A Guide to Data Pipeline Architecture

One of the most critical areas where enterprises must pay special attention is data management. To be able to generate value from their data, enterprises go to great lengths to ensure that its quality is not compromised.

Often, data duplication and corruption, and other issues such as having a wide variety of different data formats make the entire process tedious and cumbersome. The most common way to address these issues is to automate the data pipelines.

But first, we need to understand what a data pipeline architecture is. So, let us get right to it.

Data Pipeline Architecture

As the name suggests, data pipelines allow for the transport of data from one or multiple sources to a target destination for business intelligence (BI) and reporting initiatives. It is important to note that data pipeline is a wider concept, therefore terms like ETL pipeline or big data pipeline can be thought of its subsets.

Recently, greater emphasis is being laid on speeding up the processing of data through data pipelines. There are three main factors that affect the speed with which data can move through a data pipeline, and these are discussed underneath.

Factors Affecting the Speed of Data Pipelines

  • Throughput – Throughput can be defined as the rate at which a pipeline can process data within a specified amount of time.
  • Reliability – This pertains to the quality of the data. A data pipeline will be deemed reliable if it has built-in validation mechanisms, auditing, and logging.
  • Latency – Latency can be understood with the concept of response time. It is the time it takes for a single unit of data to move through the entire data pipeline. Ideally, the lower the latency the better.

However, enterprises must strike a balance to get maximum value since lower latency can lead to significantly higher costs.

Designing A Data Pipeline

A data pipeline architecture is layered, this means that each part of the system feeds data into the next. Starting from data sources, data moves through the ingestion components, undergoes transformation, and then finally reaches the destination.

Data Sources: One of the best ways to understand data sources is to think of it in terms of plumbing. That is, data sources are wells, streams, lakes, etc. where enterprises gather data.

Therefore, data sources constitute the first layer of a data pipeline, and it is critical that data quality in these sources is not compromised. Some of the most common data sources include relational DBMS, cloud sources, Hadoop, NoSQL, etc.

Ingestion: The next layer is the ingestion layer that reads and extracts the required data from these sources. To assess the quality of the required data, a process called data profiling is used.

Data profiling examines the data for its structure and characteristics and evaluates how well it is going to serve the business intelligence initiatives. Data can be ingested either in batches or via streaming.

As the name suggests, batch processing includes extracting and operating sets of records as a group. Batch processing is sequential, which means that groups of records are ingested, processed, and output according to rules or criteria set by the users beforehand.

Furthermore, batch processing either runs on a trigger or on a schedule and does not support real-time processing.

As an alternative, streaming allows for data sources to automatically pass individual records one by one. Most organizations use batch ingestion while streaming is used mostly when near real-time data is needed for analytics and reporting.

Transformation: Now that the data is extracted, is it in the structure or format that the organization needs it in? If not, it will need to go through transformation processes. These processes include filtering, aggregation, using joins, as well as mapping coded values to descriptive ones.

Depending on the data replication process an organization decides to use, transformation can take place either before or after loading into a data repository, such as a data lake or a data warehouse.

ETL, an on-premises technology, transforms data before loading it into a warehouse. ELT, on the other hand, first loads the data into a warehouse before it can be transformed.

Destinations: Once the data is transformed, it is now ready to be moved to a destination. The most used destination nowadays is a data warehouse. These databases hold all the enterprise’s standardized data in a centralized location accessible by different departments for analytics, reporting, and business intelligence initiatives.

Data that is not well-structured is usually stored in data lakes. Moreover, enterprises can also directly load data into BI tools.

This marks the end of the data pipeline. As mentioned before, without automation, most of these processes will need to be handled manually which requires a lot of resources. Therefore, enterprises looking to improve overall operational efficiency should look for solutions that automate the entire data pipeline.

Astera Centerprise- The Right Way to Build a Data Pipeline

Astera Centerprise automates the data pipeline process with its ETL and built-in transformation capabilities. Using an automated solution cuts down processing time and increases operational efficiencies.