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We live in a digital era where data rules. The business landscape has improved drastically thanks to insightful data that helps optimize business processes, better understand customers, make better decisions, and monitor performance.

But with so many sources of data in differing formats, stored on-premises, infrastructure or on cloud-based servers, it can all become overwhelming.

That's where data pipelines come in.

Data pipelines help centralize all this data so that it can ultimately be used to enhance business processes, improve the customer experience, and enable important analytics.

Think of a pipe that collects dirty water at one end, filters it, and produces clean and drinkable water at the other end. A data pipeline works off a similar concept, raw data goes in that isn't ready for consumption, undergoes some transformation, and is then available to use at the other end.

Unfortunately, too many businesses get it wrong when it comes to data pipelines. But fear not, we've put together this handy guide to make sure you get it right.

Perhaps you need to measure the performance of your shiny new QR code generator software? Or maybe you need to streamline customer data in your new PBX system? Whatever it is your business needs, keep reading for the data pipeline meaning: the ultimate guide for beginners.

What exactly is a data pipeline?

Okay, so we know pipelines help centralize data from multiple sources to make use of it. But what exactly is a data pipeline? You'll need to understand the data pipeline meaning before you can use it to benefit your business.

In simple terms, a data pipeline is an end-to-end sequence of digital processes that allows you to collect, modify, and transport data. Businesses use data pipelines to copy or move data from one source to another where it can then be stored, analyzed, or combined with other data.

Bottom line, data pipelines seek to help businesses break down information silos and gain value from any available data.

How exactly does a data pipeline work?

 

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To help understand how a data pipeline works, let's picture a pipe.

A pipe carries something from a source to a destination for a certain purpose. In data terms, the process of moving data from different sources to a storage medium where it can be retrieved, used, and analyzed is called data ingestion.

While being transported along the pipe, the data can undergo different processes depending on the intention and destination. For example, a data pipeline could serve as a simple process of data extraction and loading. On the other hand, another data pipeline might be set up to handle data for more advanced purposes, such as a data warehouse for machine learning or analytics.

But regardless of its purpose, a data pipeline takes data from a source, manipulates it, and delivers it to a destination where it can be used, just like a pipe would work for other purposes in life.

As data moves through a data pipeline, four processes that happen are: collection, governance, transformation, and sharing.

Each set of data is merely a collection of raw data from different sources with no real value at the start point. This data can come from anywhere, be it from databases, file names, queues (JMS), topics (Kafka), or file paths (HDFS). This raw data needs to go through your data pipeline to become of use.

After collecting your data from the chosen sources, it needs to be organized, which is also known as ‘data governance'. In linking the raw data to its intended business purpose, you'll attach actionable value to it. This data can now be stored and used by the business.

The transformation process cleanses and modifies the data to align with your required reporting formats. This process includes removing corrupt and unnecessary data and optimizing the remaining data based on your needs.

Once the data has been transformed, it can be shared for use by the business. Data is usually stored in a cloud data warehouse or directly within your software applications and tools.

This is how data generally passes through a data pipeline.

What are the benefits of a data pipeline?

Now we know the data pipeline meaning, let's look at the benefits.

In the digital age, businesses handle large amounts of data from multiple sources. In order to analyze this data and make use of it, you need a single, overall view of the entire data set.

Data that comes from differing sources needs to be combined in a way that makes sense for your business when it comes to analyzing and making use of it. Many businesses struggle, with 95% saying the need to structure data is a key problem they face.

The problem is, data flow itself can be problematic and unreliable. Why? There are several points during the transportation from one system to another where data corruption or bottlenecks can happen. The larger the scale of data at hand, the larger these problems potentially become.

This is why data pipelines are vital. They get rid of the manual steps involved in the process and allow the smooth, automated flow of data from one place to another. Data pipelines are crucial for real-time analytics to help inform data-backed decisions.

You'll benefit from data pipelines if your business:

• Depends on the real-time analysis of data.
• Uses the cloud to store data.
• Has data in multiple sources.
  

In consolidating all the data from your different silos into one single source, you're ensuring a stable and reliable quality of data that enables fast data analysis for deeper insights.

The benefits of data pipelines are far-reaching and depend on their specific purpose. Need to know which customers viewed the latest agile manifesto or find out who has been purchasing your latest product matching software? Once your data is in a pipeline, you can start using it to drive results.

How are data pipelines architectured?

 

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There are different variations of data pipelines, for example, batch data processing, real-time streaming, multi-cloud data pipelines, and more. The pipeline you build will depend on your specific needs.

Let's take a look at some common data pipelines.

Batch data processing pipeline

A batch process is usually utilized for analytics where large samples of data are periodically collected, transformed, and then shared to a cloud-based data warehouse for use by the business.

Businesses can quickly and efficiently shift large volumes of data from siloed sources into the cloud where it can be processed with minimal human effort.

With batch processing, data is collected and stored during what's called the batch window, this helps manage large amounts of data and repetitive tasks in an optimized way.

