Data is the new oil. Almost every industry is becoming more and more data-driven, and this trend will only continue to grow in the coming years. With so many organizations now relying on data for decision-making, they must easily access and analyze their information through data pipelines. This article will get you started on how to build your own data pipeline.

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Table of Contents

Data pipeline overview

The first step is to understand what a data pipeline does. Many companies think they need one but don't know how it will benefit them or even where to start building it. The primary goal of the pipeline is to take your raw information and turn it into something useful for analysis or decision-making purposes. It can also store critical company data in an organized manner so that all employees have access when needed.

Every organization has different needs, which means no two pipelines are precisely alike. This guide aims at covering many diverse use cases while still maintaining simplicity wherever possible. To ensure you get up and running quickly, this article focuses on tools built specifically for creating pipelines with ease instead of tackling every single problem yourself from scratch.

Types of data pipelines

Before diving into the different types of pipelines, it's essential to understand that there are two main categories: streaming and batch. The difference between these two types of pipelines is where the data originates from; either as a real-time stream or as large chunks of information collected over time (i.e., batches).

Streaming pipelines

A streaming pipeline is most helpful in analyzing data quickly and sending the results in real-time. This is often used for monitoring purposes since they can provide insights that are only available at this moment instead of being delayed hours or even days before receiving it. One example would be tracking website visits using a JavaScript snippet, which then sends these numbers to your analytics program as visitors head to each page on the site. If executed correctly, all users will have their session tracked immediately after entering your domain. So, there's no delay between what they do online and how many people are doing it.

Batch pipelines

If accuracy over time is more important than speed, batch pipelines might better suit your needs. They're ideal if you want to store information for future use but still want the option of analyzing it quickly when needed. One example would be storing customer data in a database that you can easily access whenever required. This allows for efficient storage while still getting updated information about each user when requested, without having to wait until the next time they log on again.

Why do you need a data pipeline?

Every company needs a data pipeline because it's the perfect way to bring all their essential business information together in one place. Imagine if your sales, marketing, finance, and manufacturing departments each maintained different contact lists for people they communicate with on an ongoing basis; this makes finding common customers or vendors difficult at best since there are so many individual spreadsheets floating around. Without having everything located within one centralized place, employees have no choice but to waste precious time looking through potentially dozens of files to find what they need instead of focusing on more pressing matters that generate revenue.

The same concept holds true when creating reports tied to specific goals you wish to reach as part of your overall plan. If the data you need is located in different locations, it's impossible to get an accurate snapshot of where things currently stand. A data pipeline solves this problem by collecting all the relevant information inside a single location - one that's easy to access on-demand whenever needed.

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Create an ETL plan to extract, transform, and load data into a database or analytics platform 

An ETL plan will help you create a more reliable data pipeline since it documents all the different tasks that need to be completed for everything to run smoothly. It also ensures each step is evaluated so there are no mistakes, which can cause costly delays when deadlines are involved or lead to unreliable results that damage your reputation with customers and partners alike.

The extract step will pull data from its source and load it into a staging area. The transformation step is where you cleanse the information, making sure there are no errors or missing pieces that could cause problems later on before loading everything to your final destination. This remains true whether this is a spreadsheet, database, Hadoop cluster, or another analytics platform.

The transform step can be time-consuming for large data sets that contain a lot of information. This would lead to unreliable results when using machine learning algorithms to discover hidden patterns in all your collected data, so make sure this step is performed thoroughly every single time. Still, it's an essential part of the process since you can't load poor quality or incorrect data into your analytics platform.

The load step is where you pull everything together to bring it all within the platform to begin generating reports and training your machine learning models. Depending on how much information is being processed, this step could take a significant amount of time for complex pipelines.

Choose a tool that can handle the volume of data you need to process efficiently

There are many options on the market today that can handle most types of data. However, it's essential to choose one that matches your needs instead of trying to fit a square peg into a round hole. This means finding something flexible enough to grow with you without requiring significant changes down the road and taking advantage of all built-in features before choosing any additional ones that could make things more complicated than they already are. Many tools also come with training programs designed specifically for beginners, so everyone involved knows how best to get started and work together to achieve success from day one - including realistic benchmarks and timelines based on real examples.

Xplenty is a data management tool that can handle all types of information without requiring you to hire expensive IT professionals or install any clunky software on your computers. It's also straightforward to use, making it perfect for beginners or anyone who wants a more seamless process that doesn't have any downtime.

Xplenty allows you to load data from virtually anywhere - whether this is your cloud server, Google Cloud Platform account, Amazon AWS storage container, FTP servers, JDBC-compliant databases (such as MySQL), and many others. You will be able to track and analyze everything from raw web analytics, website forms & eCommerce orders, social media activity including Twitter feeds, Facebook updates/likes as well as LinkedIn messages - even customer service interactions through phone calls and emails so your business can understand how it's perceived by its customers.

It's easy enough for beginners but powerful enough for experienced users simultaneously–making it an ideal choice when looking for a flexible solution. Plus, with over 100+ built-in connectors available, Xplenty can integrate with any data source and destinations your business may need.

How Xplenty Can Help

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To summarize, there are several essential steps to consider when building a data pipeline. We hope this guide has helped you better understand what it takes to create a solid foundation for your data pipeline. Please feel free to contact us for more information on how Xplenty can help you load and analyze all your data.

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