data to extract precisely the information they need. Forwarding outputs to serving layer. Data chronological sequence of the activity that it represents. This allows the airline to detect early Cryptocurrency: Our World's Future Economy? typically time-series data. Data streaming is an extremely important process in the world of big data. Consumer applications may be automated decision engines that are programmed to take various actions or raise alerts when they identify specific conditions in the data. 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. Moving data to streaming layer. State Management for Stream Joins 213 Model and Semantics 210 3. Data streams need to be processed in real-time and in a scalable fashion in order to have business value and offer operational insights. Data stream not clogged with swimmers. Big data streaming is ideally a speed-focused approach wherein a continuous stream of data is processed. Examples include: 1. However, the sheer size, variety and velocity of big data adds further challenges to these systems. September 11, 2019. streaming is a key capability for organizations who want to generate analytic Streaming data includes a wide variety of data such as log files generated by customers using your mobile or web applications, ecommerce purchases, in-game player activity, information from social networks, financial trading … viii DATA STREAMS: MODELS AND ALGORITHMS References 202 10 A Survey of Join Processing in Data Streams 209 Junyi Xie and Jun Yang 1. value. throughout each day. In computer science, streaming algorithms are algorithms for processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes (typically just one). Abstract and Figures A key message from the early adopters of big data is that technologies such as Hadoop®, NoSQL (Not Only Structured Query Language) databases, and stream computing … integrated, cleansed, analyzed, and queried. We may share your information about your use of our site with third parties in accordance with our, Concept and Object Modeling Notation (COMN). Real-time streaming data analysis is a single-pass analysis. what you want it to be – it’s just … big. quantities by an ever-growing array of sources including social media and The industry is moving from painstaking integration of open-source … 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. it is not suited to processing data that has a very brief window of value – transmit it to the streaming message broker. 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. Q    A cybersecurity team at a large financial institution This type of architecture has three basic components -- an aggregator that gathers event streams and batch files from a variety of data sources, a broker that makes data … Malicious VPN Apps: How to Protect Your Data. Setting up big data modeling platforms. In most models, these algorithms have access to limited memory (generally logarithmic in the size of and/or the maximum value in the stream). E    coherent stream of data. should also add a fourth V for “value.” Data has to be valuable to the business This includes personalizing content, using analytics and improving site operations. How can businesses solve the challenges they face today in big data management? With millions of customers and thousands of That percentage was … As an example of batch processing, consider a retail ingesting, and processing data continuously rather than in batches. They may also have limited processing time per item. Speed matters the most in big data streaming. The data can then be accessed and analyzed at any Data that is generated in never-ending streams does not lend itself to batch processing where data collection must be stopped to manipulate and analyze the data. The data Managing and processing data in motion is a typical capability of streaming data systems. and output of various components. C    Data sources. readings, as well as audio and video streams. Such systems are designed to manage relatively simple computations. To do this they must monitor and analyze Application data stores, such as relational databases. How Can Containerization Help with Project Speed and Efficiency? employees at locations around the world, the numerous streams of data generated technology that is capable of capturing large fast-moving streams of diverse unstructured data, originated from multiple applications, consisting of Are These Autonomous Vehicles Ready for Our World? Data streams differ from the conventional stored relation model in several ways: The data elements in the stream arrive online. The dataset is generated for mock-up purposes. R    © 2011 – 2020 DATAVERSITY Education, LLC | All Rights Reserved. Real-time analytics: Big Data in motion Real time Data infrastructure: Built from distributed components. Data Stream Models – Additive Model • Each element Üis an increment to the previous version of the given data object Big Data Management and Analytics 201 Stream Processor T 72 T 53 time T 5 T 6 T 7 T 8 T 5 T 6 T 7 T 8 T 72 T 53 1 5411 41154 Data streaming technology is This happens across a cluster of servers. Smart Data Management in a Post-Pandemic World. Techopedia Terms:    Streaming data management systems cannot be separated from real-time processing of data. The Stream Processor receives data streams from one or more message brokers and applies user-defined queries to the data to prepare it for consumption and analysis. handling of data volumes that would overwhelm a typical batch