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ວິສະວະກໍາຂໍ້ມູນສໍາລັບ Internet of Things (IoT) ຄໍາຮ້ອງສະຫມັກ. There's a lot of buzz in the tech world around the constant update of Threads, the newest social media platform and emerging competitor to Twitter.
ວິສະວະກໍາຂໍ້ມູນສໍາລັບ Internet of Things (IoT) ຄໍາຮ້ອງສະຫມັກ
Launched on July 6, 2023, with over 23.6 million active users, Threads opens up a new way to share updates via text. With the popularity of the Internet for this new application, the discussion has once again turned to massive data production, ບໍາລຸງຮັກສາ, and IoT data engineering.
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Statista predicts that the number of IoT devices is expected to exceed 29 ຕື້ໂດຍ 2030. Major industries with more than 100 million connected IoT devices include transportation and storage, steam and air conditioning, electricity, natural gas, government, and more. Whether used in industrial facilities or residential environments, these devices generate large amounts of data. This data is then further analyzed and isolated.
ວິສະວະກໍາຂໍ້ມູນສໍາລັບ Internet of Things (IoT) ຄໍາຮ້ອງສະຫມັກ
This is where data engineering comes into play!
What is Data Engineering?
Data engineering provides data in a standardized structure, ensuring zero data duplication and proper evaluation of all data blocks. With data engineering, data becomes more reliable and data transfer is completed with acceptable delays.
Data processing is a big problem for medium-sized businesses and large enterprises. Experienced and highly specialized data scientists build robust data pipelines and work with software engineers and data engineers to make data accessible, configure databases, transform data, and optimize data systems.
According to research on the Burning Glass Nova platform, the demand for data engineers rose sharply in 2016. As the use of data in IoT increases, businesses expect to invest more in data extraction and distribution.
What is the scope of IoT data engineering?
With the increasing use of IoT devices, data engineering can improve decision-making and enhance customer relationships, supply chain management, and targeted marketing.
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In addition to this, here are some of the most important ways to use data engineering:
Transform data from one format to another, clean the data, standardize, summarize, and combine the data
How can insights from IoT data help businesses?
When businesses use machine learning in conjunction with data engineering, IoT data can be turned into valuable business insights. From building and analyzing data at scale to implementing intuitive dashboards – data engineering has revolutionized the way businesses use data.
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How can IoT data engineering help medium to large enterprises?
There are many institutions that offer services related to IoT applications. These primarily include analytics services, engineering services, and solutions related to predictive analytics.
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When discussing the scope of services, it is necessary to mention that IoT services are divided into four distinct sections: ການວິເຄາະ, consulting, implementation and support.
1. Analysis:
Through analytics, businesses can transform complex data in IoT and generate insightful information, including comprehensive data analysis. ຕັ້ງແຕ່ນັ້ນມາ, companies have started making detailed dashboards and using artificial intelligence or machine language to enable predictive analytics.
2. Consultation:
When businesses use IoT devices, it becomes crucial to find the source of all technical failures. Expert businesses provide in-depth solutions, analyze the root cause of problems, and evaluate different IoT frameworks and platforms.
3. Implementation:
Businesses that partner with brands to accelerate revenue growth with end-to-end customizable solutions for IoT applications. The process begins with an understanding of architecture design and development MVP/PoC implementation concepts, production deployment, appropriate maintenance, and ongoing support.
4. ສະຫນັບສະຫນູນ:
Once insights are gained from IoT data, businesses need to continuously monitor their infrastructure, so they need a reliable agency to gather support services. This includes automation, ການຄຸ້ມຄອງ, cloud infrastructure optimization, and more.
What are the challenges of IoT data engineering?
The sheer volume and speed of data can sometimes be overwhelming. Robust infrastructure and data processing systems are needed to handle the constant flow of data.
IoT data comes in a variety of formats. Can be structured, semi-structured, and unstructured. By developing unique strategies and techniques, data scientists can transform data sources into suitable formats for proper data analysis.
ຄໍາຮ້ອງສະຫມັກ IoT - ເວທີຄລາວ iot
Data quality and reliability are often questioned when threatened by network outages, sensor failures, and more. ໃນກໍລະນີນີ້, the data cleansing and validation process is critical to eliminate the scope of errors or mismanagement of the data.
In today's world, data security and privacy issues are a big concern. ເພາະສະນັ້ນ, appropriate security measures must be taken to prevent the leakage of sensitive information.
summary
The global data engineering services market is expected to expand to $87.37 ຕື້ໂດຍ 2025, growing at a CAGR of $39.5 ຕື້ໃນ 2020 ກັບ 17.6%. As a result, data integration and fusion can improve decision-making, optimize processes and systems, and facilitate real-time data processing.
IoT data engineering
IoT data engineering can leverage artificial intelligence and machine learning techniques to help IoT unlock its true potential. ເພາະສະນັ້ນ, organizations and brands of all sizes and industries should seize all opportunities to apply data engineering to enhance the possibilities and impact of ອຸປະກອນ IoT.
