Home / Blogs / Data Pipeline Architecture: All You Need to Know

Table of Content
The Automated, No-Code Data Stack

Learn how Astera Data Stack can simplify and streamline your enterprise’s data management.

Data Pipeline Architecture: All You Need to Know

January 17th, 2024

The pinnacle of success for any organization today lies in how fast it can process data, which is why the need for highly scalable data pipelines is becoming increasingly critical to the operations of a business. Data pipelines are the modern solution to automate, and scale repetitive data ingestion, transformation, and integration activities. A proper data pipeline architecture can significantly accelerate the availability of high-quality data to downstream systems and applications. 

In this blog, we’ll cover what is data pipeline architecture and why it needs to be planned before a data integration project. Next, we’ll see the basic parts and processes of a data pipeline. We will also explore different data pipeline architectures and talk about one of the best data management tools in the market. 

What is Data Pipeline Architecture?

All You Need to Know About Data Pipeline Architecture

To put it simply, a data pipeline takes data from the source, processes and it moves it into the destination, just like any other pipeline in the physical world. You can think of data pipeline architecture as this organized, interconnected system that takes raw data, refines it, stores it, analyzes it, and then shares the valuable results – all in a systematic and automated way.  The objective is to provide downstream applications and end users with a consistent flow of clean and consistent data. 

Unlike an ETL 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 and ETL (Extract, Transform, Load) just a subset. The key difference between ETL and data pipeline architecture is that the latter uses data processing tools to move data from one system to another, whether the data is transformed or not. 

Components of Data Pipeline Architecture

Now that you have an idea of the data pipeline architecture, let’s look at the blueprint and tackle each component of a data pipeline: 

Data Sources: Your data pipeline starts from data sources, where your data is generated. Your data sources can be databases storing customer information, log files capturing system events, or external APIs providing real-time data streams. These sources generate the raw material for your data journey. The type of source determines the method of data ingestion. 

Data Ingestion: Next is the data Ingestion component that collects and imports data from source systems into the data pipeline. It can be done through batch processing or real-time streaming, depending on the requirements. Data ingestion can be done in two main ways – batch processing, where data is collected at scheduled intervals, or real-time streaming, where data flows continuously as it’s generated.  

Data ingestion will also depend on the type of data you are dealing with. For example, if you have primarily unstructured data such as PDFs, you will need a specialized data extraction software such as Astera Report Miner.  

Data Processing: This is one of the most important stages in the architecture as it makes data fit for consumption. Raw data may be incomplete or 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. Data cleaning involves standardizing data, removing duplicates, and filling in the null values. 

The processing stage also involves data transformation. Depending on your data, you might have to implement various transformations such as normalization, denormalization, sort, tree join, etc. The aim of transformation is to convert data into a suitable format for analysis. 

Data Storage: Next, the processed data is stored in databases or data warehouses. A company may store data for different purposes such as historical analysis, redundancy or just making it accessible to users in a central place. Depending on the purpose, data can be stored in different places such as relational data bases. For example, PostgreSQL, MySQL, or Oracle are suitable for structured data with well-defined schemas. 

NoSQL databases, such as MongoDB, Cassandra are designed for flexibility and scalability and are well-suited for handling unstructured or semi-structured data and can scale horizontally to manage large volumes. 

Data is also stored in data warehouses. However, often data warehouses are paired with cloud storage platforms such as Google Cloud, Amazon S3 and Microsoft Blob storage that store high volume data.  

Data Analysis: The data analysis component is where the raw power of data comes to life. It involves querying, processing, and deriving meaningful insights from the stored data. Data analysts and scientists use different tools and technologies to conduct analysis such as descriptive, predictive and statistical analysis to uncover insights patterns and trends in data.  

Most used languages and techniques for data analysis include SQL, which is best suited for relational databases. Other than that, users also often use python or R programming.  

Data Visualization: A data pipeline ends at data visualization, where data is turned into tables and pie charts, so it is easier for data analysts to understand it. Visualization tools such as PowerBI and Tableau provide an intuitive interface for exploring raw data. Analysts and data scientists can interactively navigate through datasets, identify patterns, and gain a preliminary understanding of the information. 

Read More: 10 Best Data Pipeline Tools 

Types of Data Pipeline and Their Architecture

No data pipeline architecture is the same since there is no one way of processing data. Depending on the variety and number of data sources, data might have to be transformed multiple times before it reaches its destination. 

Batch Processing Architecture 

data pipeline architecture 2

Batch data pipeline is a data processing technique where data is collected, processed, and then the results are obtained in intervals. It is typically used for large volumes of data that can be processed without the need for immediate results. 

Usually, data is divided into batches or chunks. This division is done to manage the processing of large datasets in a more manageable way. Each batch represents a subset of the overall data and is processed independently. The processing can involve various operations, such as filtering, aggregation, statistical analysis, etc. The results of each batch processing step are typically stored in a persistent storage system. 