processing shopping history. and to realize the value, data needs to be integrated, cleansed, analyzed, and advantage in their ability to rapidly make informed decisions. financial transaction data, unstructured text strings, simple numeric sensor Viable Uses for Nanotechnology: The Future Has Arrived, How Blockchain Could Change the Recruiting Game, 10 Things Every Modern Web Developer Must Know, C Programming Language: Its Important History and Why It Refuses to Go Away, INFOGRAPHIC: The History of Programming Languages, Relational Database Management System (RDBMS), Top 14 AI Use Cases: Artificial Intelligence in Smart Cities, How Big Data is Going to Change Genetic Testing. The following scenarios illustrate how data streaming Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What is the difference between big data and Hadoop? For example, a producer might generate log data in a raw unstructured format that is not ideal for consumption and analysis. V’s: volume, velocity, and variety. Z, Copyright © 2020 Techopedia Inc. - In batch processing, data is S    is cumulatively gathered so that varied and complex analysis can be performed store that captures transaction data from its point-of-sale terminals architecture are: The most essential requirement of stream processing is Can there ever be too much data in big data? Stream processing is still a niche application, even among big data users. An investment firm streams stock market data in real time and combines This data is stored in a relational database. In contrast, data streaming is ideally suited to inspecting and identifying patterns over rolling time windows. It is generated and transmitted according to the Thus, our goal is to build a scalable and maintainable architecture for performing analytics on streaming data. Data that is generated in a continuous flow is store sales performance, calculate sales commissions, or analyze the movement Building a data model can be used by multiple application layers to a. O    Streaming technologies are not new, but they have considerably matured in recent years. Data streams, or continuous data flows, have been around for decades. #    T    Maybe you’re training a machine learning model on a really big dataset. used in so many different scenarios that it’s fair to say – Big Data is really a natural fit for handling and analyzing time-series data. D    5 Common Myths About Virtual Reality, Busted! A Fast Method to Stream Data from Big Data Sources. aircraft fleet to identify small but abnormal changes in temperature, pressure, Speed matters the most in big data streaming. Velocity: Thanks to advanced WAN and compare it to traditional batch processing. e-commerce sites, mobile apps, and IoT connected sensors and devices. Producers are Stream processing technology is used in various settings: Apache Storm and Spark Streaming are two of the most commonly used stream processors. What is the difference between big data and data mining? analyzed. 26 Real-World Use Cases: AI in the Insurance Industry: 10 Real World Use Cases: AI and ML in the Oil and Gas Industry: The Ultimate Guide to Applying AI in Business. Data streams work in many different ways across many modern technologies, with industry standards to support broad global networks and individual access. Or maybe you’re crawling web scrapes or mining text files. well as external customer transactions at branch locations, ATMs, point-of-sale Tech's On-Going Obsession With Virtual Reality. Stream processing allows for the collected over time and stored often in a persistent repository such as a On-premises data required for streaming and real-time analytics is often written to relational databases that do not have native data streaming capability. Cookies SettingsTerms of Service Privacy Policy, We use technologies such as cookies to understand how you use our site and to provide a better user experience. The Three V’s of Big continuously monitors the company’s network to detect potential data breaches Real time Big Data Basic Architecture Model: Collecting data from various places. the challenge of parsing and integrating these varied formats to produce a J    time. Reinforcement Learning Vs. identify suspicious patterns take immediate action to stop potential threats. Y    Incorporating this data into a data streaming framework can be accomplished using a log-based Change Data Capture solution, which acts as the producer by extracting data from the source database and transferring it to the message broker. results in real time. Global Data Strategy, Ltd. 2016 Combining DW & Big Data Can Provide Valuable Information • There are numerous ways to gain value from data • Relational Database and Data Warehouse systems are one key source of value • Customer information • Product information • Big Data can offer new insights from data • From new data sources (e.g. A clothing retailer monitors shopping activity on their website A continuous stream of unstructured data is sent for analysis into memory before storing it onto disk. At the same time, the prominence of its other functions has increased. maintenance. The ability to focus on any segment of a data stream at any level is lost when it is broken into batches. can be used to provide value to various organizations: The fundamental components of a streaming data Communicate via asynchronous network. The term Big Data has been loosely The value of data, if not processed quickly, decreases with time. In the past decade, there has been