Stages of Data Processing

 

Without data processing, companies limit their access to the very data that can hone their competitive edge and deliver critical business insights. That's why it's crucial for all companies to understand the necessity of processing all their data, and how to go about it.

What is Data Processing?

Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not negatively affect the end product, or data output.

Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization.

Six stages of data processing:

1. Data collection: collecting data is the first step in data processing. Data is pulled from available sources, including data lakes and data warehouses. It is important that the data sources available are trustworthy and well-built so the data collected (and later used as information) is of the highest possible quality.

2. Data preparation: once the data is collected, it then enters the data preparation stage. Data preparation, often referd to as "pre-processing" is the stage at which raw data is cleaned up and organized for the following stage of data processing. During preparation, raw data, is diligently checked for any errors. The purpose of this step is to eliminate bad data (redundant, incomplete, or incorrect data) and begin to create high-quality data for the best business intelligence.

3. Data input: the clean data is then entered into its destination (perhaps a CRM like Salesforce or a data warehouse like Redshift), and translated into a language that it can understand. Data input is the first stage in which raw data begins to take the form of usable information.

4. Processing: during this stage, the data inputted to the computer in the previous stage is actually processed for interpretation. Processing is done using machine learning, though the process itself may vary slightly depending on the source of data being processed (data lakes, social networks, connected devices etc.) and its intended use (examining advertising patterns, medical diagnosis from connected devices, determining customer needs, etc.)

5. Data output/interpretation: the output/interpretation stage is the stage at which data is finally usable to non-data scientists. It is translated, readable, and often in the form of graphs, videos, images, plain text, etc.). Members of the company or institution can now begin to self-serve the data for their own data analytics projects.

6. Data storage: the final stage of data processing is storage. After all of the data is processed, it is then stored for future use. While some information may be put to use immediately, much of it will server a purpose later on. Plus, properly stored data is a necessity for compliance with data project legislation like GDPR. When data is properly stored, it can be quickly and easily accessed by members of the organization when needed.

Data Processing Cycle

The data processing cycle consists of a series of steps where raw data (input) is fed into a system to produce actionable insights (output). Each step is taken in a specific order, but the entire process is repeated in a cyclic manner. The first data processing cycle's output can be stored and fed as the input for the next cycle.

1. Collection: The collection of raw data is the first step of the data processing cycle. The type of raw data collected has a huge impact on the output produced. Hence, raw data should be gathered from defined and accurate sources so that the subsequent findings are valid and usable. Raw data can include monetary figure, website cookies, profit/loss statements of a company user behaviour, etc.

2. Preparation: data preparation or data cleaning is the process of sorting and filtering the raw data to remove unnecessary and inaccurate data. Raw data is checked for errors, duplication, miscalculations or missing data, and transformed into a suitable form for further analysis and processing. This is done to ensure that only the highest quality data is fed into the processing unit.

The purpose of this step to remove bad data (redundant, incomplete, or incorrect data) so as to being assembling high-quality information so that it can be used in the best possible way for business intelligence.

3. Input: the raw data is converted into machine readable form and fed into the processing unit. This can be in the form of data entry through a keyboard, scanner or any other input source.

4. Data processing: the raw data is subjected to various data processing methods using machine learning and AI algorithms to generate a desirable output. This step may vary slightly from process to process depending on the source of data being processed (data lakes, online databases, connected devices, etc.) and the intended use of the output.

5. Output: the data is finally transmitted and displayed to the user in a readable form like graphs, tables, vector files, audio, video, documents, etc. This output can be stored and further processed in the next data processing cycle.

6. Storage: the last step of the data processing cycle is storage, where data and metadata are stored for further use. This allows for quick access and retrieval of information whenever needed, and also allows it to be used as input in the next data processing cycle directly.

What is Data Processing?

The method of data processing you employ will determine the response time to a query and how reliable the output is. Thus, the method needs to be chosen carefully. For instance, in a situation where availability is crucial, such as a stock exchange portal, transaction processing should be the preferred method.

