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Real-time Data Processing: Architectures and Technologies

7 Architectures and Technologies Used in Real-Time Data Processing

By VigneshPublished about a month ago 5 min read
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In today’s digital era, data is produced by numerous sources, including security logs, IoT sensors, messages, and servers, to mention a few. The increasing need to make informed decisions based on the latest data has led to real-time data processing. As the name suggests, real-time data processing refers to the ability to analyze and act upon data as it is received or generated without a significant delay.

This data processing approach is crucial in various domains, such as weather forecasting, e-commerce, finance, and other sector, where instant actions and insights are necessary. But what are the benefits of real-time data processing? And what are some architectures and technologies employed in real-time data processing? Let’s find out.

5 Benefits of Real-Time Data Processing

Here are some advantages of real-time data processing:

  • Minimal delay in data processing
  • Increased uptime
  • Information is always up-to-date and can be utilized instantly.
  • Issues are detected and mitigated early before causing significant harm to the organization.
  • Fewer resources are required to sync systems.

7 Architectures and Technologies Used in Real-Time Data Processing

Here are some frequently used architectures and technologies in real-time data processing:

1. Lambda Architecture

The lambda architecture combines real-time stream and batch processing to handle large data volumes. It involves parallel processing of data streams in two layers, namely:

Batch layer. This path facilitates the processing and storing of historical data, and

Speed layer. This layer supports real-time data analysis.

The outcomes from the two layers are combined in the serving layer, providing a unified data view. This architecture is commonly used because it efficiently processes historical and real-time data. Adopting this architecture enables organizations to derive valuable insights and make informed business decisions in real-time.

2. Apache Kafka

Apache Kafka is a distributed streaming platform that offers a fault-tolerant, scalable, and high-throughput solution for real-time data processing. It allows you to ingest, store, and process large volumes of data streams in real time. This makes it a popular choice for creating event-driven architectures.

3. Kappa Architecture

Jay Kreps initiated a discussion pointing out some challenges of the Lambda architecture, including its multi-layered processing, which made it complex to use in some enterprise scenarios. This discussion further led the big data-driven world to another real-time data processing architecture: Kappa architecture. This architecture uses fewer code resources than Lambda architecture.

This real-time data processing architecture is ideal in scenarios where active batch layer performance isn’t necessary to meet the standard quality of service. It is applied in real-time data processing of distinct events.

The Kappa architecture can be adopted for data processing enterprise models where:

The order of queries or events isn't predefined. Stream processing platforms may interact with the database at any time.

Several queries or data events are logged into a queue to be addressed against a distributed file system history or storage.

It is highly available and resilient as managing terabytes of storage is necessary for each system node to support replication.

4. In-Memory Databases

In-memory databases depend on memory for data storage, unlike typical databases that store data on SSDs or hard disks. These databases function like Random Access Memory (RAM). They’re designed to reduce response times by eliminating the need to access storage disks.

However, since all data is managed and stored exclusively in main memory, in-memory databases can lose data upon server or process failure. These databases can persist data on disks by taking a snapshot or storing each operation in a log.

For businesses looking to leverage in-memory databases and streamline their data processing, partnering with a reliable provider of data engineering services can be crucial.

Some common use cases of in-memory databases include applications that have large spikes in traffic or require microsecond response times, including the following:

  • Real-time bidding and data analytics,
  • Gaming leaderboards, and
  • Session stores.

5. Apache Flink

Apache Flink is an open-source, enterprise-class real-time data processing architecture introduced in 2014. This real-time data processing framework is designed to process large data streams in real-time. It is built on top of the Java Virtual Machine and supports stream and batch processing.

Apache Flink is a distributed system that can operate on a cluster of machines. It’s designed to be scalable, fault-tolerant, and highly available. Also, it supports numerous data sources and offers a unified API for stream and batch data processing. This makes it easy to create complex real-time data processing applications.

Some of Apache Flink include:

  • Low-latency
  • Flexible windowing
  • High compatibility
  • Fault tolerance
  • High throughput

6. Apache Storm

Apache Storm is a big data processing engine for real-time computation and analytics. It is an open-source and distributed real-time data processing architecture. This architecture has a cluster with several components.

The Apache Storm architecture comprises two types of nodes, a master node, and a worker node. Apache Storm’s master node operates the nimbus (daemon for the master node), which examines and administers tasks to the worker node or cluster. Also, it assigns tasks to various machines and supervises them to ensure they operate efficiently by detecting failures.

On the other hand, the worker node comprises of a supervise, which acts as the node’s daemon. Each worker node process runs as part of the topology as bolts and spouts.

7. Spark Streaming

Spark streaming is a crucial part of Spark core API that supports real-time data analytics. It enables you to build a high-throughput, fault-tolerant, and scalable streaming app for live data streams.

This real-time data processing architecture enables you to process real-time data from multiple sources. The processed data is stored in several output sinks. Spark streaming real-time data processing architecture has three major components, including the following:

Input data sources. These are the streaming data sources (such as Kinesis, Kafka, and Flume), static data sources (e.g., MongoDB and MySQL), Twitter, TCP sockets, etc.

Spark streaming engine. This component processes incoming data using various pre-built functions and complex algorithms. Also, it allows you to query live streams and apply machine learning leveraging SparkSQL and MLlib.

Output Sinks. As initially stated, output sinks are used to store processed data. Output sinks may include live dashboards, databases, and file systems.

Final Thoughts

As real-time data analytics services and processing grow, more architectures and technologies are born. Several architectures adopted in real-time data processing include Lambda, Kappa, Apache Storm, Apache Flink, and Spark Streaming. Each of these architectures has its pros and cons. Therefore, you should critically assess their capabilities and use cases when selecting a real-time data processing architecture. This will help you choose the architecture that perfectly meets your real-time data processing needs.

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