So far, it’s all been about the storage of data, data in flight, data from IoT devices, etc. Let’s look at some traditional data processing methods and see how they work with modern database systems. Users’ model-based enquiries are manifested in a provided by individuals that are produced when the request payloads are initiated. Combining the data and business software has been the only actors who have collaborated to implement such data models. They work together to process users’ requests and store the data in dynamic data stores for future updates. The degree of such actions among business applications ingesting data from all these distributed computing shared data storages is used to determine business continuity. Of course, if there are fewer of these actions, there is a greater chance that the firm will remain inactivity while waiting for new data to be acquired.
The distributed computing paradigm above is intrinsically set up to potentially miss out on a huge opportunity to improve business continuity. A modification in the fixed database paradigm is required to close these gaps. The new enormous ingested information processing requirements necessitate the development of large-scale data processing that continually creates insight from any “data in-flight,” preferably in real-time. The persistence of intermediate calculated values in a temporary database server is expected to be kept to a minimum to address storage access performance limitations.
This distributed computing blog looks at these new data processing approaches from real-time streamed ingestion and processing. It also goes into great detail on Dell Technologies’ offerings of similar types.
Distributed computing customers can use real-time streaming analytics technology by constructing their solutions based on open-source initiatives. It isn’t easy to mix and match such components to build real-time data input and processing systems. Stabilizing such networks in production contexts necessitate a wide range of expensive expertise. Dell Technologies offers proven reference architectures to satisfy desired KPIs on storage & compute capacities to make these implementations easier. The below sections offer a high-level concept of the real-time data streaming and various platforms for implementing these solutions. This blog compares and contrasts two Dell Ready Architecture solutions: Streaming Data Technology (previously known as Nautilus) or Real-Time Streaming architectures based on distributed computing Confluent’s Kafka ingestion platform.
Streaming data in real-time
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The concept of hard data streaming encompasses a lot more than just absorbing data directly. Many articles clearly define the compelling goals of a system that continuously ingests thousands of data events. Another founder of open-sourced Apache Kafka, Jay Kreps, has written an article that gives a complete and in-depth explanation of consuming real-time streaming data.
Distributed computing platforms for real-time streaming analytics
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A complete final big data analytics solution must include the following features:
- Reduce the complexity of the data ingestion layer.
- Integrate with various components of the big data environment seamlessly.
- Develop insight-analytics apps with programming model APIs.
To access the processed data to visualization and business intelligence layers, provide plug-and-play hooks. Over the last several years, growth in real ingestion features has prompted the implementation of various integrated and holistic engines, each with its focused architecture. Streaming analytics engines have various capabilities, from micro-batching streaming data during processing to near-real-time performance to actual real-time processing. The consumed data could be anything from a binary event to a sophisticated event format. Dell Technologies’ Pravega & challenge posed Apache 2.0 Kafka are two examples of large size ingestion engines that can be effortlessly linked to open-source big data processing engines like Samza, Spark, Flink, & Storm, to mention a few. A variety of vendors offer proprietary implementations of related technologies. Striim, WSO2 Advanced Signal Processor, IBM Stream, SAP Action Stream Processor, & TIBCO Event Processing are just a few of these products.
A Dell Technologies plan for real-time streaming analytics
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According to Dell Technologies, customers can choose between two options for implementing their real-time streaming infrastructure. The ingestion layer is created on Apache Kafka, and the default data stream processing engine is Kafka Stream Processing. The second approach is based on Pravega, an open-source ingestion layer, and Flink, the default simple data stream processing engine. And how are these products being leveraged to meet the needs of customers? Let’s look at some of the integration patterns that Dell Technologies’ real-time stream products can help with, including big data and partial pretreatment layers.
Patterns of real-time streaming and massive data processing
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Customers use real-time streaming in various ways to fulfil their unique needs. This means there could be various ways to integrate real-time streaming solutions with the rest of the customer’s IT ecosystem. Customers can mix and combine a range of existing streams, storage, computation, etc., business analytics technologies to create a basic big data integration structure.
The Stream Processing layer can be implemented in various ways, including the two following Dell Technologies solutions.
Confluent Enabled Platform for Hard Data Streaming from Dell EMC
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Apache Kafka is one of the best solutions, and it also includes Kafka Parallel Computation in the same package. Confluent offers and promotes the Apache Kafka distribution and the Confluent Enterprise-Ready Platform, which includes enhanced Kafka features.
