Data engineering for IoT (Internet of Things) applications.
Currently, the tech world is abuzz with constant updates surrounding Threads, the newest social media platform and a budding competitor to Twitter. Launched on July 6, 2023 and with over 23.6 million active users, Threads opens up a new way to share updates via SMS. With the internet welcoming this new app, conversations once again veered towards massive IoT data production, maintenance, and data engineering.
According to Statista, the number of Internet of Things devices is projected to exceed 29 billion in 2030. Major industries with more than 100 million connected IoT devices include transportation & storage, steam & air conditioning, electricity, gas, government, etc. When used in an industrial setup or a residential environment, these devices produce massive amounts of data. Subsequently, this data undergoes further analysis and segregation.
And it’s here data engineering enter the scene!
What is Data Engineering?
Data engineering delivers data in a standardized structure, ensuring zero data duplication and proper evaluation of all data blocks. Through data engineering, data becomes more reliable and data delivery occurs with acceptable delays.
For medium to large businesses, data processing is a big deal. Experienced and highly professional data scientists build robust data pipelines and collaborate with software engineers and data engineers to make data accessible, configure databases, transform data, and optimize data systems.
According to research conducted by the Burning Glass Nova platform, the demand for data engineers has seen a sharp increase in 2016. As the application of data in the IoT gradually increases, companies are looking forward to investing more in data extraction and data distribution.
What is the scope of IoT data engineering?
With the growing use of Internet of Things devices, data engineering improves decision making and also improves customer relationships, supply chain management and targeted marketing. Aside from that, here are some of the most significant ways data engineering can be used:
- Transform data from one format to another
- Clean your data and standardize it
- Summarize and combine data
How can IoT data insights help businesses?
When companies use machine learning with data engineering, they can transform IoT data into valuable business insights. From structuring and analyzing data at scale to implementing intuitive dashboards: data engineering completely revolutionizes the way data is used in companies.
How can IoT data engineering help midsize and large companies?
There are a number of agencies that provide services related to IoT applications. These mainly include analytics services, engineering services and solutions related to predictive analytics.
When discussing service scope, it is important to remember that IoT services fall into four different categories: analytics, consulting, implementation, and support.
1. Analyses:
With analytics, companies can transform complex data into IoT and produce insights, including data analytics at scale. Next, companies work on detailed dashboards and use artificial intelligence or machine language to enable predictive analytics.
2. Advice:
When businesses work with Internet of Things devices, it becomes essential to uncover the roots of all technology failures. Experienced companies provide in-depth solutions by analyzing the roots of problems and evaluating different IoT frameworks and platforms.
3. Implementation:
Companies teaming up with brands to accelerate their revenues, provide customizable end-to-end solutions for IoT applications. This process begins with understanding the architecture design and concept development for MVP/PoC implementation, production implementation, proper maintenance and ongoing support.
4. Support:
After getting insights from IoT data, companies need to continuously monitor their infrastructure and hence, require reputable agencies to gather support services. This includes automation, administration, cloud infrastructure optimization, etc.
What are the challenges of IoT data engineering?
The magnanimity of data volume and speed can be overwhelming at times. To manage data in flux requires a solid infrastructure and robust data processing systems.
IoT data comes in all formats. It 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.
Data quality and reliability often becomes questionable when threatened by network outages, sensor malfunctions, etc. In such cases, data cleansing and validation processes become crucial to eliminate the scope of errors or mismanaged data.
In today’s world, data security and privacy require serious attention. Therefore, adequate security measures must be taken to prevent the leakage of sensitive information.
Final thoughts
The global data engineering services market is projected to expand to $87.37 billion by 2025, with an impressive CAGR of 17.6% compared to $39.50 billion in 2020. Thus, data integration and fusion improve decision-making, streamline processes and systems, and help in real-time data processing. IoT data engineering can help IoT unleash its true potential by leveraging AI and ML techniques. Therefore, organizations and brands of all sizes and across various industries should seize every opportunity to apply data engineering to enhance the possibilities and impact of IoT devices.