What are some techniques and technologies used in big data analytics?

As the size of collected digital data grows, the ability to process big data becomes increasingly important. Improving the performance and cost of managing big datasets is vital for many industries.

Big data analytics are a set of processes that derive insights through correlations, associations, and trends found in large volumes of data. Big data analytics technologies may use multiple sources, use different formats, require structured and unstructured data, and scale up by orders of magnitude.

Examples of uses of Big Data Analytics include:

  • Fraud detection: Big data analytics can identify patterns of fraudulent behaviour, such as unusual account activity or suspicious transactions.
  • Customer segmentation: Companies can use big data analytics to divide their customer base into smaller, more specific groups based on common characteristics, allowing them to effectively tailor their marketing and sales efforts.
  • Predictive maintenance: By analysing data from sensors on machinery and equipment, companies can predict when maintenance will be needed and schedule it in advance, reducing downtime and increasing efficiency.
  • Supply chain optimization: Big data analytics can help companies optimise their supply chain by identifying bottlenecks and inefficiencies, enabling them to improve their delivery times and reduce costs.
  • Personalised medicine: Big data analytics can be used to analyse patient data and provide customised treatment recommendations based on an individual’s unique characteristics and medical history.
  • Traffic prediction and management: By analysing traffic data, cities can predict and manage traffic flow, improving the efficiency of their transportation systems and reducing congestion.
  • Natural language processing: Big data analytics can be used to analyse and understand large volumes of text data, such as customer reviews or social media posts, to extract insights and inform decision-making.

The examples of big data analytics mentioned above are only a few out of a plethora of uses. Big data analytics are set to change the world as we know it.

The projected job outlook shows an increase in demand during the next decade therefore it would be an excellent investment enrolling in an online MS in computer science to be part of the big data revolution.

Techniques and technologies used in big data analytics

Big data analytics aims to help organisations make more informed business decisions, improve operations, and increase competitiveness. To achieve these goals, various techniques and technologies are used in big data analytics. Here are a few examples:

Techniques for big data analytics

There are several techniques that are commonly used for big data analytics. These include:

Data cleansing and preparation

Before data can be analysed, it must be cleaned and prepared for analysis. Data cleansing involves identifying and correcting inaccuracies and inconsistencies in the data. This may include removing duplicates, standardising, and filling in missing values.

Data preparation involves organising and formatting the data to make it suitable for analysis. This may include selecting relevant data, transforming data into a usable format, and aggregating data from multiple sources.

Data visualisation

Data visualisation is the process of representing data in a graphical or visual format. To this end, some familiarity with graphics design could come in handy. It is an important technique for big data analytics because it allows analysts to quickly and easily understand and interpret large and complex data sets. Data visualisation tools and techniques include charts, graphs, maps, and dashboards.

Descriptive analytics

Descriptive analytics involves using statistical and graphical methods to describe and summarise data. They are used to understand and describe what has happened in the past, and to identify trends and patterns in the data. Common techniques for descriptive analytics include mean, median, mode, and standard deviation.

Predictive analytics

Predictive analytics involves using statistical and machine learning techniques to make predictions about future events or outcomes. They are used to forecast trends and identify potential risks and opportunities. Common techniques for predictive analytics include linear regression, decision trees, and neural networks.

Prescriptive analytics

Prescriptive analytics go beyond predicting the future and provide recommendations or suggestions for action. They combine predictive analytics with optimization algorithms to find the best course of action. Prescriptive analytics can be used to make recommendations for resource allocation, supply chain management, and other business decisions.

Technologies for big data analytics

Many technologies can be used for big data analytics. Some examples include:


Hadoop is an open-source software framework for storing and processing large and complex data sets. It is based on the MapReduce programming model, which allows developers to write programs that can process large amounts of data in parallel across a distributed network of computers.

Hadoop is widely used in big data analytics because it can handle large volumes of structured and unstructured data and scale to meet growing organisations’ needs.


Spark is an open-source data processing engine for large-scale data processing. It is designed to be faster and more flexible than Hadoop and can be used for a wide range of data processing tasks, including batch processing, stream processing, and machine learning. Spark is often used in big data analytics because it can process data in real time and handle a variety of data formats.

NoSQL databases

NoSQL databases are designed to handle large volumes of unstructured data and are often used in big data analytics. NoSQL databases are designed to be distributed, scalable, and flexible and can handle structured, semi-structured, or unstructured data. Examples of NoSQL databases include MongoDB, Cassandra, and Couchbase.

Data lakes

A data lake is a centralised repository that allows organisations to store all their structured and unstructured data at any scale. Data lakes are often used in big data analytics because they provide a single place to store and process data from a variety of sources, including sensors, social media, and transactional systems.

Cloud-based analytics platforms

Cloud-based analytics platforms, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, allow organisations to store and process big data in the cloud.

These platforms offer a range of services for big data analytics, including data storage, processing, and visualisation. A cloud-based analytics platform can help organisations scale their analytics capabilities and reduce the costs and complexity of managing their infrastructure.

Bottom line

Big data analytics involves a variety of techniques and technologies for examining and interpreting large and complex data sets. These techniques and technologies can help organisations make more informed business decisions, improve operations, and increase competitiveness.

By leveraging the right techniques and technologies, organisations can unlock the full potential of their data and gain valuable insights that drive business success.

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