Big Data: An overview

Big data refers to extremely large, diverse, and fast-moving datasets that traditional tools can't easily manage. It includes structured, unstructured, and semi structured data that continues to grow exponentially over time. 

In structured data, information is arranged in a predefined format, mostly in tables, in the form of rows and columns. Data can be quantitative and qualitative. Structured data is mostly stored in relational databases and spreadsheets. An example of structured data is customer, e-commerce, employee data. In unstructured data, information is not arranged in a predefined format. It can not be stored in traditional relational databases and is instead stored in data lakes .A data lake is a centralized repository, where data is stored in its raw form. Data can be quantitative and qualitative. Some examples of unstructured data are emails, data from social media, images, audio files, and video files. Semi structured data has both the components of structured and unstructured data. A few examples of semi structured data are XMl file and JSON objects. They have a flexible schema.

Traditional data and big data differs in many ways. The main difference is volume. Traditional data has manageable volume, and the volume can be in gigabytes to terabytes. Big data has massive volume, and that volume can be in petabytes to zettabytes. Next is the type of data they contain. Traditional data contains structured data whereas big data contains structured, unstructured, and semi structured data. The tools used to analyze them are also different. For traditional data, we use SQL and Relational Databases. For big data, we use machine learning, data mining, and data visualization.   

Now, let's talk about the V's of Big Data: volume, velocity, variety, veracity and value. 

These are the main characteristics of big data.

Volume - A massive amount of data is generated everyday from sensors, social media, and transactions etc. To manage a vast amount of data, we use cloud based storage.

Velocity -  The speed at which data is generated and analyzed. It is very important to get insight from real time data, such as data from stocks and data from social media etc.

Variety - Big data may have data in different formats such as audio, video, text, images etc in the form of structured, unstructured, and semi structured data. Managing and getting insight from such diverse data is a challenge.

Veracity - reliability  and accuracy of data is also challenging because it is getting data from various sources and in a very vast amount.

Value - It refers to the benefit we or the organization are getting from the big data. These benefits are the insights we get by analyzing and processing the large datasets. 

Data lakes, data warehouses, and data lakehouses are used to store big data.

Data lakes are repositories where raw data is stored. Data lakes do not clean and validate data. This makes data lakes cost effective. Stored data in data lakes is used for AI training, machine learning, and data analytics.

Data warehouses consist of central repositories which collect data from different sources. They clean and validate data so that the stored data is ready to use. Also, they transform stored data into a relational format so it is ready to be used by analysts. 

Data lakehouses have both the properties of data lakes and data warehouses but they are very complex to maintain

In big data, Hadoop, Apache Spark, and NoSQL databases are three of the most commonly used data processing tools. Hadoop is an open source framework. This framework helps the Hadoop Distributed File System (HDFS) to manage large data efficiently. Apache Spark works well with real time data analytics. NoSQL databases are designed to work well with unstructured data.

Big data benefits organizations in many ways:

Decision making – Organizations are analyzing big data. They are getting better insights and patterns. By considering these insights and patterns, organizations are making  better decisions for the organization, employees and customers.

Agility and innovation – Organizations can analyze real time large datasets and can get insight. This helps organizations to make the right decisions at the right time. These insights help them to plan better to produce and launch new products

Better customer experiences – Using big data’s feature to analyze structured and unstructured data, organizations get a better understanding of customer behavior and they can use this knowledge to give customers a better experience.

Continuous intelligence – By allowing big data’s advanced analysis of real time data, organizations are getting new insights continuously. By applying these insights, the companies are benefiting continuously.

More efficient operations – Big data’s analytics tools help organizations find the areas where they can reduce cost, save time, and become more efficient.

Improved risk management – Big data can evaluate massive amounts of data which helps organizations evaluate risk better. Early and better insight helps them respond quicker and better.

Challenges of big data analytics:

• Getting skilled and talented data scientists, data analysts, and data engineers

• Big data is changing and growing every moment so proper infrastructure is needed to handle it

• Storage and processing because of its ever-changing behavior

• Data quality because it is directly related to decision making. Messy data will lead the organization in a wrong direction.  

• Data privacy and regulatory requirements need to be followed because data may contain sensitive information.

• Data integration because an organization gets different kinds of data from various sources. To analyze, it needs to be integrated which is a challenge.

• Security because big data contains important information about businesses and its customers. This makes big data an alluring target for attackers.

Despite these challenges, data driven organizations are benefitting from big data. These organizations include healthcare, retail, finance, and marketing. In health care, big data can be used for prediction of diseases, writing better prescriptions, and early intervention of providers before patients reach a life threatening condition. In retail, big data can be used to maintain the inventory and give a better experience to the customers. In finance, big data can be used to detect fraud, as well as finding new trends. Though we have just started using it, the potential for big data is immense.