Data Engineering Fuelling the Internet of Things (IoT) Revolution

1. DATA ENGINEERING FUELLING THE INTERNET OF THINGS (IOT) REVOLUTION www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

2. We live in a highly interconnected world. Not just humans, but today, even all sorts of devices are also interconnected. These interconnected devices, popularly known as the Internet of Things or IoT devices are transforming our world. Be it smart homes and wearable devices connected to cars or industrial machinery; these growing networks of interconnected devices are continuously generating huge amounts of data. In fact, a recent study from McKinsey reported that the value generated by IoT globally is projected to reach a staggering $3.9-$10 trillion by 2030. However, this exponential growth of data presents a significant challenge to address. If the organizations want to effectively use this data generated, then they must collect, manage, and analyze this ever-growing data effectively. This is where Data Engineering comes into play in bringing about the IoT revolution. CHALLENGES OF IOT DATA IoT data is characterized by three key features; Volume, Variety, and Velocity. VOLUME By 2025, it is expected that the global datasphere will be around 175 zettabytes of data (as per IDC). And a significant portion of this huge amount of data will come from the growth of IoT devices. So, handling such a huge amount of data for the betterment of the organization requires a robust infrastructure along with proper data processing pipelines. VELOCITY Not just that, in various functions, data is generated in real-time. So, in applications in various industries such as healthcare where remote patient monitoring and in the manufacturing industry predictive maintenance require real-time analysis, it becomes very important to extract insights in a timely manner. VARIETY Another challenge that data science professionals often face is the variety of data formats that come from various sources such as sensors and devices. They add an extra layer of complexity to data for processing. So, data engineers have to properly address these challenges and ensure there is a seamless flow of data and an efficient data analysis. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

3. CORE FUNCTIONS OF DATA ENGINEERING FOR IOT Data engineering for IoT applications mainly focuses on building an efficient data pipeline that can handle the data lifecycle properly, right from data ingestion to data analysis. Here are some important data engineering functions for IoT: 1. DATA INGESTION This refers to the collection of data from varied IoT sources. MQTT, HTTP, CoAP, and others are some of the common protocols used for the same. Data engineering helps design an effective mechanism that can collect data from various sources and different formats and transfer them to proper places like data silos, or data lakes. 2. DATA PROCESSING AND TRANSFORMATION The raw data collected are mostly unstructured, improper, and inadequate. They need to be cleaned, corrected, and processed before they can be used for analysis. Data engineering is important for such processes. • Data cleaning and standardization: Data engineering helps to find errors in the data, missing values, and redundant elements, and correct them. It also ensures that the data format is consistent. Data filtering and aggregation: in this step, data is filtered to remove irrelevant data, and with the aggregation, summarizes data for easier analysis. • 3. DATA STORING AND MANAGEMENT Now, the important step in data processing and analysis is storing and managing data. For this, the right storage solutions should be chosen which depends on the specific needs of the application. CHOOSE THE RIGHT STORAGE SOLUTION • Data lakes: Best for storing large volumes of raw and heterogeneous data Data warehouses: Helpful in structured data analysis and reporting Time-series Databases: great for storing and analyzing time-stamped data from sensors and devices. • • CONSIDER SCALABILITY AND PERFORMANCE Organizations must also consider data engineering solutions that are scalable and can handle the ever-growing volume of data efficiently in the long run. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

4. UNDERSTANDING BIG DATA 4. DATA PROCESSING AND ANALYTICS When it comes to IoT applications, real-time monitoring becomes the most important element of data engineering. So, consider these: • Stream vs. Batch Processing: While stream processing is helpful in the continuous analysis of data right when it arrives, batch processing is best for historical data analysis. Integration with machine learning and AI for advanced analytics: with the use of advanced technology like Machine Learning and AI algorithms, valuable insights can be extracted from IoT data. This helps in predictive maintenance, anomaly detection, and automated decision-making. • APPLICATIONS OF DATA ENGINEERING IN IOT Data engineering facilitates efficient data management and analysis thereby empowering various IoT applications. PREDICTIVE MAINTENANCE In the manufacturing industry, sensory data are analyzed to predict equipment failures and schedule maintenance proactively. This further helps in reducing downtime and costly repair costs. A study by GE Healthcare found that IoT data analytics-powered predictive maintenance helped to reduce healthcare equipment downtime by up to 50%. SMART CITIES With data engineering, data collected from traffic sensors, connected grids, environmental monitors, etc. are analyzed to optimize traffic flow, and energy consumption management, and help improve resource allocation in cities. Frost and Sullivan estimated global smart city market will reach $1.8 trillion by 2025 and one dominating factor is the increasing adoption of IoT technologies. CONNECTED HEALTH A huge amount of data is generated by wearable devices and remote monitoring systems about patient’s vital signs, activity levels, and sleep patterns. With real-time analysis of these data serious health issues can be detected early and help to personalize treatment plans improving the overall healthcare process. 83% of physicians believe IoT will revolutionize patient care by 2025, as per a study by Accenture. www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

5. INDUSTRIAL AUTOMATION IoT sensors collect data on machine performance, production processes, environmental conditions, etc. in factories and industrial plants. Data engineering helps in predictive maintenance and optimization of production schedules and improves production quality. A PwC report estimated that AI and data analytics in manufacturing powered by IoT will become 30% more productive by 2030. ASSET TRACKING AND SUPPLY CHAIN MANAGEMENT IoT tags can be attached to vehicles, contained, inventory items, or other things, and organizations can track their location in real time. And by analyzing these data, logistics can become more efficient reducing shrinkage and optimizing supply chain operations. Real-time visibility through IoT in supply chains will help save $340 billion by 2025 globally. CONCLUSION Organizations can leverage data engineering in IoT and encourage a culture of collaboration between data engineers, data scientists, and domain experts to unlock the full potential of data in the IoT revolution. This will help in driving innovations and creating a more connected and intelligent world. LEARN THE DATA ENGINEERING FUELLING THE INTERNET OF THINGS (IOT) REVOLUTION www.usdsi.org © Copyright 2024. United States Data Science Institute. All Rights Reserved

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