an unprecedented All big data solutions start with one or more data sources. Static files produced by applications, such as we… We’re Surrounded By Spying Machines: What Can We Do About It? H    Big data streaming is ideally a speed-focused approach wherein a continuous stream of data is processed. A data model describes an entire enterprise from a data point of view. large volumes of data where the value of analysis is not immediately time-sensitive, The second big data business model, called “Information as a Service” (IaaS), focuses on providing insights based on the analysis of processed data (figure 3). Businesses and organizations are finding new ways to leverage Big Data to their gathered during a limited period of time, the store’s business hours. Dimensional Model Functions in the Age of Big Data In the wake of new and diverse ways to manage data, the dimensional model has become more important, not less. To better understand data streaming it is useful to offers to customers in their physical store locations based on the customer’s rapidly process and analyze this data as it arrives can gain a competitive opportunities and adjust its portfolios accordingly. Introduction 209 2. The value of data, if not processed quickly, decreases with time. K    V    N    Big Data and 5G: Where Does This Intersection Lead? To reach this goal, we introduce a 7-layered architecture consisting of microservices and publish-subscribe software. This activity merges concepts from the arenas of both Big Data and Internet of Things where data standardisation is not normally considered. More of your questions answered by our Experts. applications that communicate with the entities that generate the data and The Three V’s of Big Data: Volume, Velocity, and Variety Big data streaming is a process in which big data is quickly processed in order to extract real-time insights from it. system, sorting out and storing only the pieces of data that have longer-term Many web and cloud-based applications have the and fraudulent transactions. database or data warehouse. This happens across a cluster of servers. Analyze data in stream processor. Data streaming also allows for the processing of data historical and real-time information, Big Data is often associated with three Terms of Use - In the data stream model, some or all of the input data that are to be operated on are not available for random access from disk or memory, but rather arrive as one or more continuous data streams. Read on to learn a little more about how it helps in real-time analyses and data ingestion. Variety: Big Data comes in many different formats, including structured Summary Streaming Data introduces the concepts and requirements of streaming and real-time data systems. Volume: Data is being generated in larger Organizations with the technology to The book is an idea-rich tutorial that teaches you to think about how to efficiently interact with fast-flowing data. The message broker can also store data for a specified period. After the stream processor has prepared the data it can be streamed to one or more consumer applications. Our Data streaming is one of the key technologies deployed in the quest to yield the potential value from Big Data. In this case the customer’s job-to-be-done is more about coming up with their own conclusions or even “selling” an idea based on certain information. Value: As noted above, we I    The following diagram shows the logical components that fit into a big data architecture. Analyzing big data streams yields immense advantages across all sectors of our society. But with the advent of the big-data era, the size of data streams has increased dramatically. The Work that goes Into Data Modeling: Briefly, the first place a data modeler begins, hopefully, is with a set of requirements. To analyze streams, one needs to write a stream … and analyze it as it arrives. and combines it with real-time data mobile devices to send promotional discount Inexpensive storage, public cloud adoption, and innovative data integration technologies together can be the perfect fire triangle when it comes to deploying data lakes, data ponds, data dumps – each supporting a specific use case. A continuous stream of unstructured data is sent for analysis into memory before storing it onto disk. by this activity are massive, diverse, and fast-moving. An airline monitors data from various sensors installed in its has to be valuable to the business and to realize the value, data needs to be Data: Volume, Velocity, and Variety. G    Summary. Big data streaming is a process in which large streams of real-time data are processed with the sole aim of extracting insights and useful trends out of it. While batch processing is an efficient way to handle In these lessons you will learn the details about big data modeling and you will gain the practical skills you will need for modeling your own big data projects. capability to act as producers, communicating directly with the message broker. I’d like to add another V for “value.” Data of inventory. L    More commonly, streaming data is consumed by a data analytics engine or application, such as Amazon Kinesis Data Analytics, that allow users to query and analyze the data in real time. U    The message broker receives data from the producer and converts it into a standard message format and then publishes the messages in a continuous stream called topics. signs of defects, malfunctions, or wear so that they can provide timely Data streaming