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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 ‚Äústay awake‚ÄĚ 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.

Challenges Building Data Streaming Applications

Principales difficultés liées à la conception d'applications de données en temps réel

√Čvolutivit√©¬†: lorsque des erreurs syst√®me se produisent, le volume des donn√©es de journalisation provenant de chaque appareil peut cro√ģtre, leur taux d'envoi passant d'un certain nombre de kilobits par seconde √† des m√©gabits par seconde, puis √† des gigabits par seconde apr√®s agr√©gation des donn√©es. L'ajout de davantage de capacit√©, de ressources et de serveurs au fil de l'√©volution des applications est instantan√©, ce qui augmente de fa√ßon exponentielle la quantit√© de donn√©es brutes g√©n√©r√©es. Lorsqu'on travaille avec des donn√©es diffus√©es en continu, concevoir des applications pr√©sentant une dimension √©volutive est primordial.

Ordre : déterminer l'ordre des données dans le flux n'a rien de futile, et c'est au contraire très important dans de nombreuses applications. Un chat ou un échange n'auraient pas de sens s'ils n'étaient pas ordonnés. Lorsque les développeurs résolvent un bug en consultant une vue agrégée des données de journalisation, il est essentiel que chaque ligne soit dans le bon ordre. L'ordre des paquets de données générés diffère souvent de l'ordre dans lequel ils atteignent leur destination. Des divergences apparaissent aussi au niveau de l'horodatage et des horloges des appareils qui génèrent des données. Lors de l'analyse des flux de données, les applications doivent être conscientes de leurs hypothèses concernant les propriétés ACID des transactions.

Cohérence et durabilité : l'accès aux données et leur cohérence constituent toujours un problème complexe dans le cadre du traitement des flux de données. Les données lues à un instant T pourraient avoir déjà été modifiées et être devenues obsolètes dans un autre centre de données situé ailleurs dans le monde. La durabilité des données représente également un défi lorsqu'on travaille avec des flux de données sur le cloud.

Tolérance aux pannes et garanties autour des données : ce sont des aspects qu'il est important de prendre en compte lorsqu'on travaille avec les données, le traitement des flux ou tous types de systèmes distribués. Les données provenant d'un grand nombre de sources et d'emplacements, dans des formats et des volumes variés, votre système peut-il empêcher les interruptions à partir d'un point de défaillance unique ? Peut-il stocker les flux de données présentant une disponibilité et une durabilité élevées ?

How Confluent Empowers Stream Processing on Enterprise Scale

Built by the original creators of Apache Kafka¬ģ, 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.