Cut the Clutter: The key to Big Data management

By Indraneel Ghosh

With the rise in automation in the world, there has been a rise in the amount of data that is being generated due to the interaction of these “sensor-based” systems. It is predicted that the network of these “sensor-based devices” would rise beyond 50 billion by the year 2025. This network of devices forms the components of a network that is known as the “Internet of Things” (IoT). These devices need frameworks which allow the transport of data over wired or wireless networks in a secure manner. Some of the more commonly used framework standards include OneM2M and Google’s Physical Web.

The various processes involved in the interaction of these devices lead to the generation of large amounts of unstructured data. The data generated, can provide new and valuable insights into the behaviour of the world which can help companies to make smarter decisions. But failure to use it efficiently would lead to the generation of irrelevant data. The data would be a waste of storage space, which is uneconomical for the company.

Data overload

The quantity of data being generated by these devices today is too large for the traditional data management techniques to handle. This has made it necessary to devise methods to manage the massive amounts of data generated by these IoT based devices to further improve their performance. The modern methods of data management can be used to track the collection and storage of monitoring sensor data, data analytics and making forecasts. For instance, for an IoT based device, they could help ensure that a given device is subjected to maintenance only when required by incorporating a responsive maintenance mechanism in the device. This can help optimize the performance of the device and save money.

Yet, all is not rosy about these recent developments. There have been concerns about data overload and the rise in possible security vulnerabilities. In fact, this is one of the primary reasons that make everyone wary of the use of IoT based devices. Another major issue is the lifetime of the methods that are being used today. The present-day methods may seem to do the job well as of now. But they wouldn’t be able to cope with the rate at which the generation of data is increasing after a few years. This has led to companies investing heavily in research. They aim to build infrastructure that could address this issue.

General process of IoT data management

The structure of a traditional Database Management System (DBMS) and the IoT systems are different. Unlike the traditional DBMS, an IoT system receives data from a variety of sources like sensors, RFIDs and mobile devices. To manage such large quantities of data and draw meaningful conclusions from it, IoT employs multi-layer data management frameworks. Each of the layers processes the data in various aspects. The framework ensures that the data is secure and at the same time redundant data is eliminated. It eliminates the repetitive and redundant data pieces, saving space and processing power.

Some of the frameworks that are being used to manage the different types of data are:

Hadoop

Hadoop is an open-source framework which can process, store and analyze a given data. It distributes a large block of data into smaller constituents. Then these smaller blocks undergo processing. The output of the process is a well-analyzed piece of data.

Some of the key components of Hadoop are:

1. HDFS: HDFS, or Hadoop Distributed File System, which serves as the default storage layer for the data.
2. MapReduce: MapReduce executes many smaller functions simultaneously. After processing, it ‘reduces’ the data into a single value result.
3. YARN: YARN ensures efficient cluster management and scheduling of user applications.
4. Spark: Using Spark on top of the HDFS layer can lead to a multi-fold rise in the processing speed. This can help us in obtaining our results much quicker.

Not Only SQL (NoSQL)

NoSQL is adept at processing dynamic, semi-structured data. They are better suited for tasks where data is being processed in real-time. In contrast, Hadoop is better suited for an in-depth analysis of the entire data is available. As a result, both these frameworks are quite often marketed together.

Massively Parallel Processing (MPP)

As the name suggests, MPP is proficient in parallel processing of massive chunks of data concurrently. The data processing involves the use of hundreds of processors simultaneously. All these processors work on different parts of the same programme. This makes MPP a more expensive setup.

Cloud computing

Cloud computing is a service generally provided by a third-party organization. The user requires accessing a web-based service to shift all the necessary resources and programs. This is then processed by a third-party organization and the results are returned to the client. The biggest advantage of using cloud computing is that a company doesn’t need to invest a massive amount of money to set up the infrastructure. They send it to the third-party organization who manage the data. But there exists an obvious risk in giving the data to a third-party organization for performing the analysis. There are different kinds of cloud platform services provided by these companies to meet the needs of the consumer. The consumer companies store sensitive information in a private cloud. Other forms of information can be stored on public or hybrid cloud platforms. The use of graph DBMS in sync with the existing file management systems can improve the efficiency of cloud data storage. Companies which offer these third-party services include EMR for Hadoop and BigQuery for Google.

These methods help process the data into a more structured and comprehensible form. Following this, the data can be used for drawing various conclusions and can improve the performance of the devices.

What next?

With a major chunk of the companies shifting towards the use of big data analytics for solving problems, this sector has the potential to grow leaps and bounds in the coming years. Despite the enormous progress that has been made in the field of big data analytics, we are not prepared to handle the large amounts of data that is expected to be generated in over the next decade yet. As the technologies used in these devices improve, IoT connected devices would be able to collect more data about the world around them.

Further, there are problems with the management systems that are in use today. One of the major issues that need to be sorted is the security of data, with there being a significant number of reports about hacking and data breaching. Another major concern is the orchestration of workflows and processes for those trying to implement IoT. This is being countered by the rise in the integration of machine learning algorithms and artificial intelligence with IoT. This integration can lead to a drastic change in the efficiency of the management of the deluge of data that is at our disposal. In fact, to put things into perspective, it can be said that “IoT forms the senses, Big Data the fuel and Artificial Intelligence the brains of a smarter and well-integrated future.”

The only thing that seems constant in the field of IoT is that it’s evolving, even surpassing modern expectations and standards. While still a primitive field, it shows a lot of promise and at the same time throws up a lot of challenges. Computer scientists are responsible for ensuring that the data management methods can keep pace with the rising deluge of data. Efficient management of this data generated can go a long way in making the world a “smarter” place to live in.

Featured Image Source: luckey_sun on Visual Hunt / CC BY-SA