What Is a Data Pipeline? Definitions, Types & Use cases

DiT

Introduction

Several business models integrate data with other systems to gain competitive advantages. To extract raw data from different sources like Relational databases, CMS platforms, Enterprise resource planning (ERP) platforms, social media management tools, and data streams to the point of use. These businesses make use of a data pipeline.

A data pipeline uses data ingestion and transfers extracted or raw data to a location for storage and analysis from various sources. It also can contain filters and other features that provide resilience for failures.

In this article, you will learn about data pipelines, how data pipelines operate, why we need data pipeline tools and many more.

What is a data pipeline?

A data pipeline is a set of steps to perform certain operations and transmit data from one system to another in a specific manner. Data pipelines are designed to obtain data from various sources. During each step, the output is used for the next step. This procedure will continue until all pipeline operations have been completed.

Sometimes independent actions may run simultaneously with one step as well. Data pipeline processes are often performed using special software systems and tools.

Some standard data pipeline tools are Talend Open Studio, Apache Airflow, Luigi, Apache Spark, IBM DataStage Informatica Power Center, and Oracle Data Integrator.

It typically has three main components — data sources, processing steps, and destination.

Why do we need data pipelines?

Today’s competitive environment requires modern data processing systems to help quickly move data and extract valuable information as data continues to expand and multiply. Data pipelines are often used to ingest, transform, and aggregate raw data to extract valuable information either in batch processing, stream processing, or both to drive business goal.

Data-driven enterprises require a data-driven environment where data is transferred efficiently and quickly. Unfortunately, there exist several barriers limiting the clean data flow. Pipelines take the steps necessary to find the solution and turn manual operations into an efficient automated system.

Data pipelines provide speedy data analysis for business insights by combining data from all your diverse sources into one central repository. Additionally, they guarantee continuous data quality, which is essential for obtaining accurate business insights.

Though not everyone needs a data pipeline, it would make a good solution for all businesses and is most helpful:

Some use cases for the Data Pipeline can be found below:

1. Machine Learning
2. Data Analytics
3. Business intelligence
4. Artificial Intelligence research

How to Build a Data Pipeline

Our digital world is continuously generating massive amounts of data which is crucial if we want government function and business success. Not only are there many data sets available, but there are many processes and things that can lead to poor data delivery.

An organization must choose the method of ingestion it intends to utilize to extract data from sources and transport it to the destination before establishing a data pipeline. Two typical approaches to ingestion are batch processing and streaming. Therefore, before moving the data to the desired location, it must be decided whether to employ an ELT or an ETL transformation procedure. Later in this post, we will outline the critical distinctions between the two methods.

And it is only the first step in building a data pipeline. Creating reliable, scalable, low-latency data pipelines requires a lot more work.

Data pipeline examples

Data pipeline designs and complexity depend on their intended function. Macy Streaming Services uses the data from its on-premise database in Google Cloud. This enables the customer experience regardless of whether they buy online or offline. HomeServe uses streaming data pipelines to transfer leaked data into big Google queries. LeakBot’s software analyzes device performance and optimizes its algorithms continuously.

How do data pipelines work?

Your business probably works with a lot of data. To evaluate all of your metrics and data and provide useful insights, you must have a single view of your data.

But in order to evaluate your data successfully, you’ll need to organize and combine it if it originates from several platforms, applications, and devices. You could believe that you can integrate your data by copying and pasting it from one source to another. However, using this approach might result in data corruption or blockages, rendering the data you’ve gathered meaningless.

Data pipelines are useful in this situation. Imagine a data pipeline as a water pipe that transports water from one place to another to better comprehend how it operates.

The same principles govern a data pipeline. It safely moves data from one or more sources, such as a customer relationship management (CRM) platform or analytics tool, to a different location, such as a data warehouse, allowing you to organize and analyze your data in one location. Data pipeline software automates this process of data extraction from disparate sources.

7 Components of modern data pipelines

Now that you are aware of what a data pipeline is and how it functions, let’s look at the seven components of a data pipeline below;

1. Origin/Source
2. Data flow
3. Processing
4. Destination
5. Storage
6. Workflow
7. Monitoring

Data Source

In a data pipeline, the source is where data is first entered. A company’s reporting and analytical data environment may have data sources (transaction processing applications, IoT devices, social media, APIs, or any public datasets) and storage systems (cloud data warehouse, data lake, or data lakehouse).

Data flow

Data flow refers to the transfer of data between points of origin and destinations, taking into account any modifications and data storage it encounters along the route.

Processing

Data is typically taken from sources, modified and changed in accordance with business requirements, and then placed at its destination. Some of the operations performed in the data pipeline processing stage are:

1. Transformation
2. Augmentation
3. Filtering
4. Grouping
5. Aggregation

Destination

A destination is where data is sent in the end. Therefore, transferring the data to its final destination is the last stage in a data pipeline. To store all of your data in one location, you’ll often use your data pipeline to transmit your data to a large-scale storage platform like data lakes, data warehouses, or data mart, or it can be supplied to feed analytical and data visualization tools and real-time processing.

Storage

Systems that maintain data at various points along the pipeline are referred to as storage. In addition, data storage relies on a number of variables, such as the amount of data, how frequently and in-depth a storage system is queried, how the data will be used, etc

Some common storage types are:

Data warehouse

A data warehouse is used to store data in a queryable format. A Data warehouse contains organized and relational data that is optimized for business analysis and reporting.

