Expected Properties of a Big Data System

Prerequisite - Introduction to Big Data, Benefits of Big Data

There are various properties that mostly rely on complexity as per their scalability in big data. As per these properties, Big data systems should perform well, efficiently, and reasonably well. Let’s explore these properties step by step.

Properties of Big Data Systems

• Robustness and error tolerance – As per the obstacles in distributed systems encountered, it is quite arduous to build a system that “does the right thing”. Systems are required to behave in the right manner despite machines going down randomly, the composite semantics of uniformity in distributed databases, redundancy, concurrency, and many more. These obstacles make it complicated to reason about the functioning of the system. Robustness of big data systems is the solution to overcome the obstacles associated with it. It’s domineering for the system to tolerate human fault. It’s an often-disregarded property of the system that can not be overlooked. In a production system, it's domineering that the operator of the system might make mistakes, such as by providing an incorrect program that can interrupt the functioning of the database. If re-computation and immutability are built into the core of a big data system, the system will be distinctively robust against human fault by delivering a relevant and quite cinch mechanism for recovery.

• Debuggability – A system must be debug when unfair thing happens by the required information delivered by the big data system. The key must be able to recognize, for every value in the system. Debuggability is proficient in the Lambda Architecture via the functional behaviour of the batch layer and with the help of re-computation algorithm when needed.

• Scalability – It is the tendency to handle the performance in the context of growing data and load by adding resources to the system. The Lambda Architecture is straight scalable diagonally to all layers of the system stack: scaling is achieved by including further number of machines.

• Generalization – A wide range of applications can be function in a general system. As Lambda Architecture is based on function of all data, a number of applications can run in a generalized system. Also, Lambda architecture can generalize social networking, applications, etc.

• Ad hoc queries – The ability to perform ad hoc queries on the data is significant. Every large dataset contains unanticipated values. Having the ability of data constantly provides opportunities for new applications and business optimization.

• Extensibility – Extensible system enables to function to be added cost effectively. Sometimes, a new feature or a change to an already existing system feature needs to reallocate of pre-existing data into a new data format. Large-scale transfer of data become easy as it is the part in building an extensible system.

• Low latency reads and updates – Numerous applications are needed the read with low latency, within a few milliseconds and hundred milliseconds. In Contradict, Update latency varies within the applications. Some of the applications needed to be broadcast with low latency, while some can function with few hours of latency. In big data system, there is a need of applications low latency or updates propagated shortly.

• Minimal Maintenance – Maintenance is like the penalty for developers. It is the operations which is needed to keep the functionality of the systems smooth. This includes forestalling when to increase the number of machines to scale, keeping processes functioning well, along debugging. Selecting components with little complexity plays a significant role in minimal maintenance. A developer is always willing to rely on components along with quite relevant mechanism. Significantly, distributed database has more probability of complicated internals.

• Data Security and Privacy - In a big data system, protecting sensitive information is a top priority. These systems often handle personal data, financial records, or business insights—so it's crucial to keep it safe from hackers or leaks. Security in big data isn't just about locking things down; it’s also about making sure only the right people can access the right data. To achieve this, we use data encryption(which locks the data so only authorized users can read it), access control (which ensures people only see what they’re allowed to), and audit logging (which tracks who accessed what and when). These simple but effective practices help build trust and keep systems secure, even at massive scale.

• Integration with Machine Learning - Modern big data systems don’t just store data—they help us learn from it. By connecting with machine learning (ML) tools, these systems can analyze patterns and make smart predictions. This means businesses can spot problems early, understand customer behavior, or even automate tasks based on data insights. For example, ML can help detect fraud, predict equipment failures, or recommend products to users. With big data feeding these models, the results become more accurate and useful over time—turning raw information into real-world action.

Big Data Processing Frameworks

Here's a overview of key frameworks used in big data processing:

Apache Hadoop

• What It Is: A distributed computing framework for handling large datasets.
• Key Features:
  • HDFS: Stores data across multiple machines.
  • MapReduce: Processes data in parallel.
• Best For: Batch processing of huge datasets.

Apache Spark

• What It Is: A faster, in-memory processing framework.
• Key Features:
  • Processes data much faster than Hadoop.
  • Supports batch and real-time processing.
• Best For: Speedy, flexible data processing and machine learning.

Apache Flink

• What It Is: A real-time stream processing framework.
• Key Features:
  • Processes data as it arrives (real-time).
  • Supports batch processing as well.
• Best For: Low-latency, real-time data processing.

MapReduce

• What It Is: A data processing model used in Hadoop.
• Key Features:
  • Divides tasks into "Map" and "Reduce" phases.
  • Processes data in parallel.
• Best For: Large-scale batch processing, but slower than newer frameworks.

Choose based on your processing speed, data type, and real-time requirements.

Conclusion

Big data systems are constructed to manipulate vast amounts of data with maximum efficiency, security, and intelligence. These systems must be scalable, reliable, easy to debug, and accommodate all real-time and batch data effectively to perform their objectives. Other appealing features, such as low-latency updates, very high levels of security, low maintenance, and machine-learning integration, can increase their power.

With processing frameworks such as Hadoop, Spark, and Flink, companies can choose when they need speed, scale, or real-time insight the exact right tool for their requirements. In other words, the large-scale data systems serve as a backbone for contemporary data-intensive decision-making.