Real-time streaming data pipeline

Streaming pipelines allow users to ingest data, structured and unstructured, from a wide range of data streaming sources, for example, the internet of things (IoT), social media streaming data, mobile apps, connected devices, etc.

The collected data then goes through real-time transformation using a streaming processing tool to deliver real-time analytics for multiple uses, such as predictive maintenance, fraud detection and prevention, customer improvements, and targeted marketing.

Cloud data pipeline

In the past, businesses relied on pipelines built in-house by developers, but the digital transformation and the rise of enterprise cloud services make this method more redundant and costly compared with today's data technologies.

It's now far more efficient and economic to build a cloud-based data pipeline than it is to operate on-premise infrastructure.

Multi-cloud pipelines allow data to be taken from one cloud and worked on before being loaded onto another cloud provider. This allows businesses to use cloud-based tools and also navigate any restrictions from one particular provider.

Cloud-based data pipelines are great for a more modern workflow that utilizes cloud-based tools and remote collaboration. Watch out for data limits though, some providers will increase costs once data sizes become larger.

How to create an efficient data pipeline

Building a data pipeline to serve your business needs may seem daunting. But there's a simple six-step process you can follow:

1. Collect and govern data, allowing access to secure and compliant data across the whole business.
2. Ingest data efficiently from different sources such as on-premises databases, software tool data, IoT sources, and streaming data into the cloud.
3. Create zones such as a landing zone, an enrichment zone, and business zones so that data can be integrated through cleansing, enriching, and transformation.
4. Make data available across the whole business to support DataOps by applying data quality rules to cleanse and manage data.
5. Prepare the cleansed and managed data so that it can be stored on the cloud to allow self-service analytics and other uses.
6. Conduct stream processing to gain insights from real-time streaming data.

Data pipeline tips

When it comes to creating a data pipeline, businesses should follow the best practices right from the initial design stage to ensure data processing and data transformation is effective, efficient, and easy to use.

Your data pipeline should be updated with the latest data and be able to handle large amounts of data to assist DataOps and Machine Learning Ops (MLOps).

In order to support the next generation of analytics and AIML (artificial intelligence and machine learning) functions, you need to make sure your data pipeline can:

1. Deploy and process data seamlessly on any cloud system for batch processing and real-time streaming data processing.
2. Ingest data efficiently from any source, whether it be on-premise systems, applications, software tools, databases, or IoT sources into a destination such as a cloud data warehouse or data lake.
3. Identify schema drift in your Relational Database Management System (RDBMS) schema and automatically replicate the required changes in real-time for data synchronization and live analytics.
4. Provide a simple-to-use wizard interface with no hand-coding to ensure a unified experience.
5. Implement AI and automation such as auto-tuning, auto-provisioning, and auto-scaling.
6. Deploy an advanced serverless environment to increase productivity and streamline operations.
7. Solve common data quality issues by applying data quality rules to cleanse data.
   

Examples of data pipelines in use

 

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Now that you've gained a better understanding of what data pipelines are and how they can benefit your business, let's look at some real-world examples to get you thinking about how they can help you.

Some common examples of data pipeline uses include:

• Construction firms tracking everything such as hours worked, costs of materials, and logistics data for picking a ship strategy.
• AI revolutionizing ecommerce stores by tracking consumer trends and uncovering insights into their target audience.
• Health industries analyzing large amounts of data to find effective treatments.
• Financial institutions using Big Data to predict upcoming trends and improve the customer experience.
• Businesses involved in entertainment, communication, and other media may use Big Data in several ways, for example, to deliver real-time social media updates, improve HD media streaming on devices, or to achieve low-cost digital marketing.
• Educational institutions tracking student success rates, teacher performance levels, and predicting enrollment trends.
• Energy firms may use Big Data pipelines to quickly find and isolate problems before they grow into bigger and more catastrophic issues.
• Governments can use data pipelines in various ways for uses such as processing claims, detecting fraud, identifying illnesses before they become widespread, and monitoring changes to the environment.
• Manufacturing businesses use Big Data pipelines to align their activities so they can bring customers the best products, lower their expenditure, and identify potential hazards.
• Advertisers may use a data pipeline to optimize their affiliate marketing efforts by finding out more about their viewers and their needs.

Final Thought

As the use of data becomes more and more important to business survival, and as new cloud capabilities emerge to empower businesses, there are real benefits to building intelligent and automated data pipelines.

The impact of data, AI, and machine learning for modern businesses are huge. With functional data pipelines, your business will be able to fulfill its potential by harnessing the power of deep data insights.

Hopefully, this guide on the data pipeline meaning has given you a better understanding of how data can help you.

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ABOUT THE AUTHOR

Pohan Lin - Senior Web Marketing and Localizations Manager

 

A Senior Web Marketing and Localizations Manager at Databricks, Pohan Lin specialises in demonstrating the impact of massive scale data engineering, data analysis, cybersecurity analytics, and collaborative data science. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth, Pohan Lin is dedicated to innovating the way we use data in marketing.