Customer Data Pipeline and Data Processing: Types, Importance, and Benefits
Why use an Automated Pipeline?
A data pipeline is an automated process, but why do we need it in the first place?
Data pipelines take the role of arranging all data volumes in the same format and one place, thus reproducible.
Key Elements of a Data Pipeline
A data pipeline represents a data preparation process based on a few key elements - source, destination, dataflow, processing, workflow, and monitoring.
Data pipelines enable application or data lake information to travel to a data warehouse or an analytics database, respectively.
A data source is a place from where the pipeline retrieves information. These can include cloud-based customer tools (Salesforce, Facebook Ads, Google Ads, etc.), relational management systems, social media database management tools, and even sensor devices.
You can enter the data directly in the visual data analysis tools, but this is the data pipeline’s endpoint, where it removes all data pulled.
The raw data can be changed while traveling from its source to its destination- a move also referred to as data flow.
Data processing is a method that enables you to evaluate how data is gathered, transformed, and stored.
As an important step, data processing in data pipelines chooses whereby you should implement the dataflow. The processing of data includes the extraction of business data from all available sources. Once processed, this data undergoes an inspection and is adjusted to the business user before it is loaded into the data store.
Types of Data Processing
So, can you make a difference between a data processing system and data processing? Yes, and here's how it works:
Data processing regards data transformed into beneficial information, whereas a data processing system is an optimized tool for proper data management.
Some of the basic types of data processing include:
The processing of the transaction is deployed in mission-critical situations. If violated, these conditions will negatively affect your business.
The system for processing transactions should have redundant hardware. Excess hardware allows partial defects because you can automate the unnecessary components to take the system and maintain it operationally.
The software system for processing transactions shall be designed to recover quickly from the defect. Usually, systems for processing transactions use abstraction transactions to achieve this. If there's a failure, uncommitted transactions are interrupted, allowing the system to restart quickly.
Distributed data processing breaks down these big stores, while data sets across many machines or servers. Data within a distributed processing system has a huge tolerance for slips. When one or two network servers crash in a distributed processing system, the system redistributes the data processing to other remaining servers.
Real-time processing takes the same approach seen in transaction processing.
If an error is detected in the input, it ignores it and instead switches to the next piece of incoming data. The most popular application of real-time data processing is GPS tracking apps.
As the name suggests, serial processing happens when pieces of data stored at a certain time are analyzed either together or in batches. Serial processing is required when you need to analyze huge volumes of data for a detailed inspection. As the system handles mass volumes of data, the processing period might take longer to complete. The favored real-time data processing method is serial processing, given that accuracy of information is more significant than the processing speed.
Multiprocessing is a method in which two or more processors work on the same database. The most obvious drawback of this data processing type is the cost. Keep in mind that building and maintaining internal servers is very expensive.
Workflow usually refers to the sequencing of jobs within the pipeline data, as well as their co-dependence. Here, dependencies and sequencing decide when a data pipeline is operating.
The final element is monitoring, meaning the pipeline is continually monitored for efficiency and speed to evaluate data accuracy and loss.
Data Pipeline and ETL
ETL stands for Extract, Transform and Load, and is a method applied with batch loads regarding particular pipeline data.
Essentially, this process refers to source data transferring, like an app target, such as a data warehouse.
- Data Extraction using various source systems enables an easy acquiring of relevant data from a specific source.
- Data Transformation applies to the processes of filtering, aggregating, and preparing data for further analysis.
- Data Loading represents the loading of data into its final destination.