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2014
Defining the environment for analyzing streamed big data in real time is not an easy task. There are many architecture proposals for real time big data analytic, but the most interesting one for our problem is Lambda Architecture. In this paper we are presenting motivation for developing such architecture, how it works and our practical work for implementing it. Lambda Architecture is comprised by three layers batch, speed and serving layer. Thus far we have implemented the batch layer employing Hadoop framework. We also briefly review the other two layers in order to implement them in the next phase of our work, where for serving and speed layer we conclude that Storm is the best choice. Practical example demonstrates the analytical process in Hadoop for analyzing Wikipedia text data.
Leveraging Lambda Architecture for Efficient Real-Time Big Data Analytics, 2020
In this era of big data, firms struggle with processing, analyzing and making sense of the "big data" in realtime, hence the ability to extract valuable information in time for decision-making. The lambda architecture has become a potent framework that allows extensive data systems to handle large and real-time data sets, both batch and streaming. Through Lambda Architecture, this paper discusses Lambda architecture and its relation to up-to-date data analysis. We will review the core architecture parts, including batch, speed, and serving layers, and then show how they work to provide a practical solution to colossal data processing and service. Moreover, we dive into the usage of Lambda Architecture by employing comprehensive big data technologies, including Apache Kafka for taking data in, Apache Hadoop for batch processing, Apache Spark for stream processing, and Apache Cassandra for serving the results. Moreover, we discuss the positive aspects and the issues that may arise with applying Lambda Architecture and even provide some real examples related to this area. The performance of the scheme we implemented shows that the Lambda Architecture models a sustainable and functional system to exploit big data for quick analytics and decision-making in all areas of life.
Big data is a term that describes the large volume of both structured and unstructured data that inundates a business on a day-today basis. Due to the fact that the database systems like RDBMS can process the unstructured data but RDBMS finds it challenging to handle such huge data volumes. To deal with the challenges Hadoop is used. It is software framework for distributed storage and distributed processing of very large data sets. But the disadvantages faced by Hadoop are Security Concerns and Vulnerable by Nature. To resolve those problems, Spark is introduced and used which is a cluster computing framework and performances up to 100 times faster for certain applications. As this paper is on both real time and interactive big data, to cope up them, we use Lambda architecture which is a data-processing architecture designed to handle massive quantities of data by taking advantages of both batch and stream processing layers. To pull the data from external sources and pass to Hadoop, tools like Flume is used. Scala is the programming language in which the programs are written because it is Scalable Language which in inspired by Java. Spark SQL and Spark Streaming are tools of Spark which are used for batch and stream data-processing. Cassandra is an open-source distributed database management system designed to handle and store large amounts of data.
2015 IEEE International Conference on Big Data (Big Data), 2015
Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis 'on-the-fly', as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.
2014
Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parall...
International Journal of Advanced Computer Science and Applications, 2017
Processing a data stream in real time is a crucial issue for several applications, however processing a large amount of data from different sources, such as sensor networks, web traffic, social media, video streams and other sources, represents a huge challenge. The main problem is that the big data system is based on Hadoop technology, especially MapReduce for processing. This latter is a high scalability and fault tolerant framework. It also processes a large amount of data in batches and provides perception blast insight of older data, but it can only process a limited set of data. MapReduce is not appropriate for real time stream processing, and is very important to process data the moment they arrive at a fast response and a good decision making. Ergo the need for a new architecture that allows real-time data processing with high speed along with low latency. The major aim of the paper at hand is to give a clear survey of the different open sources technologies that exist for real-time data stream processing including their system architectures. We shall also provide a brand new architecture which is mainly based on previous comparisons of real-time processing powered with machine learning and storm technology.
In recent years, real-time processing and analytics systems for big data-in the context of Business Intelligence (BI)-have received a growing attention. The traditional BI platforms that perform regular updates on daily, weekly or monthly basis are no longer adequate to satisfy the fast-changing business environments. However, due to the nature of big data, it has become a challenge to achieve the real-time capability using the traditional technologies. The recent distributed computing technology, MapReduce, provides off-the-shelf high scalability that can significantly shorten the processing time for big data; Its open-source implementation such as Hadoop has become the de-facto standard for processing big data, however, Hadoop has the limitation of supporting real-time updates. The improvements in Hadoop for the real-time capability, and the other alternative real-time frameworks have been emerging in recent years. This paper presents a survey of the open source technologies that support big data processing in a real-time/near real-time fashion, including their system architectures and platforms.
