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Data Pipeline Architecture: A Complete Guide of Building a Data Pipeline | Saras Analytics
Data Engineering

Data Pipeline Architecture: How to Build a Data Pipeline?

11 minutes read


Table of Contents

Managing the movement of information from a source to a destination system, such as a data warehouse, is a vital aspect of any business that seeks to get value from raw data. The architecture of a data pipeline is a complex undertaking since various things might go wrong during the transfer of data, such as the data source creating duplicates, mistakes propagating from source to destination, data corruption, etc.

A rise in the quantity of data and the number of sources might further complicate the procedure. At this point, data pipelines enter the picture. Data pipeline automation streamlines the flow of data by automating the human procedures of extracting, transforming, and loading.

This blog post will discuss the data pipeline architecture and why it must be prepared prior to an integration project. Next, we’ll examine the fundamental components and operations of a data pipeline. We will conclude by describing two instances of data pipeline design and one of the top data pipeline technologies.

What is Data Pipeline Architecture?

A data pipeline architecture is a collection of items that captures, processes, and transmits data to the appropriate system in order to get important insights.

A data pipeline is a broader phrase than ETL pipeline or large data pipeline, which entail obtaining data from a source, changing it, and then feeding it into a destination system. It includes as a subset the ETL and large data pipelines. The primary distinction between ETL and data pipeline is that the latter employs processing tools to transfer data from one system to another, regardless of whether the data has been converted.

Why is a Data Pipeline so Crucial?

Massive volumes of data are generated by businesses, and for that data to provide value to the business, it must be examined. Traditional data architectures rely heavily on data pipelines to prepare data for analysis. A data pipeline may transport data, such as business spending records, from a source system to a landing zone on a data lake. The data then through many processing processes en route to a data warehouse, where it may be analyzed.

Businesses that rely on data warehouses for analytics for BI reporting must employ several data pipelines to transport data from source systems through many phases before delivering it to end users for analysis. Without data pipelines to transport data to data warehouses, these organizations cannot maximize the value of their data.

Because a no-copy warehouse design decreases data migration, organizations that have chosen a data warehouse can reduce the number of data pipelines they must construct and operate.

Data Pipeline Architecture

An effective data pipeline necessitates specialized infrastructure; it consists of many components that facilitate the processing of massive datasets.

Listed below are key architectural components of the data pipeline:

  • Relational databases and SaaS (software-as-a-service) technologies may serve as data sources. Generally, data is synced in real-time at predetermined intervals. Even when data is retrieved at regular intervals, raw data from numerous sources can be ingested utilizing an API request or push method.
  • Transformation is an operation that modifies data as necessary. Transformation of data may involve standardization, deduplication, reformatting, validation, and cleansing. When data travels from source to destination, the ultimate objective is to change the dataset in order to feed it into centralized storage. To further convert data and construct pipelines for training and testing AI agents, you may also extract data from centralized sources such as data warehouses.
  • Processing is the data pipeline component that determines the implementation of data flow. Methods for data ingestion collect and import data into a data processing system. There are two data intake models: batch processing for periodic data collection and stream processing for immediate data sourcing, manipulation, and loading.
  • Workflow entails the sequencing of jobs inside the data pipeline and the management of their dependencies. Technical or business-oriented workflow dependencies determine when a data pipeline operates.
  • Monitoring is a component that verifies the integrity of the data. The data pipeline must be continuously monitored for data loss and correctness. As the volume of data increases, pipelines must be equipped with devices that notify managers of speed and efficiency.

What Distinguishes a Data Pipeline from an ETL (Extract, Transform, and Load) Pipeline?

Data pipeline refers to the methods required to transfer data from one system to another. It encompasses all types of data mobility, including batch processing and real-time processing from cloud-native sources or inexpensive open sources. It does not, however, need data modification or loading.

Alternately, ETL refers to the transformation and loading of data into a data warehouse. In addition, ETL pipelines are always batch-based, whereas data pipelines might be continuous, real-time, or hybrid.

ETL pipelines are typically used for data migration, in-depth analytics, and business intelligence, where data is taken from several sources and transformed to make it easily accessible to end users in a centralized location. A data pipeline is better appropriate for real-time applications. The selection of one over the other is depending on a company’s requirements, as each offer distinct features and benefits.

Building Data Pipelines

Although there is a great deal of standardization in this field, data pipelines must be meticulously constructed to address the difficulties of data volume, diversity, and velocity, while also satisfying the requirements for high precision, low latency, and no data loss. When developing data pipelines, it is necessary to make several choices.

Best data pipelining tools include:

Batch Best choice for moving large chunks of data at regular intervals.
Cloud-native Works best for cloud-based data.
Open source Can be easily customized as per the needs of the user.
Real time Can handle data in real time.

Should data pipelines be constructed locally or on the cloud?

Data pipelines are either constructed on-premises by an organization, which employs an in-house pipeline for analyzing data, understanding user preferences, and mapping consumers. However, this can be difficult as developers would need to build new code for each source to be merged, as the sources may utilize various technologies. In addition, handling huge volumes, maintaining low latency, enabling high velocity, and assuring scalability might become difficult and expensive with in-house data pipelines.  Other firms utilize cloud-native warehouses, such as Amazon Redshift, Google BigQuery, Azure SQL Data Warehouse, and Snowflake, which offer quick and simple scaling benefits. Cloud-based data pipelines are economical, quick, and equipped with monitoring tools to handle faults and abnormalities. In addition, cloud data pipelines offer real-time analytics that rapidly delivers business insights.

