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Guide to Data Pipelines: How They Work, Why You Need One and What Your Options Are

December 28, 2020
-
Peter
Scobas
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Running a business means you’re generating a ton of data. 

For example, if you’re an ecommerce merchant, you’re gathering data every day on things like site traffic, sales, products and inventory, marketing and advertising, customers and many, many more categories related to your business operations. 

The question then becomes: how can you lay the best foundation to turn a wide range of metrics and datasets from disparate data sources into actionable insights for your business?

The answer starts with a data pipeline - a critical component of business intelligence and analytics for any company, no matter the size or industry. Here, we’ll walk you through what a data pipeline is, how they’re created, and why they’re so important when it comes to gathering and analyzing your data.

What is a data pipeline?

Investing in data science and analytics to help make business decisions is an important step forward for your organization. However, insights are only as good as the data you have available. You need to make sure you have a strong data foundation - and that foundation starts with a data pipeline.

A data pipeline is an automated process made up of a set of actions - or “jobs” - used to extract and manipulate data from various sources into a format you can use to then analyze and gather insights across those sources. 

Think of all the different places your business has data stored:

  • Ecommerce platform
  • POS system(s)
  • Advertising platforms
  • Other marketplaces or sales channels
  • Email marketing/CRM
  • Inventory managment/fulfillment platform
  • Customer support apps
  • Loyalty apps
  • Operations and financial management platforms

A data pipeline is how you can extract and move your data from all those disparate apps and platforms into one central place, and into a usable format for reporting across sources (so, for example, you can combine and analyze data from your ecommerce and retail sales and your email and Facebook marketing, even if they aren’t in the same format to start with). 

If you’re a business that relies on multichannel insights or a company that understands the importance of using data to help make decisions, configuring a data pipeline is step one in your business intelligence and analytics journey - before you can extract valuable insights from your data, you first need a way to gather and organize it.

How do data pipelines work - and what are they used for?

Earlier, we mentioned that a data pipeline is an automated process made up of a set of actions, or “jobs.” A job can be something like: 

  • Take three columns from one data table and merge it with columns from another table 
  • Change the new columns’ formatting
  • Replace the NULL values
  • Load this new data into a different database

A data pipeline can be made up of many, many jobs just like this.

A robust and scalable data pipeline allows your business to have all your data in the same place and in the same format - without requiring you to manually import and clean your data every time you need to analyze it. This saves you time and ensures that the data you rely on to help drive business decisions is clean and correctly formatted for reporting.

Here’s an example. Say you’re an ecommerce company, and you have customer and purchase-level data from your Shopify store, email marketing campaign data in MailChimp, and advertising performance data in Facebook, Instagram and Google Ads (not to mention data on your website traffic and behavior, returns and shipping details, customer success, and other metrics from a myriad of other sources).  

Your goal is to optimize your email and social media marketing efforts, from campaign to final conversion, using all the data you have at your disposal. 

Here’s one way you could do it: after you run an email marketing or Facebook ad campaign, you could manually gather all the data from your various sources, try to clean and format it as best you can, and run your own calculations to analyze the data and gather insights. But what about the next campaign, or the next? 

That’s where a data pipeline comes in. A data pipeline saves you time by automating these tedious processes, so you can focus on uncovering insights and identifying opportunities that help you drive strategy - not wrestling with unnecessary data manipulation. Also (and potentially most importantly): developing a data pipeline allows for greater accuracy, ensuring that you’re not introducing errors into your data sets by manually gathering and formatting all your data.

What are the different types of data pipelines? 

Luckily, companies have options when it comes to data pipelines - from choosing an existing data pipeline solution versus building their own to selecting a specific type of data pipeline. There are several different types of data connectors:

  • Cloud-native vs. on-premise: A cloud-native solution is a pipeline using cloud-based tools. You rely on infrastructure and warehousing tools based in the cloud, which is typically cheaper and involves fewer resources. Cloud-native is now the norm for data pipelines - previously, businesses generally utilized on-premise solutions.
  • Batch vs. real-time: Businesses generally opt for a batch processing data pipeline solution when they are looking to integrate their data at specific time intervals - say, daily or weekly. Consider that marketing campaign example we discussed previously. In order to analyze your ad campaign performance, you may only need to pull in that data at a scheduled interval (for example, weekly). This differs from a real-time pipeline solution, where you need the ability to process your data in (you guessed it) real-time. A real-time or streaming data pipeline solution is useful for businesses that need to process real-time financial or economic data, location data, or communication data rapidly.
  • Open-source vs. proprietary: if you’re looking for cheaper, publically available solutions, open-source tools are a great option. However, you’ll need to make sure you have the expertise and resources on your team to expand and modify the functionality of these tools to fit your specific business needs. This differs from proprietary or “out-of the-box” tools which may be customized for a specific business use case and require little to no maintenance.
types of data pipelines
Types of data pipelines

How do you know which data pipeline solution you need?