Use Cases: Suitable for scenarios where real-time processing is not critical, such as daily or hourly data updates. For example, there is an e-commerce company that wants to analyze its sales data to gain insights into customer behavior, product performance, and overall business trends allows the e-commerce company to perform in-depth analysis of its sales data without the need for real-time results. 


  • Data Source: Where raw data originates. 
  • Batch Processing Engine: Processes data in predefined intervals. 
  • Storage: Holds processed data. 
  • Scheduler: Triggers batch processing at specified times. 

Streaming Data Pipeline 

data pipeline architecture 4

Stream processing performs operations on data in motion or in real-time. It enables you to swiftly sense conditions within a smaller 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. 

Use Cases: Ideal for applications requiring low-latency data processing. For example, in the financial industry, detecting fraudulent transactions is crucial for preventing financial losses and ensuring the security of users’ accounts. Traditional batch processing systems may not be sufficient for identifying fraudulent activities quickly. A streaming data pipeline, on the other hand, can provide real-time analysis of transactions as they occur from ATMs, credit cards etc. 

Components of the Data Pipeline 

  • Data Source: Generates continuous streams of data. 
  • Stream Processing Engine: Processes data in real-time. 
  • Storage: Optionally stores processed data for historical analysis. 


The Lambda architecture is a data processing architecture designed to handle both batch and stream processing of data. It was introduced by Nathan Marz to address the challenges of big data processing where low-latency requirements for real-time analytics coexist with the need for processing large volumes of data in batch mode. The Lambda Architecture achieves this by combining batch processing and stream processing into a single, scalable, and fault-tolerant system. 

Here are the key components and layers of the Lambda Architecture: 

Batch Layer: 

  • Function: Handles the processing of large volumes of historical data in a fault-tolerant and scalable manner. 
  • Data Storage: Typically uses a distributed file system like Apache Hadoop Distributed File System (HDFS) or cloud-based storage systems. 
  • Processing Model: Batch processing involves running computations on a complete dataset, producing results that are typically stored in a batch view or batch layer serving layer. 

Speed Layer: 

  • Function: Deals with the real-time processing of data streams, providing low-latency results for recent data. 
  • Data Storage: Usually relies on a distributed and fault-tolerant storage system that supports rapid writes and reads for real-time processing. 
  • Processing Model: Stream processing involves analyzing data in real-time as it arrives, providing up-to-the-moment results. 

Serving Layer: 

  • Function: Merges the results from the batch and speed layers and provides a unified view of the data. 
  • Data Storage: Utilizes a NoSQL database or a distributed database that can handle both batch and real-time data. 
  • Processing Model: Serves precomputed batch views and real-time views to the querying application. 

Query Layer: 

  • Function: Enables users to query and access the data in the serving layer. 
  • Data Storage: Query results are retrieved from the serving layer. 
  • Processing Model: Allows ad-hoc queries and exploration of both batch and real-time views. 

ETL Pipeline 

There is a difference between an ETL pipeline and a data pipeline. An ETL pipeline is a form of a data pipeline that is used to extract data from various sources, transform it into a desired format, and load it into a target database or data warehouse for analysis, reporting, or business intelligence purposes. The primary purpose of an ETL pipeline is to facilitate the movement of data from diverse sources to a central repository where it can be efficiently analyzed and used for decision-making. 

ELT Pipeline 

An ELT (Extract, Load, Transform) pipeline is an alternative to the traditional ETL approach. While the basic goal of both ETL and ELT is to move and prepare da. ta for analysis, they differ in the order in which the transformation step occurs. In an ETL pipeline, transformation is done before loading data into the target system, whereas in an ELT pipeline, transformation is performed after the data is loaded into the target system. 

ELT pipelines often leverage the processing power of modern data warehouses, which are designed to handle large-scale data transformations. 


An on-premises data pipeline refers to a set of processes and tools that organizations use to collect, process, transform, and analyze data within their own physical infrastructure or data centers, as opposed to relying on cloud-based solutions. This approach is often chosen for reasons such as data security, compliance requirements, or the need for more direct control over the infrastructure.  

On-premises architectures rely on servers and hardware physically located within an organization’s own data center or facility. Organizations have complete control over hardware, software, and network configurations. They are responsible for purchasing, maintaining, and upgrading all components. However, scaling the infrastructure often involves significant capital investment, and expansion may take time. 

Cloud Native 

A cloud-native data pipeline architecture is designed to leverage the benefits of cloud computing, offering scalability, flexibility, and cost-effectiveness. It typically involves a combination of managed services, microservices, and containerization 

A cloud-native data pipeline architecture is designed to be dynamic, scalable, and responsive to changing data processing needs. It optimizes resource utilization, enhances flexibility, and often results in more cost-effective and efficient data processing workflows compared to traditional on-premises solutions. 