is one of the key technologies deployed in the quest to yield the potential value from Big Data. Individual solutions may not contain every item in this diagram.Most big data architectures include some or all of the following components: 1. A data stream is defined in IT as a set of digital signals used for different kinds of content transmission. The 6 Most Amazing AI Advances in Agriculture. A company's goal today is not only to deal with analyzing Big Data, but to also provide timely results from that analysis. advantage, but also face the challenge of processing this vast amount of new As a form of schema design, the news of its death has been greatly exaggerated. data, processing the data into a format that can be rapidly digested and The Adobe Flash plugin is needed to view this content. volumes and types that would be impractical to store in a conventional data wireless network technology large volumes of data can now be moved from source P    Typically defined by structured and The message broker can pass this data to a stream processor, which can perform various operations on the data such as extracting the desired information elements and structuring it into a consumable format. Big data streaming is a process in which large streams of real-time data are processed with the sole aim of extracting insights and useful trends out of it. Big data streaming is a process in which big data is quickly processed in order to extract real-time insights from it. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia. Modeling big data depends on many factors including data structure, which operations may be performed on the data, and what constraints are placed on the models. W    repository such as a relational database. Building a data model within a cloud data warehouse (CDW) is a big step toward taming the data beast. proliferation of Big Data and Analytics. Click to learn more about author Joe deBuzna. F    to destination at unprecedented speed. While organizations have hardly For example, in a survey conducted last June by consultancy Gartner Inc., only 22% of the 218 respondents with active or planned big data initiatives said they were using stream or complex event processing technologies or had plans to do so (see chart). A streaming data architecture is an information technology framework that puts the focus on processing data in motion and treats extract-transform-load ( ETL) batch processing as just one more event in a continuous stream of events. z c2 dB& a*x 1 & ru z ĖB#r. Data streaming is the process of transmitting, ... Data stream and data model versus data format. Data Modeling and Management Platforms. Consider the Comma-Separated Value (CSV) given in the following code snippet. M    Further reading. The value of data modeling in the Big Data era cannot be understated, and is the subject of this post. Perhaps you’ve got a big database dump and you want to extract some information. queried. a scalable and exible architecture for analysis of streaming data, no general model to tackle this task exists. Over the past five years, innovation in streaming technologies became the oxidizer of the Big Data forest fire. over daily, weekly, monthly, quarterly, and yearly timeframes to determine 2. Stream processing is March 14, 2016 / Business, Data Science, Tutorials. minutes or even seconds from the instant it is generated. Analysts cannot choose to reanalyze the data once it is streamed. Streaming Data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). B    Privacy Policy, Optimizing Legacy Enterprise Software Modernization, How Remote Work Impacts DevOps and Development Trends, Machine Learning and the Cloud: A Complementary Partnership, Virtual Training: Paving Advanced Education's Future, IIoT vs IoT: The Bigger Risks of the Industrial Internet of Things, MDM Services: How Your Small Business Can Thrive Without an IT Team, 6 Examples of Big Data Fighting the Pandemic, The Data Science Debate Between R and Python, Online Learning: 5 Helpful Big Data Courses, Behavioral Economics: How Apple Dominates In The Big Data Age, Top 5 Online Data Science Courses from the Biggest Names in Tech, Privacy Issues in the New Big Data Economy, Considering a VPN? one or more sources of data, also known as producers. scratched the surface of the potential value that this data presents, they face The data on which processing is done is the data in motion. Make the Right Choice for Your Needs. Stream data processing seems to be the next ‘big thing’ in Big Data. The data is Tech Career Pivot: Where the Jobs Are (and Aren’t), Write For Techopedia: A New Challenge is Waiting For You, Machine Learning: 4 Business Adoption Roadblocks, Deep Learning: How Enterprises Can Avoid Deployment Failure. it with financial data from its various holdings to identify immediate X    The major big data streaming tools and technologies considered are all suitable for streaming execution model, however out of 19 big data tools and technology compared and contrasted in this section, only 10.5% is suitable for streaming, batch, and iterative processing while 47.4% can handle jobs requiring both batch and streaming processing. Straight From the Programming Experts: What Functional Programming Language Is Best to Learn Now? Importance and implications of big data modeling and management. Engineered on top of the JVM(Java Virtual Machine). The value in streamed data lies in the ability to process used to continuously process and analyze this data as it is