what is a data pipeline

What is a Data Pipeline? Types, Use Cases and Top Benefits

Businesses and corporations rely heavily on data to assist in crucial decision-making, streamline operations and improve their products and services.

Data is the bridge that links companies with their customers and enables them to understand their customer base on a deeper level.

Data is usually found in many different forms and is scattered across different platforms. To gather all the raw data a business has and turn it into useful information, data analysts use data pipelines.

Since technology has made it easier to access and manipulate data coming from multiple sources, ensuring the effective and secure collection, management and processing of data has become even more critical. And that’s the job of data pipelines.

So, what is a data pipeline, and how does it help businesses get more out of their data? Let’s find out!

What is a Data Pipeline and How Does it Work?

what is a data pipeline

A data pipeline is a set of processes used for collecting, managing, interpreting, and analysing raw data. Data pipelines start by collecting data from different types of sources. That data is then transferred to a repository, where it is subsequently filtered, processed and interpreted.

Say that in your company, your respective sales, marketing and customer support teams have different raw information that enters their databases. The absence of a channel that consolidates all these big and small pieces of information often leads to unmissable errors.

Such errors typically take the form of repetitive figures in your reports, increased data latency and the inability to adapt to more complex functions and market demands. If left unresolved, your business can lose its potential to meet customer expectations and generate a higher ROI.

Main Types of Data Pipelines

There are three main types of data pipelines. Let’s look at each of them and find out what characteristics make them unique.

#1. Batch Processing Data Pipelines

Batch processing involves converting data in clusters in a fixed time frame instead of accumulating and interpreting it individually. Hence, it is ideal for systems that regularly work with large quantities of data, such as SaaS applications, payroll, sales, billing and inventory management.

In batch processing, data is extracted from the source before it is computed, classified and screened. For instance, this eliminates the need for constant human supervision when processing simultaneous payments or transactions done by different customers on an e-commerce website. Batch processing may take several minutes or a few days.

#2. Streaming Data Pipelines

In contrast to batch processing, streaming data pipelines work on data in real time. This pipeline channels information as it is created to ensure a continuous flow of data. In turn, applications and systems that depend on an uninterrupted influx of information can function optimally.

Some concrete examples of how streaming data pipelines are applied include fraud detection in credit card companies, personalising the customer experience using past purchasing behaviour, machine learning and log streaming.

#3. Event-Driven Data Pipelines

With the keyword here being “event-driven”, this pipeline processes data based on an action done by or triggered by a user. Specific actions on the user’s end follow a precise chain of commands that lead to an outcome or a result.

An example of an event-driven data pipeline application is when a player inputs a command on the controller and triggers a specific action in the game. A reactive type of data pipeline architecture is also useful for streamlining the flow of tasks within a team.

Benefits of Using a Data Pipeline

If your business is working with data that’s scattered across different teams and platforms, consolidating it in a data warehouse is the optimal way to get the most out of your data. To get started, it’s crucial to set up a good data pipeline. The benefits of one are as follows:

  • A data pipeline enables flexible and effective data management. At present, it is more common for companies to generate user data across multiple sources. A set of clear-cut data pipeline stages not only streamline all this information but broaden the types and volume of data that an organisation can handle.
  • A data pipeline supports businesses that aim to attain more independence in their operations. A business that is capable of organising, analysing and transforming data into essential facets of improving its products and services unlocks a higher potential for long-term growth.
  • A solid data pipeline architecture paves the way for enhanced data security for both organisations and their users. Security breaches have become rampant over the past years, and so a data pipeline adds an impenetrable wall to protect a company’s data.

Data Pipeline vs ETL: What are the Differences?

ETL pipelines are a subset of data pipelines. So, in theory, they are similar processes; however, there is one key characteristic that differentiates them.

Data pipelines and ETL pipelines differ in that the data conversion process is not a necessity in data pipelines. In some organisations, a data pipeline’s main functions may be simply collecting and storing information.

Another big difference is that data pipelines are often used for the continuous and real-time influx of data, while ETL processes information in batches and follows a timetable.

Data Pipeline Components

To further understand how data pipelines work, it’s useful to look at the individual components that construct a data pipeline.

#1. Data Sources

Data sources are the origin of all information extracted by data pipelines. All the data that the pipeline is going to work with will come from these sources.