It utilizes the serverless functions and services to reduce operational overhead and scale resources based on demand. 

How to Increase Data Pipeline Speed

Regardless of the data architecture you opt for, at the end of the day it all comes down to how fast and efficient your data pipeline is. Well, there are certain metrics that you can measure to o evaluate the speed of a data pipeline. These metrics provide insights into different aspects of the pipeline’s processing capabilities:

1. Throughput

It measures the rate at which data is successfully processed by the pipeline over a specific period. 

Throughput (records per second or bytes per second) = Total processed records or data size / Time taken for processing

2. Latency

Latency is the time it takes for a piece of data to travel through the entire pipeline from source to destination. 

Latency = End-to-end processing time for a data record 

3. Processing Time

It measures the time taken to transform or manipulate the data within the pipeline. 

Processing time = Time taken for transformation or processing of a data record 

4. Resource Utilization

Resource utilization measures how efficiently the pipeline uses computing resources (CPU, memory, etc.) during data processing. 

Resource utilization = (Actual resource usage / Maximum available resources) * 100 

Key Design Considerations

When setting a data pipeline architecture, it is important to keep in mind certain factors and best practices to create a data pipeline architecture that is robust, scalable, and easy to manage. Here is how you can design your data pipeline:  

Modularity: Modular design promotes code reuse, simplifies maintenance, and allows for easy updates to individual components. Break down the pipeline into smaller, independent modules or services. Each module should have a well-defined responsibility, and communication between modules should be standardized. 

Fault Tolerance: Building fault tolerance into the pipeline ensures the system can recover gracefully from errors and continue processing data. Implement retry mechanisms for transient failures, use checkpoints to track progress, and set up monitoring and alerting for abnormal conditions. 

Orchestration: Orchestration tools help schedule and manage the flow of data through the pipeline, ensuring tasks are executed in the correct order with proper dependencies. You can use tools like Astera Centerprise to define workflows that represent the logical sequence of pipeline activities. 

Parallel Processing: Parallel processing enables the pipeline to handle large datasets more efficiently by distributing workloads across multiple resources. Astera Centerprise supports a high-powered parallel processing ETL/ELT engine that you can utilize for your data pipelines. 

Data Partitioning: Make sure to choose efficient data partitioning as it improves parallelism and overall performance by distributing data processing tasks across multiple nodes. Common techniques include range partitioning, hash partitioning, or list partitioning. 

Scalability: Always keep in mind scalability. Design the pipeline to scale horizontally (adding more instances) or vertically (increasing resources per instance). Leverage cloud-based services for automatic scaling based on demand. 

Version Control: Use version control systems like Git for pipeline code and configuration files. Follow best practices for branching, merging, and documenting changes. 

Testing: Implement unit tests for individual components, integration tests for workflows, and end-to-end tests for the entire pipeline. Include tests for data quality and edge cases. Rigorous testing will ensure that the pipeline performs reliably and always meets business requirements 

Continuously Improve Data Pipeline Architecture

Defining data pipeline architecture is not a one time process; you need to keep identifying areas for improvement, implementing changes, and adapting the architecture to evolving business needs and technological advancements. The goal is to ensure that the data pipeline remains robust, scalable, and able to meet the organization’s changing requirements. Here is what you can do:  

  1. Regularly monitor the performance and health of the data pipeline. Use monitoring tools to gather metrics related to resource usage, data processing times, error rates, and other relevant indicators. Analyze the collected data to identify bottlenecks, areas of inefficiency, or potential points of failure. 
  2. Establish feedback loops that allow users, data engineers, and other stakeholders to provide insights and feedback on the pipeline’s performance and functionality. 
  3. Define and regularly review KPIs for the data pipeline. Key metrics may include throughput, latency, error rates, and resource utilization. Use KPIs to assess the effectiveness of the data pipeline and to guide improvement efforts. 
  4. Implement incremental enhancements rather than attempting major overhauls. Small, targeted improvements are easier to manage and can be continuously integrated into the existing pipeline. Prioritize improvements based on their impact on performance, reliability, and user satisfaction. 

Astera Centerprise-The Code-Free Automated Data Pipeline Tool

Astera Centerprise is a completely zero code data pipeline tool that comes with a visual and intuitive user interface. The tool has been designed keeping in mind accessibility of business users so they can also build data pipelines without relying too much on the IT department. Want to start self-maintaining high volume data pipelines? Try it for free for 14 days.  

All You Need to Know About Data Aggregation
Data Governance in the Insurance Industry
Data Governance in the Education Industry
Considering Astera For Your Data Management Needs?

Establish code-free connectivity with your enterprise applications, databases, and cloud applications to integrate all your data.

Let’s Connect Now!