A data pipeline refers to the process of ingesting raw data from various sources, filtering it, and finally moving it to a destination for storing or analyzing. The process includes a series of steps that incorporate three main elements: a source, processing steps, and a destination. For example, you may move data from an application to a data lake or data warehouse, or from data storage to an analytics tool. 

Data pipelines are becoming more meaningful in environments where microservices are sought after. Microservices are applications with small codebases, and they move data between various applications to make the output more efficient. One data pipeline example could be a social media post that could potentially fuel a social listening report, a sentiment analysis, and a marketing app that counts brand mentions. Even though the data source is singular, it can feed different pipelines and bring out helpful insights. 

Data Pipeline Process

Data pipeline processes vary from business case to business case. It may be a straightforward process of data extraction and loading or it could be a complex data flow in which data is processed for the purpose of training machine learning models. The process consists of the following elements:

Source

Data sources comprise relational databases and SaaS applications whereby the data is pulled from multiple sources via an API call, webhook, or a push mechanism. Depending on the business case, data can be moved in real-time or at certain intervals that are relevant to the concrete goal. Businesses can choose between various platforms like AWS S3, Google Bucket, and Azure blob storage delivered by the cloud service provider of their preference.

Processing 

When it comes to processing, there are two main data pipeline architectures: batch processing, which involves gathering data at certain intervals and wiring it to the destination, and stream processing, which sends data as soon as it is generated. There is a third data pipeline example that combines both aforementioned models. Lambda is a pipeline component that facilitates real-time processing while providing a platform to store and access historical batch data. This way, developers can create additional pipelines to correct errors in previous pipelines or wire them to new destinations if a new business case emerges. 

Transformation

Data transformation brings datasets to a format that is required for a certain business goal. This step involves standardizing datasets, sorting them out, removing duplicates, and validating and verifying information. Another vital aspect of data transformation is normalization and denormalization where the former refers to removing data redundancy and the latter combines data into a single source. The purpose of this step is to pave the way to further analyze datasets in the most efficient way.

Destination

Destination varies depending on the logic of the data pipeline. In some cases, it may land in a data lake, data warehouse, or data mart while in others it can go to analytics tools. For example, platforms like Google Big Query, AWS Glue, Lambda, AWS EMR, Azure Bus transfer, etc. are commonly used for analytic purposes.

Monitoring

Monitoring is an essential part of a well-functioning data pipeline as it adds quality assurance to the process. Administrators must be notified when something goes wrong that can jeopardize data integrity. For example, a source can go offline and stop fueling the pipeline with new information. The most prominent monitoring tools are Looker and Tableau. 

Main Features of Data Pipelines

An efficient data pipeline needs to encompass a list of features that make the team more productive and the results more viable. 

Cloud-based Architecture 

Cloud-based solutions simplify and optimize Big Data ETL setup and management and allow companies to focus on business goals rather than technical tasks. Cloud providers carry the responsibility for the rollout and support of services and architectures and maintain their efficiency. 

Continuous Data Processing 

Market trends change rapidly, and the need for real-time data processing is becoming more critical. Modern data pipelines collect, transform, process, and move data in real-time, which allows companies to have access to the most relevant and recent information. On the other side of the spectrum is batch processing, which delivers data with a few hours or even days of delay. This can be hazardous to the business as trends change incredibly quickly. Real-time data processing offers a competitive edge and helps businesses make data-driven decisions—especially businesses that rely on real-time data, such as logistics firms who are required to adopt this type of processing power. 

Fault-tolerant Architecture

Fault-tolerant architecture refers to an automatic backup plan that turns on another node in case the original one fails. This feature protects businesses from unexpected failures and substantial monetary losses. These types of pipelines offer more reliability and availability and guarantee stable and smooth processing. 

Big Data Processing 

The amount of data generated on a daily basis is enormous and unimaginable, and 80% of this data is unstructured or semi-structured. An efficient data pipeline needs to have the capacity to process large data volumes of unstructured data, such as sensor or weather data, as well as semi-structured data such as JSON, XML and HTML. On top of that, a pipeline has to be able to transform, clean, filter, and aggregate datasets in real-time, which requires significant amounts of processing power.

DataOps and Automation

DataOps is a set of methodologies and principles that helps developers shorten the development cycle. It does so by automating the processes across the entire lifecycle. In the long run, such pipelines are easier to scale up and down, run tests with, and modify in the pre-deployment phase. 

Data Democratization 

Data democratization opens up access to the data across the company and removes gatekeeping that hinders the development of the business. Instead of only a few executives having access to important information, everyone that works at the company can analyze the data and use it for their decision-making. 

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