Managing the flow of information from a source to the destination system, such as a data warehouse, forms an integral part of every enterprise looking to generate value from their raw data. Data pipeline architecture 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 amount of data and number of sources can further complicate the process. This is where data pipelines enter the scene. Data pipeline technologies simplifies the flow of data by eliminating the manual steps of extract, transform, and load and automates the process
In this blog, we’ll cover what data pipeline architecture and why it needs to be planned before an integration project. Next, we’ll see the basic parts and processes of a data pipeline. Lastly, we’ll explain two examples of data pipeline architecture and talk about one of the best data pipeline management tools.
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 or big data pipeline that involves extracting data from a source, transforming it, and then loading it into a target system, a data pipeline is a rather wider terminology. It embraces the ETL and big data pipelines as a subset.
The key difference between ETL and data pipeline is that the latter uses data processing tools to move data from one system to another, whether the data is transformed or not.
Factors Contributing to the Efficiency of a Data Pipeline
Three main factors should be considered when building a data pipeline:
- Throughput: It is the rate at which the data in a pipeline process within a specified time.
- Reliability: It requires the various systems in the data pipeline to be tolerant of faults. Therefore, a reliable pipeline has built-in auditing, validation, and logging systems that ensure data quality.
- Latency: It refers to the time required for one unit of data to pass through the data pipeline. It is essentially about response time than throughput.
Why Do You Need a Data Pipeline?
With huge volumes of data flowing inwards every day, it is beneficial to have a streaming data pipeline architecture allowing all the data to be handled in real-time, as a result boosting analytics and reporting. Data pipelines increase the targeted functionality of data by making it usable for obtaining insights into functional areas.
For example, a data ingestion pipeline transports information from different sources to a centralized data warehouse or database. This can help analyze data concerning 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, secure data quality pipelines can help administrators constrain access to information. They can allow in-house or peripheral teams only to 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 streaming 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 design can be classified into the following parts:
Components of the data ingestion pipeline architecture help retrieve data from diverse sources, such as relational DBMS, APIs, Hadoop, NoSQL, cloud sources, open sources, data lakes, data stores, etc. After data retrieval, you must observe security protocols and follow best practices for ideal performance and consistency.
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 field elements need to be masked, data extraction comes into play.
As part of a data pipeline architecture design, it’s common for data to be joined from diverse sources. Joins specify the logic and criteria for the way data is pooled.
Often, data might require standardization on a field-by-field basis. This is done in terms of units of measure, dates, elements, color or size, and codes relevant to industry standards.
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 in the data pipeline architecture corrects the data before it is loaded into the destination system.
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.
Data pipelines in big data are usually implemented several times and usually on a schedule or uninterruptedly. Scheduling of different processes needs automation to reduce errors, and it must convey the status to monitoring procedures.
Like any other system, individual steps involved in data pipeline development should also be comprehensively scrutinized. Without monitoring, you can’t correctly determine if the system is performing as expected. For instance, you can measure when a specific job was initiated and stopped, total runtime, completion status, and any relevant error messages.
Examples of Data Pipelines Architecture
The two most important big data pipeline examples are:
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 a key financial company has executed in a month.
Batch processing is more suitable for large data volumes that need processing, while they don’t require real-time analytics. Acquiring exhaustive insights in batch based-data pipelines are 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.
The diagram below shows how a batch-based data pipeline system works:
Streaming Data Pipeline
Stream processing performs operations on data in motion or in real-time. It enables you to swiftly sense conditions within a smaller time period from getting the data. As a result, you can enter data into the analytics tool the moment it is created and obtain prompt results.
The streaming 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.
Here’s an example of how a streaming data pipeline system works:
Raw datasets include data points that may or may not be important for your business. A data pipeline architecture uses different software technologies and protocols to integrate and manage critical business information to simplify reporting and analytics.
There are plenty of options available when it comes to building data pipeline architecture that simplifies data integration. One of the best data pipeline automation tools is Astera Centerprise 8.0 that helps you extract, clean, transform, integrate, and manage your data pipelines without writing a single line of code.