Data
We study big-data hybrid-data-processing lambda architecture, which consolidates low-latency real-time frameworks with high-throughput Hadoop-batch frameworks over a massively distributed setup. In particular, real-time and batch-processing engines act as autonomous multi-agent systems in collaboration. We propose a Multi-Agent Lambda Architecture (MALA) for e-commerce data analytics. We address the high-latency problem of Hadoop MapReduce jobs by simultaneous processing at the speed layer to the requests which require a quick turnaround time. At the same time, the batch layer in parallel provides comprehensive coverage of data by intelligent blending of stream and historical data through the weighted voting method. The cold-start problem of streaming services is addressed through the initial offset from historical batch data. Challenges of high-velocity data ingestion is resolved with distributed message queues. A proposed multi-agent decision-maker component is placed at the MALA ...
International Journal of Database Theory and Application, 2014
Data analytics and machine learning has always been of great importance in almost every field especially in business decision making and strategy building, in healthcare domain, in text mining and pattern identification on the web, in meteorological department, etc. The daily exponential growth of data today has shifted the normal data analytics to new paradigm of Big Data Analytics and Big Data Machine Learning. We need tools to perform online data analysis on streaming data for achieving faster learning and faster response in data analytics as well as maintaining scalability in terms of huge volume of data. SAMOA (Scalable Advanced Massive Online Analysis) is a recent framework in this reference. This paper discusses the architecture of this SAMOA framework and its directory structure. Also it expresses a practical experience of configuring and deployment of the tool for handling massive online analysis on Big Data.
International Journal of High Performance Computing and Networking, 2019
Nowadays, real-time messaging system is the essential thing in enabling time-critical decision making in many applications where it is important to deal with real-time requirements and reliability requirements simultaneously. For dependability reasons, we intend to maximise the reliability requirement of the real-time messaging system. To develop a real-time messaging system, we create real-time big data pipeline by using Apache Kafka and Apache Storm. This paper focuses on analysing the performance of producer and consumer in Apache Kafka processing. Apache Kafka is the most popular framework used to ingest the data streams into the processing platforms. The comparative analysis of Kafka processing is more efficient to get reliable data on the pipeline architecture. Then, the experiment will be conducted the processing time in the performance of the producer and consumer on various partitions and many servers. The performance analysis of Kafka can impact on messaging systems in real-time big data pipeline architecture.
2020
While scientific data is usually processed with specialized software, more and more institutions make use of new developments in Big Data for processing all the ancillary data that accompany experimental research and scientific computing. One common use case is monitoring of the experiments and the IT infrastructure, but a well-realized Big Data platform can be used to extract useful knowledge from many available data streams. This work describes our efforts to lay out and implement a general-purpose Big Data processing framework capable of both batch and stream operation for use in our institute. As the core, we chose Apache Spark running on cluster resources controlled by Apache Mesos. To demonstrate the functionality of the platform in both modes, we chose a sample problem of local network packet analysis. TCP and IP header fields were aggregated by the source IP and converted to the AGgregate and Mode (AGM) form. After encoding into numerical features, clusterization and princip...
IFAC-PapersOnLine, 2016
Expectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kind of environments with their complexity in terms of heterogeneity and volatility. The paradigm of the Lambda architecture features key characteristics (such as, robustness, fault tolerance, scalability, generalization, extensibility, ad-hoc queries, minimal maintenance, and low-latency reads and updates) to cope with this complexity. The paper at hand suggest a basic set of strategies to handle the arising challenges regarding the volatility, heterogeneity, and desired low latency execution by reducing the overall system timing (scheduling, execution, monitoring, and faults recovery) as well as possible faults (churn, no answers to executions). The proposed strategies make use of services such as migration, replication, MapReduce simulation, and combined processing methods (batch-and streaming-based). Via these services, a distribution of tasks for the best balance of computational resources is achieved, while monitoring and management can be performed asynchronously in the background.
2020
In recent years data has grown exponentially due to the evolution of technology. The data flow circulates in a very fast and continuous way, so it must be processed in real time. Therefore, several big data streaming platforms have emerged for processing large amounts of data. Nowadays, companies have difficulties in choosing the platform that best suits their needs. In addition, the information about the platforms is scattered and sometimes omitted, making it difficult for the company to choose the right platform. This work focuses on helping companies or organizations to choose a big data streaming platform to analyze and process their data flow. We provide a description of the most popular platforms, such as: Apache Flink, Apache Kafka, Apache Samza, Apache Spark and Apache Storm. To strengthen the knowledge about these platforms, we also approached their architectures, advantages and limitations. Finally, a comparison among big data streaming platforms will be provided, using as...
