What Is Data Pipelining: The Process, Types, and Considerations for Building a Pipeline

Our digital world churns out gigs of data daily, information that’s essential for governments to function, for businesses to thrive, and for us to get the correct thing we ordered (including the right color) from our favorite online marketplace.

Not only is there a vast amount of data in existence, but there are also countless processes to apply to it and so many things that can go wrong. That’s why data analysts and data engineers turn to data pipelining.

This article gives you everything you need to know about data pipelining, including what it means, how it’s put together, data pipeline tools, why we need them, and how to design one. We begin with what it is and why we should care.

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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.

What Is a Data Pipeline?

We know what pipelines are, large pipes systems that carry resources from one location to another over long distances. We usually hear about pipelines in the context of oil or natural gas. They’re fast, efficient ways of moving large quantities of material from one point to another.

Data pipelines operate on the same principle; only they deal with information rather than liquids or gasses. Data pipelines are a sequence of data processing steps, many of them accomplished with special software. The pipeline defines how, what, and where the data is collected. Data pipelining automates data extraction, transformation, validation, and combination, then loads it for further analysis and visualization. The entire pipeline provides speed from one end to the other by eliminating errors and neutralizing bottlenecks or latency.

Incidentally, big data pipelines exist as well. Big data is characterized by the five V’s (variety, volume, velocity, veracity, and value). Big data pipelines are scalable pipelines designed to handle one or more big data’s “v” characteristics, even recognizing and processing the data in different formats, such as structure, unstructured, and semi-structured.

All About Data Pipeline Architecture

We define data pipeline architecture as the complete system designed to capture, organize, and dispatch data used for accurate, actionable insights. The architecture exists to provide the best laid-out design to manage all data events, making analysis, reporting, and usage easier.

Data analysts and engineers apply pipeline architecture to allow data to improve business intelligence (BI) and analytics, and targeted functionality. Business intelligence and analytics use data to acquire insight and efficiency in real-time information and trends.

Data-enabled functionality covers crucial subjects such as customer journeys, target customer behavior, robotic process automation, and user experiences.

We break down data pipeline architecture into a series of parts and processes, including:

Sources

This part is where it all begins, where the information comes from. This stage potentially involves different sources, such as application APIs, the cloud, relational databases, NoSQL, and Apache Hadoop.

Joins

Data from different sources are often combined as it travels through the pipeline. Joins list the criteria and logic for how this data comes together.

Extraction

Data analysts may want certain specific data found in larger fields, like an area code in a telephone number contact field. Sometimes, a business needs multiple values assembled or extracted.

Standardization

Say you have some data listed in miles and other data in kilometers. Standardization ensures all data follows the same measurement units and is presented in an acceptable size, font, and color.

Correction

If you have data, then you will have errors. It could be something as simple as a zip code that doesn’t exist or a confusing acronym. The correction phase also removes corrupt records.

Loads

Once the data is cleaned up, it's loaded into the proper analysis system, usually a data warehouse, another relational database, or a Hadoop framework.

Automation

Data pipelines employ the automation process either continuously or on a schedule. The automation process handles error detection, status reports, and monitoring.

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Data Pipeline Tools: An Overview

Data pipelining tools and solutions come in many forms, but they all have the same three requirements:

  • Extract data from multiple relevant data sources
  • Clean, alter, and enrich the data so it can be ready for analysis
  • Load the data to a single source of information, usually a data lake or a data warehouse

Here are the four most popular types of data pipelining tools, including some specific products:

Batch

Batch processing tools are best suited for moving large amounts of data at regularly scheduled intervals, but you don’t require it in real-time. Popular pipeline tools include:

  • Informatica PowerCenter
  • IBM InfoSphere DataStage

Cloud-native

These tools are optimized for working with cloud-based data, like Amazon Web Services (AWS) buckets. Since the cloud also hosts the tools, organizations save on in-house infrastructure costs. Cloud-native data pipelining tools include:

  • Blendo
  • Confluent

Open-source

A classic example of “you get what you pay for,” open source tools are home-grown resources built or customized by your organization’s experienced staff. Open source tools include:

  • Apache Kafka
  • Apache Airflow
  • Talend

Real-time

As the name suggests, these tools are designed to handle data in real-time. These solutions are perfect for processing data from streaming sources such as telemetry data from connected devices (like the Internet of Things) or financial markets. Real-time data pipeline tools include:

  • Confluent
  • Hevo Data
  • StreamSets

Data Pipeline Examples

Here are three specific data pipeline examples, commonly used by technical and non-technical users alike:

B2B Data Exchange Pipeline

Businesses can send and receive complex structured or unstructured documents, including NACHA and EDI documents and SWIFT and HIPAA transactions, from other businesses. Companies use B2B data exchange pipelines to exchange forms such as purchase orders or shipping statuses.

Data Quality Pipeline

Users can run data quality pipelines in batch or streaming mode, depending on the use cases. Data quality pipelines contain functions such as standardizing all new customer names at regular intervals. The act of validating a customer’s address in real-time during a credit application approval would be considered part of a data quality pipeline.

MDM Pipeline

Master data management (MDM) relies on data matching and merging. This pipeline involves collecting and processing data from different sources, ferreting out duplicate records, and merging the results into a single golden record.

Data Pipeline Design and Considerations or How to Build a Data Pipeline

Before you get down to the actual business of building a data pipeline, you must first determine specific factors that will influence your design. Ask yourself:

  • What is the pipeline’s purpose? Why do you need the pipeline, and what do you want it to accomplish? Will it move data once, or will it repeat?
  • What kind of data is involved? How much data do you expect to work with? Is the data structured or unstructured, streaming or stored?
  • How will the data be used? Will the data be used for reporting, analytics, data science, business intelligence, automation, or machine learning?

Once you have a better understanding of the design factors, you can choose between three accepted means of creating data processing pipeline architecture.

Data Preparation Tools

Users rely on traditional data preparation tools such as spreadsheets to better visualize the data and work with it. Unfortunately, this also means the users must manually handle every new dataset or create complex macros. Thankfully, there are enterprise data preparation tools available to change data preparation steps into data pipelines.

Design Tools

You can use tools designed to build data processing pipelines with the virtual equivalent of toy building blocks, assisted by an easy to use interface.

Hand Coding

Users employ data processing frameworks and languages such as Kafka, MapReduce, SQL, and Spark. Or you can use proprietary frameworks like AWS Glue and Databricks Spark. This approach requires users to know how to program.

Finally, you need to choose which data pipelining design pattern works best for your needs and implement it. They include:

Raw Data Load

This simple design moves bulk, unmodified data from one database to another

Extract-Transform-Load

This design extracts data from a data store and transforms (e.g., clean, standardize, integrate) it before loading it into the target database

Extract-Load-Transform

This design is like ETL, but the steps are changed to save time and avoid latency. The data’s transformation occurs in the target database

Data Virtualization

Whereas most pipelines create physical copies of stored data, virtualization delivers the data as views without physically keeping a separate copy

Data Stream Processing

This process streams event data in a continuous flow in chronological sequence. The process parses events, isolating each unique event into a distinct record, allowing future use evaluation

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