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Batch Processing vs Real Time Data Streams

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.

Intro to Stream Processing

What is Stream Processing?

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.

How Data Streaming Works

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.

Batch Processing vs Real-Time Streaming - What's the Difference?

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.

Key Differences and Considerations

Batch Processing vs Real-Time Streaming - What's the Difference?

The key differences in selecting how to house all the data in an organization comes down to these considerations:

  • Batch processing is when the processing and analysis happens on a set of data that have already been stored over a period of time. An example is payroll and billing systems that have to be processed weekly or monthly.
  • Streaming data processing happens as the data flows through a system. This results in analysis and reporting of events as it happens. An example would be fraud detection or intrusion detection. Streaming data processing means that the data will be analyzed and that actions will be taken on the data within a short period of time or near real-time, as best as it can.
  • Real-time data processing guarantees that the real-time data will be acted on within a period of time, like milliseconds. An example would be for-real time application that purchases a stock within 20ms of receiving a desired price.

###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

Many companies are finding that they need a modern, real-time data architecture to unlock the full potential of their data, regardless where it resides. Where some real-time data processing is required for real-time insights, persistent storage is required to enable advanced analytical functions like predictive analytics or machine learning. This is where a full-fledged data streaming platform comes in.

Herausforderungen bei der Entwicklung von Daten-Streaming-Anwendungen

Die grĂ¶ĂŸten Herausforderungen bei der Entwicklung von Echtzeit-Anwendungen

Skalierbarkeit: Bei SystemausfĂ€llen können die von den einzelnen GerĂ€ten kommenden Protokolldaten von einer Übertragungsrate von Kilobit pro Sekunde auf Megabit pro Sekunde ansteigen und zu Gigabit pro Sekunde aggregiert werden. Das HinzufĂŒgen von KapazitĂ€ten, Ressourcen und Servern wĂ€hrend der Skalierung von Anwendungen geschieht blitzschnell und erhöht die generierte Menge an Rohdaten exponentiell. Die Entwicklung von skalierbaren Anwendungen ist essenziell, wenn mit Streaming-Daten gearbeitet wird.

Reihenfolge: Die Bestimmung der Datenfolge in Datenströmen ist nicht außer Acht zu lassen, denn fĂŒr viele Anwendungen ist sie von großer Bedeutung. Ein Chat oder ein GesprĂ€ch wĂŒrden ohne die richtige Reihenfolge auch keinen Sinn ergeben.

Wenn Entwickler versuchen, ein Problem zu lösen, indem sie sich die aggregierten Protokolldaten anschauen, muss jede Zeile an der richtigen Stelle stehen. Oft gibt es Diskrepanzen zwischen der Reihenfolge des generierten Datenpakets und der Reihenfolge, in der es am Zielort ankommt. Auch bei Zeitstempeln und Uhren von GerĂ€ten, die Daten generieren, kommt es oft zu Abweichungen. Bei der Analyse von Datenströmen mĂŒssen Anwendungen die Voraussetzungen fĂŒr ACID-Transaktionen berĂŒcksichtigen.

Konsistenz und Dauerhaftigkeit: Datenkonsistenz und Datenzugriff stellen immer ein großes Problem bei der Verarbeitung von Datenströmen dar. Die Daten, die zu einem bestimmten Zeitpunkt gelesen werden, könnten bereits in einem Rechenzentrum irgendwo anders auf der Welt modifiziert worden oder veraltet sein. Die Dauerhaftigkeit von Daten bildet auch eine Herausforderung bei der Anwendung von Datenströmen in der Cloud.

Fehlertoleranz und Datengarantien: Diese beiden Aspekte spielen bei der Arbeit mit Daten, bei der Datenstromverarbeitung und bei allen verteilten Systemen eine wichtige Rolle. Sind die vorhandenen Systeme in der Lage, AusfĂ€lle durch einen einzigen Fehlerpunkt zu verhindern, wenn Daten aus zahlreichen Quellen und von unterschiedlichen Standorten kommen und in verschiedenen Formaten und Mengen vorliegen? Können sie Datenströme mit hoher VerfĂŒgbarkeit und Dauerhaftigkeit speichern?

How Confluent Empowers Stream Processing on Enterprise Scale

Built by the original creators of Apache KafkaÂź, 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.