What is Data Pipeline Architecture?

What is Data Pipeline Architecture?

In today’s fast-paced and highly competitive economy, your data is one of the most valuable assets in your organization. Also, most of the time, the data is big. However, it will be next to impossible for you to extract this value from the data you collect without a robust and secure data pipeline architecture.

Data pipeline architectures help you take the raw data coming from your sources and turn it into insights that can drive business decisions. A truly effective pipeline reduces the workload on your analytics team by removing the noise from the data so that you and your team can focus on what matters to your business.

Practically, a data pipeline comprises a variety of tools, platforms, and hardware that generate data at the source, process that data, and then move it to its destination. These days, one of the most important capabilities of a modern data pipeline is that it can process data in real-time.

What are the Challenges of Big Data, and How Does Data Pipeline Architecture Solve Them?

Oftentimes, when we hear the word Big data, we think of large quantities, but big data is much more than just a large amount of data; It is enormous in volume, velocity, and variety. This means big data is extensive, with millions of terabytes rapidly generated daily in different formats. It is also complex, takes too long to process, and is significantly challenging to manage with traditional methods. It also requires a lot of computing power and crazy storage capacity.

The challenge of big data is deeply rooted in storage, processing, security, data quality, validation, scaling, data source, and gaining real-time insights. From the storage and processing perspective, we are referring to that size and how transforming this data in unified formats can be challenging. Since this data is valuable, security should be of the utmost concern to every organization. The good thing is that a good data pipeline architecture can address these challenges by providing a more structured and systematic way to the whole process.

How does Data Pipeline Architecture Solve These Big Data Challenges?

For starters, the data pipeline architectures are designed to support big data ingestion from diverse sources and formats. Many tools like Apache Kafka support this. Once you get the data, you can design your pipeline to deal with this big data processing in parallel in batch or real-time by leveraging distributed computing frameworks. To ensure data quality, you can implement data validation and error monitoring checks within the pipelines. Security measures like access control and encryption are also implemented to protect sensitive data and ensure compliance.

Once the data is processed, you can use scalable, cost-effective data storage solutions like MongoDB, designed for big data as your destination. One thing about data pipelines is that regardless of the increasing data volumes, you can scale horizontally depending on the tool to add more nodes to the cluster to handle the load without compromising performance. The best part is that all this workflow can be monitored using tools like Acceldata and automated using CI/CD pipeline and orchestration tools.

How can you Build a Data Pipeline?

Since talking about the codes or going hand all is outside the scope of this article, we will be looking at this section from an overview perspective.

One way many organizations accomplish this is by building a data pipeline using Kafka and Spark. These two data pipeline tools are frequently used in conjunction with each other to help move and analyze data. Kafka can be considered your data conveyor belt, which takes your data in and moves it where you need it. The Kafka architecture supports real-time data streaming, making it an excellent choice for data pipelines. Additionally, Kafka streams your data to any number of targets simultaneously. This means you can send the data straight to your data lake and a program for end-users. Spark is another tool that often forms the other half of that equation. Spark provides the power to process these data streams to be cleaned and converted into valuable insights.

Another set of tools organizations use together is Snowflake and Databricks, especially when they want a unified environment for big data processing and machine learning. Databrick can seamlessly handle big data and leverage the cloud. This tool also supports machine learning workflows; deploying this model isn't an issue. Snowflake comes in for data processing and storage since it can store various data formats, structured or semi-structured.

Does the Tool Matter When Building a Data Pipeline?

Well, yes, cause of your business use case. However, regardless of the tools you need,

• Ensure you know your data objectives—would it need machine learning or real-time analytics functionalities?
• Identify your data source and ingestion strategy(batch or streaming)—this will determine the tool you use.
• Have a data processing plan—Would it be ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform)?
• Identify where you want to store the data—this depends on your data format.
• Automate your workflow.
• Monitor everything.

Acceldata works perfectly fine for data observability and monitoring. With Acceldata (a platform that provides multiple integrations, including both Kafka and Spark), you gain visibility into your data pipeline, giving you opportunities to increase its efficiency and helping you to ensure that your pipeline performance is meeting business requirements.

5 Types of Data Pipeline Architecture Designs

Today, various types of data pipeline architecture designs are available. These types are based on factors like the integration complexity of the data volume and the processing logic required.

