Big data vs. tradition data

Big data is a new challenge due to the quantity of data organizations must manage. Big data is vast amounts of data that can be simple or complex. The term big data also includes the process by which that large amount of information is analyzed. Big data sets can include data that is unstructured, semi- structured, or structured. Big data can also be defined by the speed for which the data is received. Big data is delivered quickly and is frequently analyzed in real- time. Big data can also be considered more accurate. However, quality can be affected by the same factors that traditional data can be affected, bias, outliers, and noise. MongoDB, Cassandra, and NoSQL databases can be used to process big data. They are non- relational and raw data is used for processing (Big Data vs. Traditional Data, n.d.).

In contrast, traditional data is often structured data that can be stored in files or tables. Traditional data is often easier to analyze and still provides valuable insights. Traditional data is still the largest portion of data. While the processing of data is easier, traditional data may offer simpler conclusions than big data sets. SQL, MySQL, and Oracle DB can be used to process traditional data. Traditional data is the preferred type and process for confidential data because it is small enough to be stored and analyzed locally (Big Data vs. Traditional Data, n.d.).  

Processing big data requires a different approach than processing traditional data. Parallel processing can be used to analyze big data, and it is a process of data analysis on multiple servers at the same time. The scalability is also a factor. Scalability refers dynamics and capabilities of programs to analyze data. Big data processing programs also have a factor of fault tolerance. Fault tolerance is the ability for program to continue to process data even after the processing of a group of data fails. This ensures accuracy and availability. Big data processing programs will also be able to process data in real-time. Those programs will also be able to process data from multiple sources. Rosidi provides a list of some of the big data tools. Those include Apache Tez, Apache Spark Apache Storm, Apache Flink, IBM Streams, Apache Kafka, Amazon Kinesis, Google Cloud Dataflow, Apache Hadoop MapReduce (Rosidi, 2023).

References

Big Data vs. Traditional Data | Pure Storage. (n.d.). Www.purestorage.com. https://www.purestorage.com/knowledge/big-data/big-data-vs-traditional-data.html

Rosidi, N. (2023, September 11). Working with Big Data: Tools and Techniques. KDnuggets. https://www.kdnuggets.com/working-with-big-data-tools-and-techniques