The world generates an unfathomable amount of data, and it continues to multiply at a staggering rate. Companies have quickly shifted from batch processing to data streams to keep up with the ever growing amounts of big data. In this article, we’ll cover what data streaming is, how it differs from batch processing, and how your organization can benefit from real-time streams of data.
Stream processing, also known as data streaming, is a software paradigm that ingests, processes, and manages continuous streams of data while they're still in motion. Data is rarely static, and the ability to empower data as it's generated has become crucial to the success of today's world.
Modern data processing has progressed from legacy batch processing of data towards working with real-time data stream processing. Similarly, consumers now stream data like movies on Netflix or songs on Spotify instead of waiting for the entire movie or album to be downloaded. The ability to process data streams in real-time is a key part in the world of big data.
Read on to learn a little more about how stream processing helps with real-time analyses and data ingestion.
Legacy infrastructure was much more structured because it only had a handful of sources that generated data and the entire system could be architected in a way to specify and unify the data and data structures.
Modern data is generated by an infinite amount of sources whether it’s from hardware sensors, servers, mobile devices, applications, web browsers, internal and external and it’s almost impossible to regulate or enforce the data structure or control the volume and frequency of the data generated. Applications that analyze and process data streams need to process one data packet at a time, in sequential order. Each data packet generated will include the source and timestamp to enable applications to work with data streams.
Applications working with data streams will always require two main functions: storage and processing. Storage must be able to record large streams of data in a way that is sequential and consistent. Processing must be able to interact with storage, consume, analyze and run computation on the data.
This also brings up additional challenges and considerations when working with data streams. Many platforms and tools are now available to help companies build streaming data applications.
All industries that are generating data continuously will benefit from processing streaming data. The use cases typically start from internal IT systems monitoring and reporting like collecting the data streams generated by employees interacting with their web browser and devices and the data generated by its applications and servers. The operations of the company and its products benefit from data stream processing of sensors, equipment, data centers and many more sources.
Since its customers and partners also consume and process streaming data, the ability to send, receive, process streaming data becomes increasingly important. As more companies rely on its data, its ability to process, analyze, apply machine learning and artificial intelligence to streaming data is crucial.
The key differences in selecting how to house all the data in an organization comes down to these considerations:
###Here’s a breakdown of major differences between batch processing, real-time data processing, and streaming data:
| Batch Data Processing | Real-Time Data Processing | Streaming Data | |
|---|---|---|---|
| Hardware | Most storage and processing resources requirement to process large batches of data. | Less storage required to process the current or recent set of data packets. Less computational requirements. | Less storage required to process current data packets. More processing resources required to “stay awake” in order to meet real-time processing guarantees |
| Performance | Latency could be minutes, hours, or days | Latency needs to be in seconds or milliseconds | Latency must be guaranteed in milliseconds |
| Data set | Large batches of data | Current data packet or a few of them | Continuous streams of data |
| Analysis | Complex computation and analysis of a larger time frame | Simple reporting or computation | Simple reporting or computation |
확장성: 시스템 장애가 발생하면 각 장치에서 생성되는 로그 데이터가 증가할 수 있는데 이는 킬로비트/초에서 메가비트/초의 속도로 전송되고 기가비트/초의 속도로 집계됩니다. 애플리케이션의 확장에 따라 더 많은 용량, 리소스, 서버를 추가하는 작업이 즉각적으로 이루어지며 생성되는 원시 데이터의 양이 급격하게 증가합니다. 규모에 맞는 애플리케이션을 설계하는 것은 스트리밍 데이터에 대한 작업 시 굉장히 중요합니다.
순서 지정: 데이터 스트림에서 데이터의 순서를 결정하는 것은 사소한 일이 아니며 많은 애플리케이션에서 아주 중요한 작업입니다. 채팅이나 대화는 순서가 맞지 않는 경우 의미가 없습니다.
개발자가 집계된 로그 보기를 확인하여 문제를 디버깅할 때는 각 행이 순서대로 되어 있는지가 아주 중요합니다. 때때로 생성된 데이터 패킷의 순서와 목적지에 도달한 데이터 패킷의 순서가 다를 수 있습니다. 또한 데이터를 생성하는 장치의 시계와 타임스탬프에 차이가 있는 경우도 종종 있습니다. 데이터 스트림을 분석할 때 애플리케이션은 ACID 트랜잭션에 대한 가정을 알고 있어야 합니다.
일관성 및 내구성: 데이터 일관성과 데이터 액세스는 데이터 스트림 처리에서 언제나 어려운 문제입니다. 특정한 시간에 읽은 데이터가 다른 지역의 또 다른 데이터 센터에서는 이미 수정된 부실 데이터일 수 있습니다. 또한 클라우드에서 데이터 스트림에 대한 작업을 할 때 데이터 내구성이 문제가 될 수 있습니다.
내결함성 및 데이터 보장: 이는 데이터, 스트림 처리 또는 분산 시스템에 대한 작업 시 중요한 고려사항입니다. 데이터의 소스, 위치, 형식, 규모가 다양한 상황에서 시스템이 단일 장애 지점으로 인한 중단을 방지할 수 있습니까? 높은 가용성과 내구성을 유지하면서 데이터 스트림을 저장할 수 있습니까?
Built by the original creators of Apache Kafka®, the most popular stream processing framework, Confluent enables stream processing on a global scale.
By integrating historical and real-time data into a single, central source of truth, Confluent makes it easy to empower modern, event-driven applications with a universal data pipeline and real-time data architecture. Unlock powerful new use cases with full scalability, performance, and reliability.