Let's explore the five major types of data pipeline architecture designs:

• Batch Architecture is great for extracting and processing data in large batches over a defined interval or trigger. Since everything is done in batches, the data isn't immediately processed and used for real-time analysis. An example will be retail inventory management or the local store sales dashboard.
• Streaming Data Architecture is used for real-time data processing and analytics. Thus, low latency is required as the data gets processed as soon as it is extracted in milliseconds. Think of your live stream and user interaction within your social media applications.
• Lambda Architecture Type is a hybrid of streaming and batch processing. Thus, it often has three layers: one for batch processing, another for real-time processing, and the last layer that merges the output from the previous two layers. This type gives flexibility and a more comprehensive analysis.
• Change Data Capture (CDC) is similar to streaming architecture but isn't as real-time as streaming. It facilitates data integration between two different systems so that you can capture changes. Thus, new ingestion only loads changes to the new system since the last change. A great example will be synchronizing your transactional database with a data warehouse.
• Kappa Architecture is Lambda architecture without the multiple layers. In this architecture, batch and real-time processing occur under a single stream processing engine.

Data Pipeline Architecture Best Practices

Let’s take a step back for a minute. What is data pipeline architecture? Simply put, a data pipeline organizes data to make analysis easier. Raw data from the source is frequently full of white noise data – irrelevant points that can cloud the data's true insights, making analysis a nightmare. A data pipeline works to eliminate this noise and is critical in enabling businesses to use their data to drive decision-making. There are three main data pipeline stages.

1. Sources—where the data is initially captured. Examples of common sources include SAP and Oracle systems.
2. Processing—the data is manipulated based on the business's specific requirements. This could be data transformation, augmentation, filtering, or any other modification
3. Destination—typically a data lake or data warehouse for analysis

When building or evaluating your own data pipeline, knowing the data pipeline architecture best practices is helpful. The data pipeline framework should be both predictable and scalable. This means that it shouldn’t be hard to identify the data source and that your pipeline should rely on technologies that enable you only to use the resources you need at any given time. Furthermore, end-to-end visibility is another best practice for data pipelines. Visibility is a way to ensure consistency throughout the pipeline and provide proactive security. It helps in better data quality management. One example of a data visibility solution for your pipeline is Acceldata. Acceldata provides pipeline monitoring through our Flow solution. By auditing your pipeline with Flow, you can get better visibility into your pipeline and improve its performance.

Data Pipeline Architecture Diagram

Taking a look at a data pipeline architecture diagram can be a great way to gain a deeper understanding of data pipeline architecture itself. Most diagrams will include boxes or symbols representing the various stages the data passes through on its way to its destination. Also, arrows often represent the activity and direction of the data as it flows through the pipeline.

A typical data pipeline diagram shows the data sources that send their data to an ingestion system (like Kafka). The ingestion system sends the data to the proper processing systems before the finalized, processed data is sent to the storage location. Some diagrams may even include an additional final stage of the data pipeline, which is the visualization system. The visualization system is the end-user application that presents the data in a digestible format for business leaders to draw insights. As you can see, big data pipeline architecture is a complicated process consisting of various sources, tools, and systems. Data pipeline tools are designed to serve various functions that make up the data pipeline.

One term that frequently comes up in discussions of data pipelines and tools is ETL and ELT. When it comes to data pipeline design patterns, a distinction should be made between data pipeline vs. ETL and ELT.

The Role of ETL and ELT in Data Pipeline Architecture

As we mentioned, ETL and ELT are synonymous with the processing pipeline stages. In ETL, the data is extracted from your source(s) and transformed before being moved to your destination. For ELT, the data is extracted and moved to the destination before processing and transformation take place.

While both processing offer flexibility and scalability, choosing which to use depends on the process that effectively provides optimized data processing and analytics workflows. Other factors like the tool, data volume, transformation complexity, latency needs, specific project objectives, and your data pipeline tool can also influence your decision.  Thus, understanding ETL and ELT can help get the best outcome out of your processing.

Modern Data Pipeline Architectures

There are a few defining characteristics of the modern data pipeline architecture. One major element is the cloud. Cloud-based data pipelines enable you to automatically scale up or down your usage so that you only rely on the resources you need. Another vital feature is real-time data streaming and analysis. Many data pipeline tools and services have been developed to enable these modern pipeline features. Looking at data pipeline examples can effectively identify the other aspects and features you want to include in your data pipeline framework. At the end of the day, building a strong data pipeline is integral to your ability to use your data to make decisions.

One of the ways you can improve the efficiency and performance of your data pipeline is to utilize a data observability platform, like Acceldata. Data observability can help enterprises to monitor data for health, accuracy, and usefullness.