Data Engineering for IoT (Internet of Things) applications

Data Engineering for IoT (Internet of Things) applications

Data Engineering for IoT (Internet of Things) applications

Currently, the world of technology is abuzz with constant updates surrounding Threads – the newest social media platform and a budding competitor of Twitter. Launched on 6th July 2023 and with more than 23.6 million active users, Threads opens up a new way of sharing updates through texts. With the internet welcoming this new app, the conversations have once again steered towards huge data production, maintenance and IoT data engineering.

 As per Statista, the number of Internet of Things devices is predicted to cross 29 billion in 2030. The principal industries with more than 100 million connected IoT devices include transportation & storage, steam & A/C, electricity, gas, government, etc. Whether used in an industrial setup or residential environment – these devices produce enormous chunks of data. Later on, this data goes through further analysis and segregation.

And this is where data engineering comes into the picture!

What is Data Engineering?

Data engineering delivers data in a standardised structure, ensuring zero data duplication and proper assessment of all data chunks. Through data engineering, data becomes more reliable and data delivery is done with acceptable delays.

For medium-sized companies and large-scale enterprises, data processing is a big issue. 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 optimise data systems.

As per the research conducted by the Burning Glass Nova platform, the demand for data engineers has witnessed a steep rise in 2016. As the application of data in IoT increases gradually, companies are looking forward to investing more in data extraction and distribution.

What is the scope of IoT Data Engineering?

With the increasing usage of Internet of Things devices, data engineering improves decision-making and also enhances customer relations, supply chain management and target marketing. Apart from that, here are some of the most significant ways data engineering can be used:

  • Transform data from one format to another
  • Clean data and standardise it
  • Summarise and combine data

How can the insights from IoT data help companies?

When companies employ machine learning with data engineering, they can transform IoT data into valuable business insights. From structuring and analysing data at scale to implementing intuitive dashboards – data engineering completely revolutionises data usage in companies.

How can IoT data engineering help medium and large companies?

There are numerous agencies providing services related to IoT applications. These mainly include analytics services, engineering services and solutions related to predictive analytics.

While discussing the scope of service, it is relevant to mention that IoT services fall under four different heads – analytics, consulting, implementation and support.

1.   Analytics:

Under analytics, companies can transform complex data in IoT and churn out insightful information, including full-scale data analysis. Following this, companies work on detailed dashboards and use artificial intelligence or machine language to enable predictive analytics.

2.   Consulting:

When companies work with Internet of Things devices, it becomes essential to find out the roots of all technological breakdowns. Expert companies provide in-depth solutions analysing the roots of the problems and evaluating different IoT frameworks and platforms.

3.   Implementation:

Companies that join hands with brands to accelerate their revenues, provide end-to-end customisable solutions for IoT applications. This process starts with understanding the architecture design and developing the concept to MVP/PoC implementation, production rollout, proper maintenance and continued support.

4.   Support:

After receiving insights from IoT data, companies need to continuously monitor their infrastructure and thus, they require reliable agencies to gather support services. This includes automation, administration, cloud infrastructure optimisation, etc.

What are the challenges of IoT Data Engineering?

The magnanimity of data volume and velocity can be overwhelming at times. Strong infrastructure and robust data processing systems are required to handle the ever-flowing data.

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 disruptions, 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 demand serious attention. Therefore, proper security measures are to be taken to prevent sensitive information from leaking out.

Final Thoughts

The global data engineering services market is predicted to expand to $87.37 billion by 2025, growing at an impressive CAGR of 17.6% from $39.50 billion in 2020. Therefore, data integration and fusion improve decision-making, optimise processes and systems and help in real-time data processing. IoT data engineering can help IoT unleash its truest potential by leveraging AI and ML techniques. Thus, organisations and brands of all sizes and belonging to various industries should embrace all opportunities of applying data engineering to enhance the possibilities and impact of IoT devices. 

Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.

Related Stories

No stories found.
Analytics Insight