Data mart

A data mart is a more compact kind of data storage that often concentrates on a single data subset, such as sales or leads. A data mart can be said to be a smaller data warehouse.

Data Lake 

A data lake is generally used with ELT and big data. Data lake enables you to store unstructured, structured, and raw data at any scale.

Workflow

The workflow outlines the order of activities (or jobs) in a data pipeline and how they are interdependent. You might benefit from having knowledge about jobs, upstream and downstream. A job is a single unit of labor that completes a certain task, in this instance, changing data. Upstream and downstream refer to the source and destination, respectively, of data flowing through a pipeline. Data travels through the data pipeline like water. In addition, upstream tasks must be completed satisfactorily before moving on to downstream tasks.

Monitoring

Monitoring helps evaluate the efficiency, accuracy, and consistency of the data as it moves through the various processing stages of the data pipeline and ensures that no information is lost along the way. Check out an article on Data Monitoring.

Types of Data Pipeline Architecture

The precise configuration of parts that allows for information extraction, processing, and distribution is referred to as a data pipeline architecture. There are a number of popular designs that businesses might take into consideration.

ETL data pipeline

ETL (Extract Transform Load) is the most typical data pipeline design and has been a standard for decades, as we previously stated. It gathers raw data from several sources, converts it into a single pre-defined format, and puts it into a destination system, usually a corporate data warehouse or data mart.

ETL Pipelines Use Cases

Data migration from legacy systems to a data warehouse, gathering user data from various touchpoints to have all customer information in one place (typically in the CRM system), combining large volumes of data from various internal and external sources to provide a comprehensive view of business operations and joining disparate datasets to enable deeper analytics are all examples of data management strategies.

The main drawback of the ETL design is that if business rules (and specifications for data formats) change, you have to rebuild your data pipeline. Another data pipeline architectural technique, known as ELT (Extract Load Transform), emerged to solve this issue.

ELT Data pipelines

The order of the processes in ELT differs from ETL, as intuition rightly predicts: loading comes before the transformation. This small alteration appears to cause big changes. You transport massive volumes of raw data right into a data warehouse or data lake instead of first transforming it. Then, you may analyze and organize your data as needed – whenever, entirely or partially, a single time or repeatedly.

ELT pipelines use cases

Extract Load Transform architecture is beneficial when huge amounts of data are involved, you are unsure of what you will do with them, and you are unsure of exactly how you want to change them.

ELT, however, is still a less developed technology than ETL.

Streaming data pipeline

Real-time or streaming analytics involves drawing conclusions from constant data flows within milliseconds. Contrary to batch processing, a streaming pipeline continuously updates metrics, reports, and summary statistics in response to each available event. It also constantly ingests a series of data as it is being generated.

Real-time analytics enables businesses to get up-to-date operational information, respond swiftly, or offer intelligent infrastructure performance monitoring solutions. Streaming architecture is preferable to batch for firms that cannot afford any delays in data processing, such as fleet management companies using telematics systems.

Batch Data Pipelines

When data is gathered, analyzed, and published to a database at once or regularly in big blocks (batches). A batch is queried by a person or a computer program for data exploration and visualization once it is available for access.

Batch Pipeline execution can take a few minutes, several hours, or even days, depending on the batch size. The procedure is frequently launched at times of low user activity to prevent overwhelming source systems (for example, at night or on weekends.)

Working with large datasets in projects that don’t require immediate attention may be accomplished via batch processing. But you should choose architectures that support streaming analytics if you want real-time information.

Big data pipeline

Big data pipelines carry out the same functions as their smaller counterparts. Their capacity to enable Big Data analytics involves managing large amounts of data arriving quickly from many sources (100+) in a wide range of forms (structured data, unstructured data, and semi-structured data).

Since ELT appears ideal for a Big Data pipeline, it can load a limitless quantity of raw data and perform streaming analytics to extract insights as they happen. However, batch processing and ETL can now handle enormous volumes of big data owing to contemporary techniques.

Organizations often use a combination of ETL and ELT, several stores for various formats, and batch and real-time pipelines to analyze Big Data.

Difference between a data pipeline and an ETL pipeline.

ETL pipelines can also be considered data pipelines. The ETL process (“extract transform load”) is performed from data sources or different sources. It is processed and converted to the proper format during the transformation phase to reach the desired destination (usually a database server). While legacy ETL has a slow transition step, ETL software such as Striim replaces disk processing with in-memory processing that allows real-time data transformation, enrichment, and analysis. The last step in ETL involves putting the results into the target destinations. On the other hand, a data pipeline is a more general term that describes a process in which data is passed from one system to another, which can transform data.

Do data analysts build data pipelines?

For speed and accurate information, analysis data engineers create data-intensive pipelines for operationalizing data.

What to consider when building data pipelines?

Before you build any data pipeline, you should think about some things.

1. What am I trying to achieve?
2. When Do I Need Information?
3. What are the sources of data?
4. What happens to the data?
5. What should we use if we need information about something?
6. Can you list the amount of information retrieved?
7. How frequently does information change?

Is the data pipeline the same as ETL?

ETL is an operation that extracts data from one system, transforms it, and loads it to one target system. A data pipeline is a more general term that describes a process in which data is passed from one system to another, which can transform data.

Check out some cool data engineering projects here.