Data-driven decision-making has become the heart of today's world, organizations generate information at an unprecedented pace from daily sales transactions to real time sensor reading in IoThttps://www.oracle.com/africa/internet-of-things/ devices.
But if this data are not collected,mined and processed properly then it has no value. This depicts the importance of data processing.
In this article, i will explain the types if data processing,use cases and differences.
It begins.
There are two ways in which data are processed:
- The Batch Processing.
- The Stream Processing.
What is Batch Data Processing?
Batch processing is when computer handle high
-volumes and repetitive tasks by grouping data into batches and processing it.
Batch processing is mainly automated, with minimal human interaction. tasks are predefined, and the system executes them according to a scheduled timeline.
There are a variety of ETL tools for batch processing. A common tool is Apache Airflow, which allows users to quickly build up data orchestration pipelines that can run on a set schedule and have simple monitoring.
Note: Data orchestration is the automated process of managing and coordinating the flow of data across various systems, applications, and platforms.
What is Stream Data Processing?
Also called real-time data processing, I personally believe that its alias is self explanatory, regardless let me delineate it.
Stream processing continuously ingests and analyzes data. Instead of waiting for data to accumulate, you can process it instantly. This is crucial for time-critical analysis.
One popular framework is Apache Kafka. Apache Kafka is a distributed, fault-tolerant, scalable, and high-throughput messaging system.
Stream Processing Use Cases
Stream processing is particularly beneficial in several key areas. Here are 4 prime examples:
Fraud detection: Stream processing allows financial institutions to monitor transactions in real time. This helps identify and flag suspicious activities immediately, which helps in preventing fraud effectively.
Network Monitoring: In network management, stream processing enables you to constantly monitor your network traffic. This real-time analysis helps in quickly detecting and addressing any anomalies or issues, ensuring smooth network operations.
Predictive Maintenance: Industries use stream processing to monitor equipment health in real time. As a result, potential issues can be detected and addressed before they lead to equipment failure, which saves costs and improves efficiency.
Intrusion Detection: In cybersecurity, stream processing helps in real-time detection of unauthorized access or activities within a network. The detection allows for swift action to mitigate potential security threats.
Batch Processing Use Cases
You should use batch processing in scenarios where data processing must be scheduled and does not require immediate results. The 3 best examples include:
End-of-day reporting: Financial institutions often use batch processing for end-of-day reports. Transactions and activities are accumulated throughout the day and processed in one go, generating comprehensive reports for analysis.
Data warehousing: Organizations use batch processing to update data warehouses periodically. Large volumes of data are collected and processed in batches, ensuring that the data warehouse is up-to-date with the latest information for analytical purposes.
Payroll processing: Companies process payroll data in batches, typically on a bi-weekly or monthly basis. This involves collecting timekeeping data, calculating salaries, and generating paychecks, all done in bulk to streamline operations.
The Difference between Batch and Stream Processing.
Data Size and Scope: Batch processing is best for handling large volume of data. it can process all the data while Stream processing is ideal for low volumes of data. It handles a process real time data eg: Mpesa Transactions.
Performance: Looking at Data Latency, the time taken from when data is collected and made available for processing, Stream processing takes a few milliseconds while batch processing takes hours, days, weeks, months, years.
Required Hardware: Sequential data handling in batch processing requires more resources and storage system to handle the large data ingestion. Continuous data handling in stream processing requires less resources data is processed real-time with no need for storage for later processing.
Note: Data ingestion is the process of collecting, importing, and loading data from various sources into a system for storage and analysis
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