Big Data vs. Traditional Data Analysis: Key Differences
With every passing day, technology gets developed and manifests into different forms, and people are getting used to the latest technologies. Big data is one such new technology that has come in place of traditional data analysis.
Many say that big data has many advantages over traditional data analysis, which comes in handy in tackling challenges. Big data can store huge amounts of data, and it can analyze these data quickly, which takes time in traditional data analysis.
As more and more businesses are looking for big data analysis, enrolling in an online course on big data will improve your job prospects.
Even though big data has a lot of advantages, knowing the difference between traditional data and big data analysis will be helpful:
What is big data?
Big data is a set of large and complex data which is also used to process this data. Big data has important characteristics such as:
Variety: The big data includes unstructured, semi-structured, and structured data.
Volume: Big data is very big, and it typically has a high volume in nature.
Velocity: Big data can be processed in quick time
What is traditional data?
Traditional data is structured data that has been used for storing and processing for decades, and it still accounts for most of the data in the world. With the help of traditional data analysis, businesses can track and manage sales. Manipulation is also easier in traditional data, and it can be done using data processing software.
The important difference between traditional data and big data analysis
When you join the big data online class, you will get to know in detail about big data analysis and its difference from traditional data analysis. To give you a basic understanding, here are some basic differences between big data and traditional data analysis:
- Flexibility
Traditional database is static in nature, and it is based on fixed schema. It can only work on structured data that can fit easily into databases or tables. Most of the traditional databases are unstructured and have a large variety of unstructured data that needs new methods for storing and processing.
The dynamic schema is used in big data, and it includes both structured and unstructured data. The schema is used only when accessing it as the data is stored in raw format. Data from various sources are used for big data analysis. These data are then used for cleaning, storing, indexing, distributing, searching, transforming, analyzing, accessing, and visualizing.
2. Distributed architecture
Big data uses a distributed architecture, and traditional data uses a centralized database. In big data, the computations are distributed among many computers. This makes big data more scalable than traditional data, with better performance and cost benefits.
The use of open-source software, commodity hardware, and cloud storage makes big data analysis much more economical. Once the data normalization and data quality checks are done, the data is modeled to be stored in a data warehouse.
3. Sources
Traditional data is derived from customer relationship management (CRM), enterprise resource planning (ERP), online transactions and other data. Big data gets its data from a broad range of areas.
The areas where big data gets data from include enterprise and non-enterprise level data, device and sensor data, social media, and other data. These data sources are evolving, dynamic, and growing every day.
The unstructured data also includes video, texts, audio files, and images. For traditional data analysis, this data is not possible by using rows and columns. Big data analysis is required to get the right value as most of the data comes from multiple sources and is unstructured.
Final thoughts
Big data uses various tools and techniques to handle huge amounts of data, which is difficult in traditional data analysis. As big data helps improve business efficiency and profit, many businesses are looking for big data analysts. So, it is the right time to join a hadoop online training and get certified in big data analysis.
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