Common examples of data sources are CRMs (Customer Relationship Management systems), payment gateways, social media management tools and IoT device sensors.

#2. Data Processing

After data is loaded into the pipeline from different sources, it then goes through processing. In this stage, raw data is classified and filtered to eliminate redundancy. Information is also analysed and validated to determine whether all of the details gathered merit any form of advantage.

#3. Data Storage

Once data processing and analysis are complete, the results can be stored permanently. Data storage is used to retain data once it has gone through the data pipeline.

Data storage can act as an extra layer of security for an organisation’s database, given that it is the final draft of all the data gathered and transformed through the pipeline. What this means is that any sudden changes and additional steps in using the data will not compromise the workflow in an organisation.

Common Data Pipeline Use Cases

Below are four of the most common data pipeline use cases to help you get a clearer picture of how it can benefit your company’s goals in the long run.

#1. Data Analysis and Reporting

A systematised library of relevant data is the secret to successfully maximising the use of information for a greater purpose.

Imagine that your organisation uses multiple data sources without a consistent structure to hold and process large volumes of information altogether. It would be as if you were comparing apples to oranges. It is impossible to make sense of any data provided by your end-users, or any viable resource for that matter, while it’s scattered across platforms.

To help analyse and report on your data in the best way possible, you should utilise a data pipeline that can help you see beyond raw numbers and interpret them correctly.

#2. Data Visualization

data pipeline

Data pipelines can be used to visualise data using bar graphs, pie charts, heat maps, etc.

When interpreting data, sometimes it can be difficult to identify patterns by just looking at the raw information. Visualising data can help you overcome that challenge and discover new insights.

These visuals can not only help you get more out of your data, but they can enrich your client reports and help your agency make a better impression.

#3. Machine Learning and AI

Machine learning (ML) and AI must be constantly “fed” with data in order to accomplish the tasks they are designed to execute. Data pipelines can facilitate that to ensure the proper functioning of ML and AI algorithms.

Data pipelines are responsible for securing a continuous flow of data accumulation, processing and storage for machine learning models to improve. Consequently, AI can identify and interpret customer-generated data with higher accuracy and adeptness.

#4. Business Intelligence

Business intelligence is a crucial tool for businesses in 2023. Data pipelines can enable businesses to make data-driven decisions and positively impact their operations.

Business owners have become more elaborate with the types of information they use to boost profit. They have also become more keen to expand resources where they can find just the data they need.

Some go as far as building their own data warehouse from scratch, while others use a combination of readily available data sources to hit multiple marks on their key metrics. Either way, data pipelines assist businesses in pinpointing customer pain points and discovering opportunities to boost sales and retention rates.

Consolidate Your Agency’s Data in BigQuery with the Help of Acuto

At this point, it must be clear that effective data warehousing is essential for organisations to survive in a competitive and tech-savvy era. Otherwise, the absence of reliable data processing could limit your capabilities as a business and hinder your performance. This is a common occurrence whenever you’re working with siloed data.

Acuto understands the value of having accessible data in attaining success for your business. We can tailor-fit a BigQuery data warehouse based on your business’s unique requirements to consolidate all your data in a SSOT, making it a breeze for your teams to use all your data as they see fit.

More importantly, we enable you to keep all your data secured in one place and your operations well-equipped to face tougher industry challenges in the future.

Consult our team to learn more about what we can do for your business.

Key Takeaways

Knowledge is power, and the right information can give you an edge over your competition. However, without a reliable system to amass, evaluate and implement useful data into practicable strategies, you will be a step behind.

Having a data pipeline in place can help you work with the data you already have and extract more insights from it.

Here is a quick run-down of the main points we discussed that emphasise the importance of having a strong data pipeline system:

  • A data pipeline is a set of processes that gather, analyse and store raw data coming from multiple sources.
  • The three main data pipeline types are batch processing, streaming and event-driven data pipelines.
  • make the seamless gathering, storage and analysis of raw data possible
  • ETL pipelines differ from data pipelines because they always include data processing as part of the pipeline, but not all data pipelines include that step.
  • The three main components of data pipelines are data sources, data processing and data storage.
  • Data pipelines are typically used to enhance data analysis and reporting, create visual data interpretations, magnify the accuracy of machine learning and AI and augment a company’s overall performance through business intelligence.
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