—Hadoop and Storm are playing a significant role in Cloud Computing and either of them has its own applicable area. Cocktail is a new hybrid system that combines Hadoop and Storm into one single system, leveraging the functions of two computing frameworks. The design and implementation of Cocktail includes a SQL-like query language making the implementation of details transparent for users, an intelligent framework selector based on cost model to choose appropriate framework automatically, and an efficient resource scheduling and task execution framework. Cocktail has a wide range of application scenarios from batch processing to stream computing, using Storm to process real-time data and Hadoop to process large-scale data. We compare the performance, throughput and scalability of Cocktail with SummingBird to demonstrate the practicability and capability. According to benchmark, for small-scale data, the performance of Cocktail is close to Summingbird based on Storm and 20%~40% faster than Summingbird based on Hadoop. And for large-scale data, Cocktail's throughput is 40% higher than Summingbird's throughout based on Storm.
Lecture Notes on Information Theory, 2017
The studies of big data analytics has emerged due to the lack of data analysis methods, and storage problems with traditional database systems. Some big data applications require real time analysis, and there is time constraint to analyze for the applications. Various methods and have been proposed to overcome this difficulty. In this study, several architectures and applications for real-time big data analysis have been investigated and compared with each other in details. Valuable suggestions have been proposed for researchers working in real-time big data analytics. Index Terms-big data, real time analysis, real time big data architecture
2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015
HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
International Journal of Scientific Research in Science and Technology, 2021
Big data analytics is becoming more and more popular every day as a tool for evaluating large volumes of data on demand. Apache Hadoop, Spark, Storm, and Flink are four of the most widely used big data processing frameworks. Although all four architectures support big data analysis, they vary in how they are used and the infrastructure that supports it. This paper defines a general collection of main performance metrics, which include Processing Time, CPU Use, Latency, Execution Time, Performance, Scalability, and Fault-tolerance, and contrasting the four big data architectures against these KPIs in a literature review. When compared to Apache Hadoop and Apache Storm frameworks for non-real-time results, Spark was found to be the winner over multiple KPIs, including processing time, CPU usage, Latency, Execution time, and Scalability. In terms of processing time, CPU consumption, latency, execution time, and performance, Flink surpassed Apache Spark and Apache Storm architectures.
International Journal of Engineering & Technology
MapReduce is the most widely used for huge data processing and it is a part of the Hadoop big data and this will provide the quality and efficient results because of their processing functions. For the batch jobs, Hadoop is the proper and also there is inflated request for non-batch elements homogeneous interactive jobs, and high data currents. For this non-batch assignments, consider Hadoop is not useful and present situations are recommending to these new crises. In this paper, these are divided into two stages that are real-time processing, and stream processing of big data. For every stage, the models are deliberate, stability and diversity to Hadoop. For every group, we have provided the working systems and structures. For the creation of the new examples, some experiments are conducted to improve the new results belongs to available Hadoop-based solutions.
2017
The paradigm shift of data from static to fast flowing data is an important move in the industry, to accommodate growing size of data. The velocity and volume of data are continuing to expand which has started to make its impact in business and other applications of Big Data. The paper describes the paradigm shift of data from static data to streaming data for data analytics beyond Hadoop. It describes how the first generation of Hadoop applications were largely built for batch-oriented paradigm . Streaming data is essentially different from traditional data handling patterns and comes with its own set of challenges and requirements. New applications such as Storm, Flume, Kafka, and other technologies are evolving to bring in an era of real-time analytics Data is generated incessantly from thousands of sources simultaneously and it can be of various type such as log files, mobile and web data, transaction etc. The sections of my paper are Introduction followed by Streaming data, Had...
it - Information Technology, 2016
Nowadays, data is produced in every aspect of our lives, leading to a massive amount of information generated every second. However, this vast amount is often too large to be stored and for many applications the information contained in these data streams is only useful when it is fresh. Batch processing platforms like Hadoop MapReduce do not fit these needs as they require to collect data on disk and process it repeatedly. Therefore, modern data processing engines combine the scalability of distributed architectures with the one-pass semantics of traditional stream engines. In this paper, we survey the current state of the art in scalable stream processing from a user perspective. We examine and describe their architecture, execution model, programming interface, and data analysis support as well as discuss the challenges and limitations of their APIs. In this connection, we introduce Piglet, an extended Pig Latin language and code generator that compiles (extended) Pig Latin code ...
it - Information Technology, 2016
With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of today's Big Data repositories can be exploited using traditional batch-oriented approaches as the value of data often decays quickly and high latency becomes unacceptable in some applications. In the last couple of years, several distributed data processing systems have emerged that deviate from the batch-oriented approach and tackle data items as they arrive, thus acknowledging the growing importance of timeliness and velocity in Big Data analytics. In this article, we give an overview over the state of the art of stream processors for low-latency Big Data analytics and conduct a qualitative comparison of the most po...
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