What is Data Pipeline? : Types, Components and Use Cases

on Data Aggregation, Data Analytics, Data Driven Strategies, Data Extraction, Data Integration • February 4th, 2022 • Write for Hevo

What is Data Pipeline

Most modern businesses make use of a large number of platforms to smoothly run their day-to-day operations. This is a result of the developments in Cloud-based technologies. Data Pipelines make it possible for companies to access data on Cloud platforms. But What is Data Pipeline? and what does it do?

For example, the Marketing team might be using a combination of Marketo and HubSpot for Marketing Automation, whereas the Sales team might be leveraging Salesforce to manage leads, and the Product team might be using MySQL to store customer insights.

This would lead to the fragmentation of data across numerous tools and result in the formation of Data Silos. As a result, there is no single location where all data is present and cannot be accessed if required. Data Silos can make it extremely difficult for businesses to fetch even simple business insights.

Hence, there is a need for a robust mechanism that can consolidate data from various sources automatically into one common destination. This data can then be used for further analysis or to transfer to other Cloud or On-premise systems.

This article will provide you with a comprehensive understanding of what is Data Pipeline, what its components and key types are, and the various architectures that are implemented to create Pipelines.

Table of Contents

What is Data Pipeline?

Data Pipelines
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A Pipeline can be defined as a series of steps implemented in a specific order to process data and transfer it from one system to another. The first step in a Pipeline involves extracting data from the source as input. The output generated at each step acts as the input for the next step.

This process continues until the pipeline is completely executed. In addition, some independent steps might run in parallel as well in some cases.

It usually consists of three main elements, i.e., a data source, processing steps, and a final destination or sink. Pipelines give users the ability to transfer data from a source to a destination and make some modifications to it during the transfer process.

Pipelines may also have the same source and destination, with it only being used to transform the data as per requirements.

However, the variety, volume, and velocity of data have changed drastically and become more complex in recent years. Hence, Pipelines now have to be powerful enough to handle the Big Data requirements of most businesses.

It is of paramount importance to businesses that their pipelines have no data loss and can ensure high accuracy since the high volume of data can open opportunities for operations such as Real-time Reporting, Predictive Analytics, etc.

Pipelines for are built to accommodate all three traits of Big Data, i.e., Velocity, Volume, and Variety. The velocity with which data is generated means that pipelines should be able to handle Streaming Data. This data has to be processed in real-time by the pipeline.

The volume of the generated can vary with time which means that pipelines must be scalable. The Pipelines should be able to accommodate all possible varieties of data, i.e., Structured, Semi-structured, or Unstructured.

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Why Do We Use Data Pipeline?

Now you have understood What is data pipeline but why do we use it? Data is growing at a rapid rate and will continue to grow. The pipelines are widely used in ingesting data that is used for transforming all the raw data efficiently to optimize the data continuously generated daily.

This transformed data can be used for Data Analytics, Machine Learning, and applications. Some of the use cases of what is Data Pipeline are listed below:

  • Delivering the Sales and Marketing data to CRM platforms to enhance customer service.
  • Streaming the data from sensors to the applications for monitoring the performance and status.
  • Unifying the data together so that it can speed up the development of new products.

Data Pipeline vs ETL

ETL and Pipeline are terms that are often used interchangeably. ETL stands for Extract, Transform, and Load. ETL pipelines are primarily used to extract data from a source system, transform it based on requirements and load it into a Database or Data Warehouse, primarily for Analytical purposes.

Then What is data pipeline? However, it can be seen as a broader term that encompasses ETL as a subset. It refers to a system that is used for moving data from one system to another. This data may or may not go through any transformations. It may be processed in batches or in real-time; based on business and data requirements.

This data might be loaded onto multiple destinations, such as an AWS S3 Bucket or a Data Lake, or it might even be used to trigger a Webhook on a different system to start a specific business process.

