The 3 main types of data processing and analysis techniques
Posted: Tue Jan 21, 2025 10:13 am
Find out which types of data processing and analysis techniques are best for your business: batch, streaming or real-time.
The choice of data processing and analysis techniques will have a decisive influence on the result. Power and scalability are characteristics that must be taken into account in the same way as the system's ability to collect outliers, detect fraud in transactions or carry out security controls . The most difficult task, however, is to reduce the latency of the analysis performed on a complete set of big data, something that requires processing terabytes of data in a matter of seconds.
Requirements regarding response time, the conditions of the data to be analyzed or the workload are the issues that will ultimately determine the best choice in terms of data processing and analysis techniques .
From Bit... to Big Data: The term Big Data has rich people database very popular, but what is Big Data really?
Batch processing: for batches of large volumes of data
Apache Hadoop is a distributed computing framework modeled after Google MapReduce for processing large amounts of data in parallel . Hadoop Distributed File System (HDFS) is the underlying file system for a Hadoop cluster and works more efficiently with a small number of large big data files than with a larger number of smaller data files.
A job in the Hadoop world typically takes minutes to hours to complete, so it could be argued that Hadoop is not the most suitable option when the business needs to perform real-time analysis, but rather in cases where offline analytics can be achieved.
Recently, Hadoop has evolved to adapt to new business needs. Businesses today demand:
Lower latencies.
Minimizing response time .
Maximum precision in decision making.
Hadoop has been revamped by improving its management capacity thanks to a new feature known as stream. One of the main objectives of Hadoop streaming is to decouple Hadoop MapReduce from the paradigm to accommodate other parallel computing models, such as MPI (Message Passing Interface) and Spark. With the new features of the application of data processing and analysis techniques in streaming, many of the limitations of the batch model are overcome . Although it can be considered too rigid for certain functions, which is not surprising if one takes into account that its origins date back more than four decades, it is still the most suitable, due to the cost-results ratio, for operations such as:
The calculation of the market value of assets, which does not need to be reviewed more than once a day.
The choice of data processing and analysis techniques will have a decisive influence on the result. Power and scalability are characteristics that must be taken into account in the same way as the system's ability to collect outliers, detect fraud in transactions or carry out security controls . The most difficult task, however, is to reduce the latency of the analysis performed on a complete set of big data, something that requires processing terabytes of data in a matter of seconds.
Requirements regarding response time, the conditions of the data to be analyzed or the workload are the issues that will ultimately determine the best choice in terms of data processing and analysis techniques .
From Bit... to Big Data: The term Big Data has rich people database very popular, but what is Big Data really?
Batch processing: for batches of large volumes of data
Apache Hadoop is a distributed computing framework modeled after Google MapReduce for processing large amounts of data in parallel . Hadoop Distributed File System (HDFS) is the underlying file system for a Hadoop cluster and works more efficiently with a small number of large big data files than with a larger number of smaller data files.
A job in the Hadoop world typically takes minutes to hours to complete, so it could be argued that Hadoop is not the most suitable option when the business needs to perform real-time analysis, but rather in cases where offline analytics can be achieved.
Recently, Hadoop has evolved to adapt to new business needs. Businesses today demand:
Lower latencies.
Minimizing response time .
Maximum precision in decision making.
Hadoop has been revamped by improving its management capacity thanks to a new feature known as stream. One of the main objectives of Hadoop streaming is to decouple Hadoop MapReduce from the paradigm to accommodate other parallel computing models, such as MPI (Message Passing Interface) and Spark. With the new features of the application of data processing and analysis techniques in streaming, many of the limitations of the batch model are overcome . Although it can be considered too rigid for certain functions, which is not surprising if one takes into account that its origins date back more than four decades, it is still the most suitable, due to the cost-results ratio, for operations such as:
The calculation of the market value of assets, which does not need to be reviewed more than once a day.