It is important to note the different between data processing and a data processing system. Data processing is the rules by which data is converted into useful information. A data processing system is an application that is optimized for a certain type of data processing. For instance, a timesharing system is designed to run timesharing processing optimally. It can be used to run batch processing, too. However, it won't scale very well for the job.

In that sense, when we talk about choosing the right data processing type for your needs, we are referring to choosing the right system. The following are the most common types of data processing and their applications.

1. Transaction Processing: deployed in mission-critical situations. These are situations, which, if disrupted, will adversely affect business operations. For example, processing stock exchange transactions, as mentioned earlier. In transaction processing, availability is the most important factor. Availability can be influenced by factors such as:

    1. Hardware: A transaction processing system should have redundant hardware. Hardware redundancy allows for partial failures, since redundant components can be can be automated to take over and keep the system running.

        2. Software: The software of a transaction processing system should be designed to recover quickly from a failure. Typically, transaction processing systems use transaction abstraction to achieve this. Simply put, in case of a failure, uncommitted transactions are aborted. This allows the system to reboot quickly.

2. Distributed Processing: Very often, datasets are too big to fit on one machine. Distributed data processing breaks down these large datasets and stores them across multiple machines or servers. It rests on Hadoop Distributed File System (HDFS). A distributed data processing system has a high fault tolerance. If one server in the network fails, the data processing tasks can be reallocated to other available servers.

Distributed processing can also be immensely cost-saving. Businesses don't need to build expensive mainframe computers anymore and invest in their upkeep and maintenance.

3. Real-time Processing: Real-time processing is similar to transaction processing, in that it is used in situations where output is expected in real-time. However, the two differ in terms of how they handle data loss. Real-time processing computes incoming data as quickly as possible. If it encounters an error in incoming data, it ignores the error and moves to the next chunk of data coming in. GPS-tracking applications are the most common example of real-time data processing.

Contrast this transaction processing. In case of an error, such as a system failure, transaction processing aborts ongoing processing and reinitializes. Real-time processing is preferred over transaction processing in cases where approximate answers suffice.

In the world of data analytics, stream processing is a common application of real-time data processing. First popularized by Apache Storm, stream processing and analyzes data as it comes in. Think data from IoT sensors, or tracking consumer activity in real-time. Google BigQuery and Snowflake are examples of cloud data platforms that employ real-time processing.

4. Batch Processing: As the name suggests, batch processing is when chunks of data, stored over a period of time, are analyzed together, or in batches. Batch processing is required when a large volume of data needs to be analyzed for detailed insights. For example, sales figures of a company over a period of time will typically undergo batch processing. Since there is a large volume of data involved, the system will take time to process it. By processing the data in batches, it saves on computational resources.

Batch processing is preferred over real-time processing when accuracy is more important than speed. Additionally, the efficiency of batch processing is also measured in terms of throughput. Throughput is the amount of data processed per unit time.

5. Multiprocessing: Multiprocessing is the method of data processing where two or more than two processors work on the same dataset. It might sound exactly like distributed processing, but there is a difference. In multiprocessing, different processors reside within the same system. Thus, they are present in the same geographical location. If there is a component failure, it can reduce the speed of the system.

Distributed processing, on the other hand, uses servers that are independent of each other can be present in different geographical locations. Since almost all systems today come with the ability to process data in parallel, almost every data processing system uses multiprocessing.

However, in the context of this article, multiprocessing can be seen as having an on-premise data processing system. Typically, companies that handle very sensitive information might choose on-premise data processing as opposed to distributed processing. For example, pharmaceutical companies or businesses working in the oil and gas extraction industry.

The most obvious downside of this kind of data processing is cost. Building and maintaining in-house servers is very expensive.

Preparing your Data for Data Processing

Before data can be processed and analyzed, it needs to be prepared, so it can be ready by algorithms. Raw data needs to undergo ETL - extract, transform, load- to get to your data warehouse for processing. Integrate.io simplifies the task of preparing your data for analysis. With our cloud platform, you can build ETL data pipelines within minutes. The simple graphical interface does away with the need to write complex code. There is integration support right out of the box for more than 100 popular data warehouses and SaaS applications. And you can use APIs for quick customizations and flexibility.