Types of Data Pipelines

Now that you have understood what is Data Pipeline and ETL. Lets’s read about different Pipelines. So What is Data Pipeline types, the list is follows:

  • Batch: Batch processing of data is leveraged when businesses want to move high volumes of data at regular intervals. Batch processing jobs will typically run on a fixed schedule (for example, every 24 hours), or in some cases, once the volume of data reaches a specific threshold.
Batch Processing Data Pipeline
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  • Real-time: Real-time Pipelines are optimized to process the necessary data in real-time, i.e., as soon as it is generated at the source. Real-time processing is useful when processing data from a streaming source, such as the data from financial markets or telemetry from connected devices.
Real-time Processing Data Pipeline
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  • Cloud-native: These pipelines are optimized to work only with Cloud-based data sources, destinations, or both. These pipelines are hosted directly in the Cloud, allowing businesses to save money on infrastructure and expert resources.
  • Open-source: These pipelines are considered to be suitable for businesses that need a low-cost alternative to commercial pipelines or wish to develop a pipeline to fit their unique business and data requirements. However, these pipelines require the support of trained professionals for their development and maintenance.

However, it is important to understand that these types are not mutually exclusive. This means that a Pipeline can have all characteristics of two different types. For example, Pipelines can be Cloud-native Batch Processing or Open-Source Real-time processing, etc.

Components of a Data Pipeline

Components of a Data Pipeline
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After you have read about What is data pipeline, and its types. Let’s read about its components. The components of a Pipeline are as follows:

  • Origin: Origin is the point of entry for data from all data sources in the pipeline. Most pipelines have transactional processing applications, application APIs, IoT device sensors, etc., or storage systems such as Data Warehouses, Data Lakes, etc. as their origin.
  • Destination: This is the final point to which data is transferred. The final destination depends on the use case. The destination is a Data Warehouse, Data Lake, or Data Analysis and Business Intelligence tool for most use cases.
  • Dataflow: This refers to the movement of data from origin to destination, along with the transformations that are performed on it. One of the most widely used approaches to data flow is called ETL (Extract, Transform, Load). The three phases in ETL are as follows:
    • Extract: Extraction can be defined as the process of gathering all essential data from the source systems. For most ETL processes, these sources can be Databases such as MySQL, MongoDB, Oracle, etc., Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) tools, or various other files, documents, web pages, etc.
    • Transform: Transformation can be defined as the process of converting the data into a format suitable for analysis such that it can be easily understood by a Business Intelligence or Data Analysis tool. The following operations are usually performed in this phase:
      • Filtering, de-duplicating, cleansing, validating, and authenticating the data.
      • Performing all necessary translations, calculations, or summarizations on the extracted raw data. This can include operations such as changing row and column headers for consistency, standardizing data types, and many others to suit the organization’s specific Business Intelligence (BI) and Data Analysis requirements.
      • Encrypting, removing, or hiding data governed by industry or government regulations.
      • Formatting the data into tables and performing the necessary joins to match the Schema of the destination Data Warehouse.
    • Load: Loading can be defined as the process of storing the transformed data in the destination of choice, normally a Data Warehouse such as Amazon Redshift, Google BigQuery, Snowflake, etc.
  • Storage: Storage refers to all systems that are leveraged to preserve data at different stages as it progresses through the pipeline.
  • Processing: Processing includes all activities and steps for ingesting data from sources, storing it, transforming, and loading it into the destination. While data processing is associated with the data flow, the focus in this step is on the implementation of the data flow.
  • Workflow: Workflow defines a sequence of processes along with their dependency on each other in the Pipeline.
  • Monitoring: The goal of monitoring is to ensure that the Pipeline and all its stages are working correctly and performing the required operations.
  • Technology: These are the infrastructure and tools behind Data Flow, Processing, Storage, Workflow, and Monitoring. Some of the tools and technologies that can help build efficient Pipelines are as follows:
    • ETL tools: Tools used for Data Integration and Data Preparation, such as Hevo, Informatica PowerCenter, Talend Open Studio, Apache Spark, etc.
    • Data Warehouses: Central repositories that are used for storing historical and relational data. A common use case for Data Warehouses is Business Intelligence. Examples of Data Warehouses include Amazon Redshift, Google BigQuery, etc.
    • Data Lakes: Data Lakes are used for storing raw Relational or Non-relational data. A common use case for Data Lakes in Machine Learning applications being implemented by Data Scientists. Examples of Data Lakes include IBM Data Lake, MongoDB Atlas Data Lake, etc.
    • Batch Workflow Schedulers: These schedulers give users the ability to programmatically specify workflows as tasks with dependencies between them to automate and monitor these workflows. Examples of Batch Workflow Schedulers include Luigi, Airflow, Azkaban, Oozie, etc.
    • Streaming Data Processing Tools: These tools are used to handle data that is continuously generated by sources and has to be processed as soon as it is generated. Examples of Streaming Data Processing tools include Flink, Apache Spark, Apache Kafka, etc.
    • Programming Languages: These are used to define pipeline processes as code. Python and Java are widely used to create Pipelines.

