Choosing A Complex Event Processing Engine: Macrometa Vs Apache Spark And Apache Flink
Digital experiences are the new normal, and understanding both customers and their intent is paramount for businesses to stay ahead of the competition. Consumers are increasingly transacting online for everyday experiences, from telehealth appointments to digital banking, online shopping, and even attending virtual events. Every online transaction can be thought of as an event, and organizations are capturing, comparing, and analyzing these events from a variety of sources to predict customer behavior and improve their experiences.
Complex Event Processing (CEP) isn’t trivial and there are a variety of solutions out there that claim to be the best, but how do you choose the right one for your use case? In this blog, we will introduce Macrometa, the new kid on the block in the world of CEP (you can even sign up and try it out - in minutes), and see how it stacks up against two mature offerings for streaming analytics, Apache Spark and Apache Flink. We will provide a high-level overview of CEP in the next section, but check out the chapter comparing Spark vs Flink to learn more about the two stream analytics platforms.
Understanding Complex Event Processing vs Event Stream Processing
Before we continue, let’s set the record straight on the differences between CEP and event stream processing (ESP), also known as streaming analytics. In short, CEP can be considered as a more complex version of ESP and generally involves detecting patterns or correlations between multiple incoming streams of events that may or may not arrive in the order they were sent, to derive real-time insights.
An example of ESP would be comparing the number of items added to Amazon shopping carts with the number of items that were actually ordered, to ultimately improve the order completion percentage over time. With CEP, you may correlate the zip codes of customers with items in their shopping carts with national weather data to notify customers that deliveries may be delayed. Aggregating a variety of events, both real-time and historical, allows you to more accurately forecast delivery estimates, providing an improved customer experience.
Hello World, Meet Macrometa
Macrometa is a Global Data Network (GDN) offering a Global Data Mesh, Edge Compute and a CEP engine with native Data Protection. Within the Macrometa GDN is an incredibly powerful component called Stream Workers which help enable CEP use cases. Macrometa’s CEP capabilities are similar to those of Spark and Flink, but we will go into more detail about how Macrometa can help simplify your architecture and expedite your deployments. Take a look at the chart below that provides a high-level overview of the three data processing engines.
Macrometa, Spark, and Flink feature comparison
Macrometa vs. Spark and Flink
Stream Workers are only one component of the Macrometa GDN and work seamlessly with the rest of the platform to expedite and simplify the creation of event-driven architectures. To put this into context, imagine how much time and expertise it would take to write stream processing jobs to aggregate a real-time stock quoting stream via a variety of intervals (by second, minute hour, day, etc.) in Spark or Flink. You’ll need to specify and integrate a backend datastore for persistence, then repeat the setup process within multiple regions as close to your end users as possible for low latency. This could take several months! With Macrometa, you could deploy a local or global Stream Worker to process and aggregate the data in as little as five minutes.
Check out the code snippet below to see how easy it is to aggregate, process, and store the data within a Macrometa database.
-- Sink stream to publish trade data. CREATE SINK STREAM TradeStream (symbol string, price double, volume long, timestamp long); -- Trade average, sum incremental aggregation query. CREATE AGGREGATION TradeAggregation WITH(store.type='database', purge.enable='true', purge.interval='10 sec', purge.retentionPeriod.sec='120 sec', purge.retentionPeriod.min='24 hours') SELECT symbol, avg(price) AS avgPrice, sum(price) AS total FROM TradeStream GROUP BY symbol AGGREGATE BY timestamp EVERY sec ... year;
Save Time with Operational Simplicity
Today’s challenges increasingly require solutions that can process, analyze, and act on data in real-time. Detecting fraudulent credit card transactions for a global customer base is one such challenge that can be addressed with a Complex Event Processing engine like Macrometa, Spark or Flink. With low-latency being critical for anomaly detection, a centralized architecture simply won’t work. Instead, you will need to have a processing engine deployed in a variety of regions as close to your customer base as possible.
If operational simplicity is important to you, then you’ll love Macrometa due to its geo-distributed architecture. You can choose to deploy Stream Workers into one or more PoPs around the world at the click of a button, ensuring data will be processed as close to where it is created as possible, while at the same time abiding by any data sovereignty restrictions. On the topic of regulatory requirements, you can geo-fence your data to ensure it stays within a particular region as well as anonymize/tokenize it to ensure access is only granted to those for whom it is intended.
Accelerate Time to Market With Developer Velocity
When processing real-time streams of data, your system will generally contain three components; a source (or sources) where the data originates, a stream processor which will analyze the data (cleanse, transform, enrich, correlate, etc.), and finally a destination where the newly processed data is consumed. Spark and Flink serve as the stream processing engines, thus it will be up to your development teams to specify and integrate the tools for the source and destination.
Macrometa on the other hand is a complete development platform that can dramatically simplify your architecture, unleashing developer velocity to focus on building great products rather than integrating disparate systems. Stream Workers can be thought of as an application blueprint, specifying data ingestion, processing your data, and finally writing to a collection in whatever data model makes sense for your use case. Oh, and if you need to index or search on your data, Macrometa can do that too.
Get Started Today
Macrometa GDN, Spark, and Flink are all fantastic stream processing engines, and which tool is right for you depends on your goals and needs. Spark is more mature, while Flink is likely more suitable for CEP due to improved latency and performance. If a managed service is more your thing, Databricks may be able to help reduce complexity of your Spark jobs.
However, if you need to process globally distributed events with as little latency as possible, or want to reduce your time to market and unleash developer velocity, Macrometa is absolutely the way to go.
Macrometa offers real-time, stateful CEP close to where the events are occurring - where your customers are creating data - to provide fast, relevant insights.
Want to see for yourself? Be sure to watch our CEP on-demand webinar to find out more! Sign up for an account (try it out for free) and check out this tutorial blog to quickly begin setting up your first Stream Worker.
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- Batch or real-time stream processing within 50ms of the global population
- Fault tolerant by design and integrated with a Global Data Mesh
- Multiple windowing options: sliding, tumbling, and count, etc.
- Query any data store directly in your code without any configuration