The type of data pipeline solution that your business needs depends on a few different factors. When evaluating data connectors for your business, it’s important to think through the entire business intelligence and analytics process at your organization and ask questions like: 

  • What types of data do I have access to? 
  • How should it be extracted, organized, and maintained?
  • How often do I need data to be updated and refreshed?
  • What kind of internal resources do I have to maintain a data pipeline?
  • What is my end goal for my data?

You can build a data pipeline on your own - however, connecting your various data sources and building a sustainable and scalable (not to mention accurate) workflow from scratch can be quite the challenge. If you’re considering this option, really think through what the process would look like and what would it take. Since a data pipeline consists of many different components, there’s a lot to think about:

  1. What general data insights are you interested in? What data sources does your business currently use and have access to?
  2. A data pipeline only gets you part of the way there when it comes to business intelligence - you’ll still need to gather additional solutions to help you with data storage and reporting/visualization. What additional systems need to be put in place? At this point, you’ll need to decide on a data warehouse to store the data - should you opt for an AWS environment? Google BigQuery? Microsoft Azure? What business analytics tools will you use once you have your data in an organized and streamlined format? Power BI? Looker? Tableau? Mode Analytics?
  3. Once you make these decisions, you’ll have to start the pipeline implementation process. This involves configuring database connections, handling API integrations, modifying (or creating) data schema, writing scripts to clean and merge data columns, deciding how to deal with missing values and formatting issues, and ultimately organizing all these systems to migrate all the data from your various data sources into a single warehouse.

If that seems overwhelming to you, or you don’t have the internal technical resources to manage a project of that scale, you’re definitely not alone - most businesses choose to use a pre-built data pipeline to help them connect data sources. This gives you the benefits and flexibility of a data connector, without the hassle of creating your own from the ground up. However, there are several things to consider when evaluating pipeline solutions, as well.

When deciding on a data connector solution, there are five major components to consider:

  1. Ease of set up: Are you a business with the resources (and the need) to invest in a custom data pipeline solution? Or should you opt for a pre-built resource that fits your business and offers a faster and easier setup process?
  2. Connectivity: Does the data pipeline option you’re considering allow you to connect to all the data sources you need it to? 
  3. Reliability: Can you count on your pipeline to extract and clean the data your business relies on, without breaking down or encountering errors?
  4. Repeatability: Does your data pipeline save you time and act as the foundational component for your business’s entire business intelligence and analytics lifecycle?
  5. Breadth of features: Is your data pipeline a data connector only - requiring you to invest in additional platforms for data storage and visualization? Or does it include or partner with other platforms to cover the entire spectrum of business intelligence?
Questions to ask when evaluating data pipeline options


>>> Glew’s data pipeline makes it easy to connect and transform all your data sources for powerful multichannel reporting, with simple, no-code set-up, 90+ integrations, automated ELT and no additional cost. Learn more.

Many pre-built, “out-of-the-box” data pipeline solutions can vary in the data sources that they connect to and work with, and in the features they provide - whether that’s an included data warehouse or reporting and visualization capabilities. The last thing you want to do is invest your time and money into a data connector solution that doesn’t have the ability to connect to all the data sources you need or requires tedious workarounds to get to the end result you need. Before you decide on a data pipeline solution for your business, make sure that it includes all the features and functionality you need.

If you have the resources, building a data pipeline from scratch can certainly be a worthwhile investment. However, keep in mind that a data connector is just a tool to help your company better understand your data and make business decisions. So in many cases, going with a robust existing solution may be the best, easiest option for you and your business.

Wrapping up - why are data pipelines so important?

It’s simple: if you’re running a business, you need to be able to make data-driven decisions. A data pipeline is the foundation of your company’s business intelligence and analytics - it ensures that you’ll be able to connect all of your disparate data in one place, and format it in a way that enables calculation and reporting across those sources.

If you’re manually trying to extract and merge data from various sources all the time, you’re bound to make mistakes. When it comes to data-driven decisions, it’s critical to make sure that your data is clean and correct - insights derived from incorrect data are worse than no insights at all. Investing in business intelligence and analytics can unlock exponential growth and drive your business forward - and it all starts with a data pipeline.

Next, we’ll cover the second piece of the business intelligence puzzle: data warehouses. But don’t forget these key takeaways about data pipelines:

  • A data pipeline is an automated set of actions used to extract and reformat data from multiple sources
  • They’re necessary for businesses with multiple data sources that need to aggregate and report on that data
  • Data pipelines save time by automating and scaling manual data extraction and cleaning processes - they also help with data accuracy
  • There are many different kinds of data pipelines - including cloud vs. on-premise, batch vs. real-time and open-source vs. proprietary 
  • If you’re looking for a data pipeline solution, you can create your own from scratch if you have the resources, or utilize a pre-built data pipeline
  • When evaluating data pipeline solutions, consider the ease of set up, number and range of data connections, reliability and scalability of data extraction and cleaning, and breadth of additional features 

Plus, further reading about other elements of the business intelligence process:

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