Benefits of a Data Pipeline

When companies don’t know about what is Data Pipeline, they used to manage their data in an unstructured and unreliable way. But as they came to know about What is Data Pipeline and how it helps companies save time and keep their data organized always. A few benefits of Pipeline are listed below:

  • Data Quality: The data flows from source to destination can be easily monitored and accessible, and meaningful to the end-users. 
  • Incremental Build: Pipelines allow users to create dataflows incrementally. You can pull even a small slice of data from the data source to the user.
  • Replicable Patterns: It can be reused and repurposed for new data flows. They are a network of pipelines that creates a way of thinking that sees individual Pipelines as examples of patterns in a wider architecture.

Data pipeline Architecture

Companies are shifting towards adopting modern applications and cloud-native infrastructure and tools. It involves the movement or transfer of huge volumes of data. Automated Data Pipelines are key components of this modern stack that allow companies to enrich their data, gather them in a central repository, analyze it and improve theri Business Intelligence.

The Modern stack includes the following:

  • An Automated Data Pipeline tool such as Hevo.
  • A destination Cloud platform such as Google BigQuery, Snowflake, Data Lakes, Databricks, Amazon Redshift, etc.
  • A data transformation tool
  • Business Intelligence tools such as Tableau, Looker, and Power BI.

Automated Data Pipelines such as Hevo allow users to transfer or replicate data from a plethora of data sources to a single destination for safe secure data analytics to transform raw data into valuable information and generate insights from it.

Data Pipeline Architecture
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Steps to Data Transfer include:

  1. Reading data from a source: Sources can be SaaS applications, productions Databases, web applications, or on-premise systems. A data pipeline reads data from the source at scheduled intervals.
  2. Defining a destination: Destinations can be cloud Data Warehouses, Databases, Businesses Intelligence tools, etc.
  3. Transforming Data: Data needs to be structured in order to use it in the right way. The structured data can be interpreted and analyzed to extract valuable information that may make sense to data professionals. 

Examples of Data Pipeline Architectures

Some examples of the most widely used Pipeline Architectures are as follows:

  • Batch-based Pipeline: In this example, you have a data source which might be an application such as a point-of-sale system that generates numerous data points that need to be pushed to a Data Warehouse or any other Analytics database. The pipeline for this use case will have the following architecture:
Batch-based Pipeline - What is Data Pipeline?
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  • Stream-based Pipeline: In this example, data from the source system has to be processed as it is generated. The Stream Processing engine in this pipeline will feed outputs from the pipeline to Data Stores, Customer Relationship Management (CRM) systems, Marketing applications, etc. A Stream-based Pipeline will have the following architecture:
Stream-based Pipeline - What is Data Pipeline?
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  • Lambda Pipeline: This pipeline is a combination of Batch and Streaming Pipelines. This architecture is widely used in Big Data environments as it gives developers the ability to account for historical Batch analysis and Real-time Streaming use cases.
    A vital aspect of this architecture is that it encourages storing data in a raw form, allowing developers to run new Pipelines to correct any errors in prior pipelines or integrate new data destinations as per the use case. The following image shows the Lambda Architecture:
Lambda Pipeline - What is Data Pipeline?
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This article provided you with a comprehensive understanding of what is Data Pipeline are. It also helped you understand the fundamental types and components of most modern Pipelines.

Most businesses today, however, have an extremely high volume of data with a dynamic structure. Companies study what is Data Pipeline creation from scratch for such data and the complexity involved in this process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased data volume and Schema variations. Businesses can instead use automated platforms like Hevo.

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