Real-time analytics can keep you up-to-date on what’s happening right now, such as how many people are currently reading your new blog post and whether someone just liked your latest Facebook status. For most use cases, real-time is a nice-to-have feature that won’t provide any crucial insights. However, sometimes real-time is a must.

Let’s say that you run a big ad agency. Real-time analytics can keep you posted on whether your latest online ad campaign—that your client paid tons of money for—is actually working, and if not, you can make immediate changes before the budget gets spent any further. Another use case is providing real-time analytics for your own app—it looks good, and your users may require it.

Real-time analytics can keep you up-to-date on what’s happening right now, such as how many people are currently reading your new blog post and whether someone just liked your latest Facebook status. For most use cases, real-time is a nice-to-have feature that won’t provide any crucial insights. However, sometimes real-time is a must.

Let’s say that you run a big ad agency. Real-time analytics can keep you posted on whether your latest online ad campaign—that your client paid tons of money for—is actually working. And if it’s not, you can make immediate changes before the budget gets spent any further. Another use case is providing real-time analytics for your own app. After all, doing so looks good, and your users may even require it.

There are quite a few real-time platforms out there. A lot of them are newcomers, and the differences between them aren’t clear to everyone. The least we can do is present all the options for you to choose from, so here are five real-time streaming platforms to use for Big Data.

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Table of Contents

  1. Apache Flink
  2. Apache Spark
  3. Apache Storm
  4. Apache Samza
  5. Amazon Kinesis
  6. Enterprise Solutions

1. Apache Flink

Apache Flink is an open-source streaming platform that’s extremely fast at complex stream processing. In fact, it’s able to process live streams within milliseconds because it can be programmed to only process new, changed data as it goes through rows of big data in real-time. In this way, Flink easily enables the execution of batch and stream processing at a large scale to offer real-time insights, so it’s no wonder this platform is known for offering low latency and high performance 

Another feature that Flink is known for is fault tolerance, meaning system failure won’t affect the whole cluster. It’s also designed to run in any cluster environment while completing computations, making it a reliable, fast solution that happens to scale easily as needed. The addition of exactly-once semantics and the presence of predefined operators can help with real-time processing on this platform.

Note that Flink can process streams of events as either bounded or unbounded data sets. With unbounded streams, there’s no defined end and can consistently be processed. On the other hand, bounded streams of events will be processed as a batch and have a defined beginning and end. This offers some flexibility, as does the fact that programs can be written in a variety of languages, such as Python, Scala, SQL, and Java. Finally, Flink is known for its ease of use and easy integration with other open-source big data processing tools, such as Kafka and Hadoop.

2. Apache Spark

Another open-source data processing framework that’s known for its speed and ease of use is Spark. This platform runs in-memory on RAM on clusters and isn’t tied to Hadoop’s MapReduce two-stage paradigm, which adds to its lightning-fast performance when it comes to big data processing.

Not only can it complete processing tasks on large data sets with ease, but it can also distribute them across several computers. Plus, it can create data pipelines, work with data streams and graphs, and more. This is why it’s one of the leading real-time streaming platforms for everything from batch processing and machine learning to large-scale SQL and streaming big data. In fact, companies like Intel, Yahoo, Groupon, Trend Micro, and Baidu are already relying on Apache Stream.

Spark can run on either the standalone cluster mode or on top of Hadoop YARN, where it can read data directly from HDFS. It can also run on EC2, Mesos, Kubernetes, the cloud, and more. Additionally, Spark users can write applications easily in Python, SQL, R, Scala, or Java, making it versatile and easy to work with. These features are why Spark is among the top real-time streaming platforms today.

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3. Apache Storm 

Storm is a free distributed real-time computation system that strives to do for streaming what Hadoop has done for batch processing. In other words, it’s a simple solution to use for processing unbounded streams of big data. Some of the big brands that use Storm include Spotify, Yelp, and WebMD.

One of the big benefits of Storm is that it was designed to be used with any programming language, offering a lot of flexibility to users. In addition, there are several use cases that include real-time analytics, machine learning, ETL, continuous computation, and more. And like many of the best real-time streaming platforms these days, it’s fast, ensuring big data gets processed within milliseconds.

Some other facts to know about Storm is that it’s fault-tolerant, scalable, and easily integrates with technologies you might already be using. In particular, it runs on top of Hadoop YARN and can be used with Flume to store data on HDFS. So in using Storm, you can expect your data to be processed quickly on a platform that’s easy to set up and use, no matter what programming language you prefer.

4. Apache Samza

Samza is an open-source distributed stream-processing framework that lets users build applications that can process big data in real-time from several sources. It’s based on Apache Kafka and YARN, but it can also run as a standalone library. LinkedIn originally developed Samza, but since then, other big brands have started using it—such as eBay, Slack, Redfin, Optimizely, and TripAdvisor.

Samza provides a simple call-back-based API that’s similar to MapReduce, and it includes snapshot management. It also offers fault tolerance in a durable and scalable way, as well as stateful processing and isolation. One feature that really sets it apart from other batch systems—such as Spark or Hadoop—is that it offers continuous computation and output, allowing it to be extremely fast when it comes to its response times.

Overall, Samza is known for offering very high throughput and low latencies for super-fast data analysis. This makes it a popular choice among the many platforms built for dealing with big data.

 

5. Amazon Kinesis

Kinesis is Amazon’s service for the real-time processing of streaming data on the cloud. This analytics solution is able to avoid the batch-processing issues that tools like Hadoop have. Because of that, Kinesis is better able to offer real-time precision when it comes to big data processing, as it can handle up to hundreds of terabytes of data every hour. 

The features of this service make it possible for you to develop applications that require real-time data. After all, with Kinesis, you can use this service to ingest, buffer, and process your data immediately, whether it’s video, audio, website clickstreams, or other media. You don’t have to wait for all your data to be collected first, as it can be processed as it arrives. This allows you to get analytics for AI, machine learning, and more within minutes. Kinesis is scalable, as well, as it can handle large amounts of streaming data from numerous sources with low latencies.

In addition, Kinesis is integrated with other Amazon services via connectors, including Redshift, S3, DynamoDB, for a complete big data architecture. This tool also includes the Kinesis Client Library (KCL), which lets you build applications and use streaming data for dashboards, alerts, or even dynamic pricing.

Enterprise Solutions

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The big firms don’t just sit and twiddle their thumbs while Big Data keeps growing. IBM InfoSphere Streams, Microsoft StreamInsight, and Informatica Vibe Data Stream are just a few of the commercial enterprise-grade solutions that are available for real-time processing. To handle all of this real-time data, you need a data integration tool that can pull, push, and transform your data correctly and efficiently, and that’s what Xplenty can give you. We even have change data capture (CDC) on our roadmap for you to try. If you want to know more about what we offer, insert your best email address below. And be sure to contact Xplenty for a demo with our team and a free 14-day pilot on our platform!