Another option is to utilize open-source software to construct a cost-effective data pipeline. Nonetheless, these open-source technologies are accessible to everyone and can be modified in any way. This needs a high level of technical expertise from the user or developer.

Elements of a Data Pipeline

Data Sources

The initial component of the contemporary data pipeline is the origin of the data. Your data source might be any system that generates data that your organization utilizes, including:

  • Analytics data (user behavior data)
  • Transactional data
  • Third-party data (data that your organization does not directly acquire but uses)

Data Collection And Intake

Next in the data pipeline is the ingestion layer, which is responsible for introducing data into the pipeline. This layer uses data ingestion technologies such as Striim to connect to internal and external data sources via a number of protocols. This layer may also send batch (data at rest) and streaming (data in motion) data to big data storage destinations.

Data Processing

Through data validation, cleaning, normalization, transformation, and enrichment, the processing layer is responsible for translating data into a consumable form. Depending on the company’s ETL (Extract Transform Load) vs. ELT (Extract Load Transform) architecture, the data pipeline can perform this processing component either before or after data is placed in the data storage.

In an ETL-based processing architecture, the data is extracted, converted, and then loaded into the data stores; this is most commonly employed when the data storage is a data warehouse. In ELT-based systems, data is initially put into data lakes and subsequently converted into a consumable form for a variety of business use cases.

Data Storage

This component is responsible for supplying the data pipeline with durable, scalable, and secure storage. It often comprises of huge data repositories such as data lakes and data warehouses (for structured or semi-structured data ).

Data Consumption

The consumption layer provides and combines scalable and effective solutions for data storage consumption. In addition, the data consumption layer delivers insights for all business users via purpose-built analytics tools that enable analytical approaches like SQL, batch analytics, reporting dashboards, and machine learning.

Data Governance

The security and governance layer protects the data in the storage layer and the processing resources of the other levels. This layer is comprised of systems for access control, encryption, network security, use monitoring, and auditing. The security layer also monitors the actions of all other levels and generates a comprehensive audit trail. Moreover, the other components of the data pipeline are natively integrated with the security and governance layer.

Examples of Data Pipeline Architecture

The two most important examples of data pipelines are:

Streaming Data Pipeline

Batch processing includes manipulating data chunks that have been previously stored over a given period of time. For instance, managing the month’s worth of transactions conducted by a major financial institution.

Large data volumes that require processing are better suited to batch processing because they do not require real-time analyses. In batch-based data pipelines, acquiring thorough insights is more crucial than achieving quicker analytical outcomes.

A source application in a batch-based data pipeline may be a point-of-sale (POS) system that generates a significant number of data points that must be sent to a data warehouse and an analytics database.

Stream Processing

Stream processing involves the execution of operations on data in motion or in real-time. It allows you to rapidly detect circumstances within a shorter amount of time after receiving data. Consequently, you are able to feed data into the analytics tool as soon as it is produced and acquire immediate results.

The streaming data pipeline handles the data in real-time. In addition to delivering them back to the POS system, the stream processing engine delivers outputs from the data pipeline to data repositories, marketing apps, CRMs, and various other applications.

Challenges of Data Pipelines

Data pipelines are comparable to plumbing infrastructure in the physical world. Both are essential conduits for meeting fundamental requirements (to move data and water respectively). Both can break and require maintenance.

In several firms, a team of data engineers will construct and manage data pipelines. As far as feasible, data pipelines should be automated to decrease the amount of manual oversight necessary. However, even with automation, businesses may encounter the following data pipeline issues:


There may be hundreds of data pipelines in enterprises. At this scale, it might be challenging to comprehend which pipelines are in use, how up-to-date they are, and which dashboards or reports depend on them. In a data world with several data pipelines, everything from regulatory compliance to cloud migration might become more complicated.


Creating new pipelines at scale may be expensive. Changes in technology, transfer to the cloud, and requests for additional data for analysis might all necessitate that data engineering and developers create new pipelines. Over time, maintaining several data pipelines can potentially increase operational expenses.

Slow Efficiency

Depending on how data is replicated and transferred within an organization, data pipelines may result in sluggish query speed. When there are several concurrent requests or large data volumes, pipelines can become sluggish, especially in situations that rely on multiple data copies or employ a data virtualization solution.

Data Pipelines Provide Deeper Insights

Data pipelines are a crucial element of a contemporary data strategy. They link enterprise-wide data to the stakeholders who require it. The efficient mobility of data facilitates the discovery of trends and the discovery of fresh insights that help both strategic planning and day-to-day decision making.

There are several process design models and numerous pipeline construction tools. The most crucial step is recognizing the value of the data your firm holds and beginning to identify new methods to harness it to advance the business. Nowadays, the SaaS platforms help companies by removing the trouble of writing their own ETL code and building data pipelines from scratch. Daton is a simple data pipeline that can populate popular data warehouses like Snowflake, Bigquery, Amazon Redshift for fast and easy analytics using 100+ data sources. The best part is that you can use Daton is easy to set up without the need for any coding experience.

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