In today’s business landscape, making smarter decisions faster is a critical competitive advantage. But harnessing timely insights from your company’s data can seem like a headache-inducing challenge. The volume of data — and data sources — is growing every day: on-premise solutions, SaaS applications, databases, and other external data sources. How do you bring the data from all of these disparate sources together? The answer is “Data pipelines”.

What is a Data Pipeline?

A data pipeline is a sequence of actions that moves data from a source to a destination. A pipeline may involve filtering, cleaning, aggregating, enriching, and even analyzing data in motion.

Data pipelines move and unify data from an ever-increasing number of disparate sources and formats so that it’s suitable for analytics and business intelligence. In addition, data pipelines give team members exactly the data they need, without requiring access to sensitive production systems.

Data pipeline architectures describe how data pipelines are set up to enable the collection, flow, and delivery of data. Data can be moved via either batch processing or stream processing. In batch processing, batches of data are moved from sources to targets on a one-time or regularly scheduled basis. Batch processing is the tried-and-true legacy approach to moving data, but it doesn’t allow for real-time analysis and insights.

In contrast, stream processing enables the real-time movement of data. Stream processing continuously collects data from sources like change streams from a database or events from messaging systems and sensors. Stream processing enables real-time business intelligence and decision-making.

data pipeline

How does it work?

A data pipeline automates the processing of moving data from one source system to another downstream application or system. The data pipeline development process starts by defining what, where and how data is collected. It captures source system characteristics such as data formats, data structures, data schemas, and data definitions.

The data pipeline then automates the processes of extracting, transforming, combining, validating, and loading data. The data is then used for further operational reporting, business analysis, data science advanced analytics, and data visualizations.

Data processing activities of a pipeline can either be processed as a sequential, time-series flow or divided into smaller processing chunks that can take advantage of parallel processing capabilities.

Some benefits 

Automation
Data pipelines can be broken down into repeatable steps that pave the way for automation. Automating each step minimizes the likelihood of human bottlenecks between stages, allowing you to process data at a greater speed.

Efficiency
Automated data pipelines can transfer and transform reams of data in a short space of time. What’s more, is that they can also process many parallel data streams simultaneously. As part of the automation process, any redundant data will be extracted to ensure that your apps and analytics are running optimally.

Flexibility
The data that you gather may likely come from a range of different sources which will contain distinguishable features and formats. A data pipeline will allow you to work with different forms of data irrespective of their unique characteristics.

Analytics
Data pipelines will aggregate and organize your data in a way that’s optimized for analytics, providing you with fast immediate access to reliable insights.

Value
Data pipelines enable you to extract additional value from your data by utilizing other tools such as machine learning. By leveraging these tools, you’ll be able to carry out a deeper analysis of your insights that can reveal hidden opportunities, potential pitfalls, and ways you can enhance operational processes.

data pipeline

Why do we need Data Pipelines?

Data-driven enterprises need to have data efficiently moved from one location to another and turned into actionable information as quickly as possible. Unfortunately, there are many obstacles to clean data flow, such as bottlenecks (which result in latency), data corruption, or multiple data sources producing conflicting or redundant information.

Data pipelines take all the manual steps needed to solve those problems and turn the process into a smooth, automated workflow. Although not every business or organization needs data pipelining, the process is most useful for any company that:

  • Create, depend on, or store vast amounts of data, or data from many sources
  • Depend on overly complicated or real-time data analysis
  • Employ the cloud for data storage
  • Maintain siloed data sources

Furthermore, data pipelines improve security by restricting access to authorized teams only. The bottom line is the more a company depends on data, the more it needs a data pipeline, one of the most critical business analytics tools.

Stages within a Data Pipeline

As with any pipeline, the data pipeline moves through several key stages. Each stage is integral to achieving the desired result; that is, gaining as much value from your data as possible.

The five key stages are:

  • Data source. This is the data created within a source system, which includes applications or platforms. Within a data pipeline, there are multiple source systems. Each source system has a data source in the form of a database or data stream.
  • Data integration and ingestion. Data integration is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process; it includes steps such as cleansing, ETL mapping, and transformation. Data is extracted from the sources, then consolidated into a single, cohesive data set.
  • Data storage. This stage represents the “place” where the cohesive data set lives. Data lakes and data warehouses are two common solutions to store big data, but they are not equivalent technologies:

+ A data lake is typically used to store raw data, the purpose for which is not yet defined.

+ A data warehouse is used to store data that has already been structured and filtered for a specific use. (A good way to remember the difference is to think of the data lake as a place where all the rivers and streams pour into without being filtered.)

  • Analysis and Computation. This is where analytics, data science, and machine learning happen. Tools that support data analysis and computation pull raw data from the data lake or data warehouse. New models and insights (from both structured data and streams) are then stored in the data warehouse.
  • Delivery. Finally, insights from the data are shared with the business. The insights are delivered through dashboards, emails, SMSs, push notifications, and microservices. The machine learning model inferences are exposed as microservices.

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Use cases of Data Pipelines

As big data continues to grow, data management becomes an ever-increasing priority. While data pipelines serve various functions, the following are three broad applications of them within the business:

  • Exploratory data analysis: Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.
  • Data visualizations: Data visualizations represent data via common graphics, such as charts, plots, infographics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.
  • Machine learning: Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.

Conclusion

The data pipeline is complex; there’s no way to remove its intricacies, or else it would be rendered significantly less effective. However, orchestrating and automating the flow of data through a data pipeline is an entirely obtainable objective.