In today’s data-driven era, a massive amount of data is generated from sensors, IoT devices, applications, social networks, online transactions, and more. To leverage the power of data retrieved from multiple external resources, organizations must constantly monitor and analyze live data through real-time Stream Processing Systems.
There is a wide range of Stream Processing Applications or tools in the market with attractive features and functionalities, allowing Developers to implement real-time streaming operations by writing simple code or using interactive UI.
In this article, you will learn about streaming processing and the best Streaming Data Processing Systems.
Prerequisites
- Fundamental knowledge of real-time Data Streaming.
What are Stream Processing Systems?
Stream Processing is a way of continuously obtaining real-time data in the form of data streams from external data sources. The stream processing method frequently involves performing numerous operations on an incoming series of data (the “data stream“), which can be done serially, in parallel, or both.
Such workflow is collectively called a Stream Processing Pipeline since it encompasses the generation of stream data, processing of the data, and delivering the data to a final destination. Furthermore, real-time stream processing is critical for dealing with massive amounts of live data gathered from a variety of sources, such as online transactions, in-game player actions, sensors, system logs, and much more.
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Top 6 Data Stream Processing Systems
1. Apache Spark
Apache Spark is an open-source unified analytics engine for processing colossal amounts of data to implement real-time streaming operations. It provides a programming interface for clusters with implicit data parallelism and fault tolerance.
Key Features
- Apache Spark uses Machine Learning-enabled Data Analytics modules and supports batch and stream data processing.
- The platform can process enormous volumes of complicated data at rapid speeds and performs workloads 100x quicker than Hadoop. Apache Spark comes pre-configured with an easy-to-use API that simplifies Big Data processing and distributed computing for developers.
- Python, Java, Scala, and SQL are among the programming languages supported by Apache Spark. Furthermore, APIs for Python, R, Java, SQL, and Scala allow you to work in your preferred developer environment.
- You can also integrate Spark with other cluster nodes such as Hadoop YARN, Kubernetes, and Apache Mesos.
Pricing:
- Since Apache Spark is one of the open-source stream processing systems, you can download and get unlimited access to every Spark offerings free of cost.
2. Apache Kafka
Apache Kafka is an open-source and distributed Stream Processing Platform that receives, stores, organizes, and delivers data across various end-users or downstream applications. In other words, Apache Kafka is a community-driven event streaming technology that can handle trillions of events each day. Kafka was originally envisioned as a messaging queue, but it is now built on an abstraction of a distributed commit log.
Key Features of Kafka
- Apache Kafka offers users Kafka Streams, which is a Java API for implementing stream processing operations. This API allows developers to implement data processing techniques like filtering, joining, aggregating, and grouping without writing any code.
- Since it is a Java library, it is simple to integrate with any services you use and transform them into powerful, scalable, fault-tolerant systems.
- Apache Kafka is used in developing both real-time streaming data pipelines and applications. A data pipeline reliably processes and transports data from one system to another, whereas a streaming application consumes data streams.
- For example, if you want to build a data pipeline that takes in user activity data for tracking how people use your website in real-time, you’d use Kafka to ingest and store streaming data while sending requests to the apps that run the data pipeline.
Pricing
- Apache Kafka is open-source, you can download and use it free of cost.
3. Apache Flink
Apache Flink is an open-source streaming data analytics platform that supports batch and stream processing operations. It is mainly designed for computing unbounded and bounded data streams in order to run stateful streaming applications at any scale. With Apache Flink, users can fetch streaming data from a variety of sources, analyze it, and distribute it across several nodes.
Key Features
- Flink’s UI is significantly easier for anyone to use and requires less technical knowledge.
- Apache Flink is capable of integrating with all popular cluster resource managers, including Hadoop YARN, Apache Mesos, and Kubernetes, but it may also be configured to run as a standalone cluster.
- Furthermore, Flink is intended to operate well with all of the previously mentioned resource managers. This is accomplished through resource-manager-specific deployment modes, which enable Flink to communicate with each resource manager.
- With Apache Flink, users can build applications and then parallelize them into thousands of jobs that are distributed and executed concurrently in a cluster.
- As a result, an application can utilize an almost infinite number of CPUs, main memory, disc, and network IO.
- Furthermore, Flink can handle very large application states since its asynchronous and incremental checkpointing methodology minimizes processing latencies while preserving exact-once state consistency.
Pricing
- Since Apache Flink is an open-source solution, you can download and use all its offerings free of cost.
4. Amazon Kinesis
Launched in 2013, Amazon Kinesis is one of the most popular suites of AWS that is primarily dedicated to performing analytics operations on real-time streaming data. Amazon Kinesis makes it simple to gather, organize, process, and analyze real-time streaming data, including video, audio, application logs, website clickstreams, and IoT telemetry, allowing users to gain timely insights and respond swiftly to new information.
Key Features
- Since Amazon Kinesis is fully managed, users can quickly run streaming applications without having to manage any infrastructure.
