In today’s fast-paced and highly competitive economy, your data is one of the most valuable assets in your organization. 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 is made up of a variety of tools, platforms, and hardware that generate data at the source, a 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. One way many organizations accomplish this is by building a data pipeline using Kafka and Spark. Kafka and Spark are two data pipeline tools that are frequently used in conjunction with each other to help move and analyze data.
Kafka can be thought of as your data conveyor belt, taking your data in and moving it where you need it to go. 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 that you can send the data straight to your data lake and to 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 so that they can be cleaned and converted into useful insights. A third tool that is very beneficial to data pipelines is Acceldata. With Acceldata (a platform that provides integrations with 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.
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 true insights the data possesses, 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.
The sources are where the data is initially captured. Examples of common sources include SAP and Oracle systems. In the processing stage, the data is manipulated based on the specific requirements of the business. This could be data transformation, augmentation, filtering, or any other modification. Finally, the data is sent to its destination. The destination is typically a data lake or data warehouse for analysis. These three stages are all essential elements in most data pipeline design patterns.
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 source of the data and that your pipeline should rely on technologies that enable you to only 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.
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 that represent the various stages that the data passes through on its way to its destination. Also, there are often arrows representing 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 then 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 that business leaders can use to draw insights from. 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. ETL is short for extraction, transformation, and loading. When it comes to data pipeline design patterns, a distinction should be made between the data pipeline vs. ETL. ETL is a term that refers to subprocesses that can occur within the data pipeline depending on the needs and desires of the business. Data pipeline, by contrast, is a broader term referring to the entire journey your data make from source to destination.
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 are only relying 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 be an effective way to 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.