Stream data model and architecture in big data

A big data architecture is designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems. The threshold at which organizations enter into the big data realm differs, depending on the capabilities of the users and their tools. For some, it can mean hundreds of gigabytes of data, while for others it means hundreds of terabytes. As tools for working with big datasets advance, so does the meaning of big data. More and more, this term relates to the value you can extract from your data sets through advanced analytics, rather than strictly the size of the data, although in these cases they tend to be quite large. Over the years, the data landscape has changed. What you can do, or are expected to do, with data has changed. The cost of storage has fallen dramatically, while the means by which data is collected keeps growing. Some data arrives at a rapid pace, constantly demanding to be collected and observed. Other data arrives more slowly, but in very large chunks, often in the form of decades of historical data. You might be facing an advanced analytics problem, or one that requires machine learning. These are challenges that big data architectures seek to solve. Big data solutions typically involve one or more of the following types of workload: • Batch processing of big data sources at rest. • Real-time processing of big data in motion. • Interactive exploration of big data. • Predictive analytics and machine l...

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Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. Only a few big data streaming tools and...

Table of Contents • • • • • • • • • • • This article is an excerpt from our comprehensive, 40-page eBook: Streaming data is becoming a core component of enterprise data architecture due to the explosive growth of data from non-traditional sources such as IoT sensors, security logs, and web applications. Streaming technologies are not new, but they have considerably matured in recent years. The industry is moving from painstaking integration of open-source Spark/Hadoop frameworks, towards full stack solutions that provide an end-to-end streaming data architecture built on the scalability of In this article, we’ll cover the key tenets of designing cloud infrastructure that can handle the unique challenges of working with streaming data sources – from ingestion through transformation to analytic querying. First, a bit about us. Upsolver is a group of data engineers and developers who built a product called SQLake, an all-SQL data pipeline platform that lets you just “write a query and get a pipeline” for data in motion, whether in event streams or frequent batches. SQLake automates everything else, including orchestration, file system optimization and infrastructure management. To give it a try you can Basic Concepts in Stream Processing Let’s get on the same page by defining the concepts we’ll we be referring to throughout the article. What is Streaming Data? Streaming data refers to data that is continuously generated, usually in high volumes and at high velocity. A streami...

Click to learn more about author In the past decade, there has been an unprecedented proliferation of Big Data and Analytics. The term Big Data has been loosely used in so many different scenarios that it’s fair to say – Big Data is really what you want it to be – it’s just … big. Typically defined by structured and unstructured data, originated from multiple applications, consisting of historical and real-time information, Big Data is often associated with three V’s: volume, velocity, and variety. I’d like to add another V for “value.” Data has to be valuable to the business and to realize the value, data needs to be integrated, cleansed, analyzed, and queried. Inexpensive storage, Data streaming is one of the key technologies deployed in the quest to yield the potential value from Big Data. This blog post provides an overview of data streaming, its benefits, uses, and challenges, as well as the basics of data streaming architecture and tools. The Three V’s of Big Data: Volume, Velocity, and Variety Volume: Data is being generated in larger quantities by an ever-growing array of sources including social media and e-commerce sites, mobile apps, and IoT connected sensors and devices. Businesses and organizations are finding new ways to leverage Big Data to their advantage, but also face the challenge of processing this vast amount of new data to extract precisely the information they need. Velocity: Thanks to advanced WAN and wireless network technology large volumes of dat...

A modern data streaming architecture allows you to ingest, process, and analyze high volumes of high-velocity data from a variety of sources in real-time to build more reactive and intelligent customer experiences. The modern streaming data architecture can be designed as a stack of five logical layers; each layer is composed of multiple purpose-built components that address specific requirements. The following diagram illustrates the modern streaming data architecture. • Source - Your source of streaming data includes data sources like sensors, social media, IoT devices, log files generated by using your web and mobile applications, mobile devices that generates semi-structured and unstructured data as continuous streams at high velocity. • Stream storage - The stream storage layer is responsible for providing scalable and cost-effective components to store streaming data. The streaming data can be stored in the order it was received for a set duration of time, and can be replayed indefinitely during that time. • Stream ingestion - The stream ingestion layer is responsible for ingesting data into the stream storage layer. It provides the ability to collect data from tens of thousands of data sources and ingest in near real-time. . • Stream processing - The stream processing layer is responsible for transforming data into a consumable state through data validation, cleanup, normalization, transformation, and enrichment. The streaming records are read in the order they are ...

This reference architecture shows an end-to-end A reference implementation for this architecture is available on Architecture Download a Workflow The architecture consists of the following components: Data sources. In this architecture, there are two data sources that generate data streams in real time. The first stream contains ride information, and the second contains fare information. The reference architecture includes a simulated data generator that reads from a set of static files and pushes the data to Event Hubs. The data sources in a real application would be devices installed in the taxi cabs. Azure Event Hubs. Azure Databricks. Azure Cosmos DB. The output of an Azure Databricks job is a series of records, which are written to • without any performance or cost impact on your transactional workload, by using the two analytics engines available from your Azure Synapse workspace: Azure Log Analytics. Application log data collected by Alternatives • Scenario details Scenario: A taxi company collects data about each taxi trip. For this scenario, we assume there are two separate devices sending data. The taxi has a meter that sends information about each ride — the duration, distance, and pickup and drop-off locations. A separate device accepts payments from customers and sends data about fares. To spot ridership trends, the taxi company wants to calculate the average tip per mile driven, in real time, for each neighborhood. Potential use cases This solution is optimiz...