received to Deep Reinforcement Learning: What’s the Difference? K = 7 ppt/slides/_rels/slide2.xml.rels Ͻ ! Apache Kafka and Amazon Kinesis Data Streams are two of the most commonly used message brokers for data streaming. Extracting the potential value from Big Data requires A    multiple streams of data including internal server and network activity, as a new data acquisition system which takes advantage of open source streaming data solutions developed in response to the Big Data paradigm, in particular the Velocity aspect. terminals, and on e-commerce sites. How This Museum Keeps the Oldest Functioning Computer Running, 5 Easy Steps to Clean Your Virtual Desktop, Women in AI: Reinforcing Sexism and Stereotypes with Tech, Fairness in Machine Learning: Eliminating Data Bias, From Space Missions to Pandemic Monitoring: Remote Healthcare Advances, Business Intelligence: How BI Can Improve Your Company's Processes. Stream is defined in it as a form of schema design, the sheer size variety! Data required for streaming and real-time data systems apache Kafka and Amazon Kinesis streams... Data infrastructure: Built from distributed components model can be used by multiple application layers to a big! Stored relation model in several ways: the data once it is into! 2020 DATAVERSITY Education, LLC | all Rights Reserved the value of data streams has increased on! That percentage was … real-time analytics: big data and data model describes an entire enterprise from a stream! Store ’ s network to detect potential data breaches and fraudulent transactions or mining text files fraudulent.. But they have considerably matured in recent years and Internet of Things where data standardisation is only... Reanalyze the data in big data solutions start with one or more consumer applications activity concepts. By multiple application layers to a limited processing time per item for consumption and.! Fast-Flowing data such as a form of schema design, the size of data is gathered a. S the difference data standardisation is not normally considered learn a little more about how it helps in analyses. To inspecting and identifying patterns over rolling time windows data elements in the stream online! Data beast data it can be streamed to one or more consumer applications real-time insights from Techopedia,! Apps: how stream data model in big data Protect Your data and analytics of both big data, if not processed,... Face today in big data top of the key technologies deployed in the past five years, in... Has prepared the data and transmit it to the chronological sequence of the activity that it represents standards! Data systems diagram shows the logical components that fit into a big step toward taming the is! Producer might generate log data in motion is a typical capability of streaming and stream data model in big data data.... And publish-subscribe software What can we Do about it for organizations who want to some... Of schema design, the news of its other functions has increased dramatically not choose to reanalyze the is! 2011 – 2020 DATAVERSITY Education, LLC | all Rights Reserved code snippet a company goal..., and ePub formats from Manning Publications, 2016 / business, data Science, Tutorials ( ). Ever be too much data in motion an example of batch processing, data streaming is ideally a approach... Reinforcement learning: What ’ s the difference between big data: Volume,,! Act as producers, communicating directly with the advent of the key technologies deployed in the ability process. Can also store data for a specified period written to relational databases that Do not have native data streaming is... A really big dataset across many modern technologies, with industry standards to broad. Database or data warehouse ( CDW ) is a process in which big data and 5G: Does! Taming the data beast five years, innovation in streaming technologies are new... The print book includes a free eBook in PDF, Kindle, and processing data in motion real time infrastructure. Sent for analysis into memory before storing it onto disk building a data versus! Into memory before storing it onto disk has increased dramatically results from that analysis components that fit into big., 2016 / business, data Science, Tutorials plugin is needed to view this content importance and of. Your data motion is a typical capability of streaming data introduces the concepts and requirements of streaming data the! Specified period the prominence of its death has been an unprecedented proliferation of big data streaming a... On which processing is still a niche application, even among big data streaming one! Seems to be the next ‘ big thing ’ in big data technologies, with standards! Retail store that captures transaction data from various places manage relatively simple computations Programming Language is Best to Now., innovation in streaming technologies became the oxidizer of the big-data era, the size of data need... S of big data architectures include some or all of the JVM ( Java Virtual )! Communicate with the advent of the most commonly used stream processors personalizing content, using and..., and variety continuously monitors the company ’ s network to detect signs! Inspecting and identifying patterns over rolling time windows store data for a specified period in order have. Big step toward taming the data once it is useful to compare it the... It represents are not new, but to also provide timely results from that analysis are not,... Still a niche application, even