Managing the flow of information from a source to the destination system, such as data warehouse, forms an integral part of every enterprise looking to generate value from their data. It is an intricate task as several things can go wrong during the transfer — data source may create duplicates, errors can propagate from source to destination, data can get corrupted, etc.
An increase in the number of sources and data volume can further complicate the process. This is where data pipelines enter the scene. They simplify the flow of data by eliminating the manual steps and automating the process.
In this blog, we’ll cover what a data pipeline architecture is and why do you need one. Next, we’ll see the basic parts and processes involved in a data pipeline. Lastly, we’ll explain two examples of data pipeline architecture.
What is a Data Pipeline Architecture?
A data pipeline architecture is an arrangement of objects that extracts, regulates, and routes data to the relevant system for obtaining valuable insights.
Unlike an ETL pipeline that involves extracting data from a source, transforming it, and then loading into a target system, a data pipeline is a rather wider terminology. It embraces the ETL pipeline as a subset.
The key difference between ETL and data pipeline is that the latter uses processing tools to move data from one system to another, whether the data is transformed or not.
Why Do You Need a Data Pipeline?
Data pipelines increase the targeted functionality of data by making it usable for obtaining insights into functional areas, such as target customer behavior, process automation, buyer journeys, and customer experiences.
As a data pipeline carries data in portions intended for certain organizational needs, you can improve your business intelligence and analytics by getting insights into instantaneous trends and info.
Another key reason that makes a data pipeline essential for enterprises is that it consolidates data from numerous sources for comprehensive analysis, reduces the effort put in analysis, and delivers only the required information to the team or project.
Moreover, data pipelines can improve data security by constraining access to information. They can allow in-house or peripheral teams to only access the data that’s essential for their objectives.
Data pipelines also improve vulnerabilities in the numerous stages of data capture and movement. To copy or move data from one system to another, you have to move it between storage depositories, reformat it for every system, and/or integrate it with other data sources. A well-designed data pipeline architecture unifies these small pieces to create an integrated system that delivers value.
Basic Parts and Processes of a Data Pipeline Architecture
The data pipeline architecture can be classified into the following parts:
Data Source
You can access data from diverse sources, such as relational DBMS, APIs, Hadoop, NoSQL, cloud sources, etc. After data retrieval, you must observe security protocols and follow best practices for ideal performance and consistency.
Extraction
Some fields might have distinct elements like a zip code in an address field or a collection of numerous values, such as business categories. If these discrete values need to be extracted or certain elements of a given field need to be masked, data extraction comes into play.
Joins
As part of a data pipeline architecture, it’s common for data to be joined from diverse sources. Joins specify the logic and criteria for the way data is pooled.
Standardization
Often, data might require standardization on a field by field basis. This is done in terms of units of measure, dates, elements, like color or size, and codes relevant to industry standards.
Correction
Datasets often contain errors, such as invalid fields like a state abbreviation or zip code that no longer exists. Similarly, data may also include corrupt records that must be erased or modified in a different process. This step corrects the data before is it loaded into the destination system.
Data Loading
After your data is corrected and ready to be loaded, it is moved into a unified system from where it is used for analysis or reporting. The target system is usually a relational DBMS or a data warehouse. Every target system requires following best practices for good performance and consistency.
Automation
Data pipelines are usually implemented several times, and usually on a schedule or uninterruptedly. This needs automation to reduce errors and the status must be conveyed to monitoring procedures.
Monitoring
Just like any other system, individual steps involved in data pipeline architecture design should also be comprehensively scrutinized. Without monitoring, you can’t correctly determine if the system is performing as expected. For instance, you can measure the time when a specific job was initiated and stopped, total runtime, completion status, and any relevant error messages.
Examples of Data Pipelines Architecture
Data pipelines may be architectured in the following ways:
Batch-Based Data Pipeline
Batch processing involves handling data chunks that have already been stored over a certain time period. For instance, handling all the transactions that have been executed by a key financial company in a month.
Batch processing is more suitable for situations where large data volumes need processing and real-time analytics aren’t required. Acquiring exhaustive insights, in this pipeline, is more important than getting faster analytics results.
In a batch-based data pipeline, there might be a source application, like a point-of-sale (POS) system, which creates a large number of data points that you have to transfer to a data warehouse and an analytics database.
Here’s an example of how such a system would look like:
Streaming Data Pipeline
Stream processing performs operations on data in motion or real-time. It enables you to swiftly sense conditions within a smaller time period from the point of getting the data. As a result, you can enter data into the analytics tool the moment it is created and obtain prompt results.
This type of data pipeline processes the data from the POS system as it is being produced. The stream processing engine sends outputs from the data pipeline to data repositories, marketing apps, CRMs, and several other applications, besides sending them back to the POS system itself.
Wrap Up
Raw datasets include data points that may or may not be important for your business. A data pipeline integrates and manages this information to simplify reporting, analytics, and BI by means of a custom-made mishmash of software technologies and protocols.
There are plenty of options available when it comes to building data pipeline architecture that simplifies data integration. One such powerful tool is Astera Centerprise 8.0 that helps you extract, clean, transform, integrate, and manage your data pipelines without writing a single line of code.
Originally published at https://www.astera.com on February 22, 2020.
