Data storage is a crucial aspect of any organization, as it enables them to securely store, manage, and access their data. With the increase in the amount of data being generated, traditional storage methods such as hard drives and local servers are becoming less efficient and cost-effective. This has led to the emergence of cloud storage solutions, which provide organizations with scalable and flexible storage options.
What is Traditional Data?
Traditional Data, on the other hand, refers to data that is structured, organized, and can be processed using conventional data processing tools such as spreadsheets or relational databases. Traditional data is typically generated from limited sources and is relatively smaller in volume compared to Big Data.
Examples of traditional data include customer information, sales data, and financial records. This type of data is generally easy to analyze and understand and is widely used in various business applications such as reporting, analytics, and decision-making.
However, traditional data processing tools may not be able to handle the massive volume, variety, and velocity of data that is being generated today, leading to the emergence of big data technologies and tools.
What is Big Data?
Big data” does not simply refer to the size of the data, but also encompasses the use and analysis of the data. Big data involves processing and analyzing large and complex data sets that cannot be managed with traditional data processing tools. The ability to process and analyze these large data sets enables organizations to gain insights and make informed decisions in real time quickly and efficiently.
Big Data refers to a vast amount of data that is too large and complex to be processed by traditional data processing tools and methods. This data is typically generated from various sources, including sensors, social media platforms, and business applications, among others.
Big Data is typically characterized by the four Vs: Volume, Variety, Velocity, and Veracity
Big data technologies such as Hadoop, Apache Spark, and NoSQL databases have emerged to handle the massive scale and complexity of big data. These tools enable organizations to store, process, and analyze data faster and more efficiently than traditional methods, ultimately leading to improved decision-making, better customer insights, and increased operational efficiency.
The major differences between traditional data and big data are:
1. Size: Traditional data sets tend to be smaller in size and easily manageable, whereas big data sets are characterized by their sheer volume, velocity, and variety.
2. Structure: Traditional data sets are typically structured and follow a predefined format, making them easy to store, process, and analyze. On the other hand, big data sets are often unstructured or semi-structured, which makes it challenging to extract meaningful insights.
3. Processing: Traditional data sets can be analyzed using traditional data processing methods, such as relational databases and SQL queries. Big data sets, however, require more sophisticated and scalable processing techniques such as distributed computing, parallel processing, and machine learning algorithms.
4. Purpose: Traditional data sets are typically used for specific business needs and are often generated by internal systems. Big data, on the other hand, can be generated from a variety of sources such as social media, mobile devices, and sensors, and can be used for a wider range of business insights.
At Vhigna, we have experience in developing and implementing big data solutions for various industries, such as healthcare, finance, and retail. Additionally, we have competency working in data warehousing, data mining, and business intelligence, as well as working with big data frameworks like Hadoop and Spark.