How to Choose a Stream Processor for Your Data
Choosing the right stream processor for your data is no easy decision. Learn about the different options and the pros and cons of each system in this guide.
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Data has become integral to most organizations. So it's no wonder that stream processing has become a critical part of big data stacks. This works wonders for consolidating and interpreting large amounts of data.
There are many end-to-end solutions available for streaming data pipelines in the cloud. Not to mention many terminologies to navigate the different stream processing tools to choose from.
The right solution will also heavily depend on your end-user and their requirements. With so much to consider, it's challenging to find the right framework for your organization.
So, in this guide, we're shedding some light on what to consider when choosing a stream processor for your data.
Let's dive in!
What Is Stream Processing?
Stream processors enable users to react to continuous data streams quickly. They also detect various conditions in milliseconds.
Stream processors are required for any application in which an immediate reaction to real-time data is needed. For example, temperature sensors will give an alert as soon as a specific temperature is hit.
As such, stream processing is often synonymous with real-time analytics. It allows users to ingest, process, and analyze data as it comes in without much delay.
You can find a more in-depth introduction to stream processing here.
DIY vs. Managed Stream Processors
When choosing a suitable stream processor, you can build the app yourself or select an existing tool. Using existing stream processing architecture saves you time and money and avoids inefficiencies in the infrastructure.
This is especially handy if you're inexperienced with setting up similar applications. But, there are many stream processing frameworks to choose from. So, before looking at different engines, make a list of features your framework needs to support.
Essential features for stream processing systems include:
- Data ingestion with a message broker
- Writing queries with streaming SQL
- A stream processing API and query writing environment
- High availability (HA), a minimal HA, and high reliability
- Streaming machine learning
- Message processing guarantees
- Out-of-order events
- Large scale system performance (Does the framework scale? Can it handle large windows?)
- User-friendliness with drag-and-drop GUIs
First, make a list of must-have features. Then, list your optional features. This will guide you as you search for the best stream processor for your data.
Different Types of Stream Processors
You'll also need to determine the correct type of stream processing engine. There are three major types:
1. Open-Source Compositional Engines
Compositional stream process engines rely on the early definition of the Directed Acyclic Graph (DAG). This occurs before the data is processed.
While this simplifies code, developers must carefully plan their framework to avoid inefficiencies in the processing.
These engines are considered the first generation of stream processors and are often complex to manage. Examples of open-source compositional engines include Apache Storm, Samza, and Apex.
2. Managed Declarative Engines
These engines can chain stream processing functions. As such, the engine calculates the DAG as it takes in data and can optimize the DAG while it's running.
This type of stream processing engine is easier to manage and comes with a range of managed service options. But, the initial setup of the pipeline remains an expensive investment.
Costs pertain to everything from source to storage and analysis. Both Apache Spark and Flink are declarative engines with managed services.
3. Fully Managed Self-Service Engines
Finally, there are fully managed self-service engines. These are the newest development in stream processing.
This engine operates the DAG and offers end-to-end solutions that include streaming data straight into the storage infrastructure.
The fully managed engine also organizes the data and feeds it into the analytics framework.
Setup, Management, and Administration
Different stream processors come with varying setup, management, and administration requirements. This is another important consideration before choosing the right tool.
To illustrate this, we may compare Amazon Kinesis vs Kafka.
Apache Kafka, for example, may take days or even weeks before a complete production-ready requirement can be set up.
The length of the process depends on the expertise available on your team. Plus, the framework is an open-source system requiring its own:
- Many nodes
Managed services are much faster to set up. In addition, they can be operational within hours because the provider will manage the infrastructure, storage, networking, and configurations.
In other words, everything you need to stream data will be available to you in a short amount of time.
Managed services will also take care of ongoing maintenance, provisioning, and deployment of the hardware and software.
Costs and Pricing Models
The pricing model associated with different stream processor types is a final consideration for choosing stream processors for your data.
Open-source solutions often need significant technical resources. Your organization may be responsible for funding the setup and the 24/7 operational burden of managing the infrastructure.
You will need to fund dedicated hardware, as well. In comparison, fully-managed services often offer pay-as-you-go pricing models. You don’t have to invest in upfront costs for setup.
The amount paid may depend on the number of standard shards needed for the throughput. In this model, you can save the time and monetary expense of setting up the infrastructure.
Choose the Right Stream Processor for Your Data
It can be challenging to understand stream processing and all of its requirements.
But, we hope this article has helped you focus on the most important considerations for choosing a stream processing engine. Many tools are on the market, and managed services often pose the most modern and flexible solutions.
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