- Furthermore, with its highly scalable feature, Amazon Kinesis can also manage any amount of streaming data and analyze data from hundreds of thousands of external sources with very low latency rates.
- Users can utilize Amazon Kinesis to create real-time applications such as application monitoring, fraud detection, and live leaderboards.
- With millisecond end-to-end latency, you can ingest streaming data with Kinesis Data Streams, process it with Kinesis Data Analytics, and publish the results to any data storage or application with Kinesis Data Streams.
- This might assist users in learning about what their customers, apps, and products are doing right now and react appropriately based on the received feedback.
Pricing
- The pricing of Amazon Kinesis varies based on your AWS region. You can use the AWS Pricing Calculator to estimate the cost of Amazon Kinesis based on your requirements and use cases.
5. Azure Stream Analytics
Developed by Microsoft, Azure Stream Analytics is one of the popular real-time stream processing systems that allows customers to analyze and process massive amounts of real-time data such as stock trading, credit card fraud detection, Web clickstream analysis, social media feeds, and much more.
Key Features
- Azure Stream Analytics is intended to be simple to use, flexible, and scalable to any workload size and is capable of routing task output to a variety of storage systems, including Azure SQL Database, Azure Blob storage, Azure CosmosDB, and Azure Data Lake Store.
- With Azure Stream Analytics, users can gather information from a variety of input sources, including devices, clickstreams, sensors, social media feeds, and applications, which can be used to identify real-time patterns and relationships.
- These patterns can be further used to launch workflows and trigger activities such as issuing alerts, feeding data to a reporting platform, or storing altered data for later use.
- Because Azure Stream Analytics is a fully managed Platform-as-a-Service (PaaS) solution, users do not need to maintain any hardware or resources to do analytics.
- It also leverages Stream Analytics Query Language, a T-SQL variation, so users with SQL backgrounds may quickly adapt to working with ASA.
- The T-SQL query language-based query is used to easily filter, sort, aggregate, and merge streaming data over time and can also be extended with JavaScript and C# user-defined functions (UDFs).
Pricing
- The price of Azure Stream Analytics is determined based on the number of Streaming Units you use for implementing stream processing tasks.
- You can use the Azure Pricing Calculator to estimate the exact cost based on the streaming units.
6. Google Cloud DataFlow
Google Cloud Dataflow is a cloud-based data streaming and processing service that allows users to do real-time and batch data processing activities. It is a fully managed service that allows pipelines to be executed within the Google Cloud Platform environment. Google Cloud Dataflow service is dedicated entirely to data transformation and enrichment in both stream (real-time) and batch (historical) modes.
Key Features
- Since Google Cloud Dataflow is a serverless solution, users can focus on programming rather than managing server clusters.
- When you execute a job on Cloud Dataflow, it creates a cluster of virtual computers, distributes the tasks in your job across the VMs (Virtual Machines), and dynamically expands the cluster based on how well the job performs.
- To optimize your job, it may even rearrange the order of processes in your processing pipeline.
- Google Cloud Dataflow’s autoscaling feature assists users in minimizing pipeline delay, optimizing resource utilization, and lowering processing costs per data record.
- With Google Cloud Dataflow, data inputs are dynamically partitioned and rebalanced in real-time to equal worker resource use and lessen the impact of “hotkeys” on pipeline performance.
Pricing
- The pricing of Google Cloud Dataflow is based on the overall sources you use to implement stream processing tasks, such as vCPUs, memory, and storage.
- You can refer to the pricing page of Google Cloud Dataflow to know more about their resource pricing details.
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Conclusion
In this article, you learned about Stream Processing and the best Stream Processing Systems. This article mainly focused on explaining the features and benefits of 6 Processing Systems.
However, there are many other open-source stream processing tools with attractive features and functionalities. You can explore and learn about other Processing Systems to get hands-on with real-time Data Streaming solutions based on your requirements and use cases.
FAQs Related to Stream Processing Systems
1. What is the difference between stream processing and OLAP?
Stream processing handles real-time data as it flows in, enabling immediate analysis and action. At the same time, OLAP (Online Analytical Processing) focuses on complex queries and analysis of historical data in a more structured, batch-oriented manner.
2. How is stream processing different from a database?
Stream processing handles continuous, real-time data flows for immediate processing and analysis, while a database stores data in a structured format, allowing for queries and retrieval of static, stored information rather than real-time events.
3. What is an example of stream data processing?
An example of stream data processing is analyzing real-time sensor data from smart devices in an IoT (Internet of Things) network. For instance, a system could process temperature readings from thousands of sensors in a smart factory, detecting anomalies and triggering alerts or automated actions instantly when specific thresholds are exceeded.
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Ishwarya is a skilled technical writer with over 5 years of experience. She has extensive experience working with B2B SaaS companies in the data industry, she channels her passion for data science into producing informative content that helps individuals understand the complexities of data integration and analysis.