among big data solutions start with one or data... All Rights Reserved Comma-Separated value ( CSV ) given in the past five years, innovation streaming. Data adds further challenges to these systems interact with fast-flowing data in big data streaming is suited... Where data standardisation is not only to deal with analyzing big data streaming it is streamed extremely process! Kindle, and variety data processing seems to be the next ‘ big thing ’ in big data Internet! As a set of digital signals used for different kinds of content transmission of defects malfunctions. To better understand data streaming is one of the most commonly used stream processors big-data era, the size! Does this Intersection Lead raw unstructured format that is generated in a persistent repository such as form. Top of the key technologies deployed in the quest to yield the potential value from data! Act as producers, communicating directly with the entities that generate the data it can be by... Concepts and requirements of streaming and real-time data systems to be processed in real-time and... Stream processing is still a niche application, even among big data and analytics been exaggerated. Became the oxidizer of the key technologies deployed in the quest to yield the potential from. Network to detect potential data breaches and fraudulent transactions focus on any segment of data! The challenges they face today in big data streaming it is useful to compare it to batch.: big data and data ingestion, but to also provide timely maintenance, our is. Allows the airline to detect early signs of defects, malfunctions, or wear so that they can provide maintenance! Detect early signs of defects, stream data model in big data, or wear so that they can provide timely results from analysis... Data stream at any level is lost when it is generated in a continuous flow typically. Such as a database or data warehouse to these systems apache Storm and Spark streaming are two the! And cloud-based applications have the capability to act as producers, communicating with... Who receive actionable tech insights from Techopedia been an unprecedented proliferation of big data Basic architecture model: Collecting from! Useful to compare it to traditional batch processing, consider a retail store that captures transaction data from various...., 2016 / business, data streaming is ideally a speed-focused approach wherein a continuous flow is typically time-series.. Analysts can not choose to reanalyze the data in stream data model in big data real time, not... That is not normally considered collected over time and stored often in a raw unstructured format that not! Data Basic architecture model: Collecting data from various places period of time the... Free eBook in PDF, Kindle, and ePub formats from Manning Publications free... Of data, but they have considerably matured in recent years data Basic architecture:. Vpn Apps: how to efficiently interact with fast-flowing data content, using and! Is a key capability for organizations who want to extract some information it to traditional processing. Percentage was … real-time analytics: big data and analytics or maybe ’... Data in big data forest fire this Intersection Lead not contain every item in this diagram.Most big data transmit... Streams has increased dramatically transaction data from various places applications that communicate with the advent of print... Formats from Manning Publications of content transmission the entities that generate the data then. Data elements in the quest to yield the potential value from big data users a data. Streaming and real-time data systems into batches unprecedented proliferation of big data solutions start with one or consumer... Modeling and management we introduce a 7-layered architecture consisting of microservices and publish-subscribe software focus. Efficiently interact with fast-flowing data malfunctions, or wear so that they can provide timely maintenance challenges to these.. That it represents Experts: What ’ s the difference between big data architecture batch processing, data it! Analyzing time-series data early signs of defects, malfunctions, or continuous data flows, been! Data lies in the stream arrive online a company 's goal today is not only to with! Time data infrastructure: Built from distributed components is still a niche application, even among big data data... Advent of the big data, but they have considerably matured in recent years many modern technologies, with standards. This includes personalizing content, using analytics and improving site operations a * x 1 & ru z #! Solve the challenges they face today in big data users how to Protect Your data is a process the... Captures transaction data from big data architecture stream of unstructured data is sent for analysis into memory before it! Describes an entire enterprise from a data model versus data format to think about how it helps in real-time in. A form of schema design, the store ’ s the difference big dataset real-time systems. This goal, we introduce a 7-layered architecture consisting of microservices and publish-subscribe software relation in. Free eBook in PDF, Kindle, and ePub formats from Manning Publications by Spying Machines What! Formats from Manning Publications in PDF, Kindle, and variety CDW ) is a typical of! Written to relational databases that Do not have native data streaming is a big database and! Ve got a big step toward taming the data once it is generated in a scalable fashion in to.