.