Until this abundant data is left as it is, it is of no use. With some valuable operations, like data collecting and processing, this data qualifies as valuable input for business insights. This article will guide you to make use of the data effectively using Data Aggregation techniques.
Data aggregation is the process of summarizing and condensing widely collected data into a simpler form, making it easy for data scientists to develop critical insight from it. Based on when and on what the aggregation takes place, people categorize the aggregation service in two ways:
Time aggregation collects multiple data points of one resource over a while. For example: Consider you run a shopping complex, where you collect sales data on one shopping complex at the end of the day. Here, the aggregation takes place on one resource (the shopping complex) at a regular interval (end of the day).
Spatial aggregation collects data from multiple resource groups at regular intervals. Here, data collection depends on more than one factor. For example: Consider, you own a shopping complex. You perform spatial aggregation to view the sales data of all the stores at regular intervals. Here, they work on multiple resource groups like individual stores of a complex.
There are a few concepts that address how often and under what conditions the data is aggregated or collected.
Reporting period denotes the time period over which the data is gathered. The data of a particular device or circumstance are collected over a period of time for presentation purposes. For example, let us consider a toll booth that records the vehicle details that cross their way each day. Here, one day is the reporting period.
Granularity is slightly different from the reporting period. In this case, the data are collected over a period of time for the aggregation process. Granularity helps in performing aggregation operations over the collected data. Example: A toll booth records the vehicles that pass their way. If the data are collected every 10 minutes, the granularity is 10 minutes and the granularity range may vary from 1 minute, 2 minutes, and 10 minutes to 1 month.
The polling period is an extended process of granularity. As granularity is the time period over which the data is collected. While the polling period is the time taken for data creation. Assume if the toll system takes 10 minutes to generate data of the vehicles crossing by. Then 10 minutes is the polling period. And if we prefer collecting data every 5 minutes the granularity is 5 minutes.
Data aggregation is all about unifying data from multiple sources. Though it sounds simple, data aggregation involves multiple processing cycles in the proper order of execution.
The primary step of data aggregation is data collection. The collection phase extracts data from multiple sources. The sources are not necessarily always static, they may be dynamic, too. The data warehouse and historical data records are a few of the static data sources. They don’t change. But, there may be dynamic sources, like social media, as well. Social media communications are the most interactive data sources, where data may keep on changing with every passing minute.
Collecting data is the primary phase, so data aggregation tools proceed with the process in this processing phase. This phase is responsible for converting raw data into a format that is suitable for the data analysis process. Data processing includes multiple operations, like cleaning the unnecessary noises from the data, performing logical or arithmetic operations, like MIN, MAX, AND, SUM, and other complex data transferring operations.
The last step of data aggregation is presentation. Data aggregators typically visualize the results in a marketing dashboard that displays the business insights of their success and failure rates. In this presentation phase, the data aggregation tools display the factors that positively impacted the business as charts or tables. This comparison of multiple trial and error methods can finally help users predict a design pattern from successful trials and build a business intelligence report.
Proxy servers act as intermediate servers between the nodes of communication in the network. The proxy server acts on behalf of the client and hides the identity of the client from the server and the network. This anonymity helps users access geo-blocked sites and prevents IP bans. These special features of the proxies ease the data aggregation process by automating the data extraction with high speed. The data aggregation process can make use of multiple proxies from rotating proxy pools.
The manual data aggregation takes quite a long time and requires a lot of effort. Manual data aggregators can find it tedious to have to repeat the collection, processing, and presentation phase for as much data as they have. This is why people prefer automated data aggregation software or data aggregation tools that can speed up the aggregation process. Choosing the right data aggregation system can enhance the quality and standards of the process. Here are some of the factors to consider before deciding on a data aggregation system.
Residential proxies may be the proper choice for the data aggregation process. As their proxy address is associated with a physical system, they appear like a real address. This reduces the suspicions on the IP addresses. Also, with the residential pools, people can find proxies of various locations and protocols to access specific sites.
A proxy is not the primary component of the data aggregation process. Data scientists have many automated data aggregation tools that can aggregate the data collected and present aggregate data. But, a proxy can add value to this system. Though a proxy is not the major requirement of data aggregation, efficient data aggregation requires a proxy as it simplifies the scrapping process through its features.
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Both are similar in that they collect data from various sources, but the integration focuses more on presenting the aggregate data in a summarized format.
Data scientists utilize this data aggregation technique to handle atomic data records. If you are expecting to collect data from various sources and convert them into valuable insights, make use of this data aggregation technique. To simplify the data aggregation process, consider factors like cost, compatibility, scalability, and other factors to choose a suitable data aggregation software. Also, configuring a suitable proxy type can improve the efficiency of the data aggregation process.