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What is a Data Pipeline? Why to Use Them, Different Types, and Standard Components

What is a Data Pipeline?

 

A data pipeline manages the flow of data from an initial source to a designated endpoint.  An organization’s specific use cases, needs, and requirements determine what happens to data in its journey through a pipeline – actions can range from basic extraction and loading tasks to more complex processing activities. 

 

Here’s a concrete example: Imagine that you’re the owner of an online business. You’re handling transactions 24/7 and capturing customer data in the databases and applications that keep your systems working nonstop. In addition to keeping your daily operations up and running, you need to tap into insight on how well different products are selling and what types of customers are purchasing them. 

 

To gain this insight, you have to move data from your transactional databases and applications to a system that can handle large volumes of data–and you need to process it along the way. After you transport and transform your transactional data, you have to analyze it with dedicated software to produce actionable insight.

 

You can perform these activities manually, but the undertaking is complicated and resource-intensive–it absorbs lots of valuable time and energy you could be devoting elsewhere. For rapid, automated data flow that provides business intelligence quickly, you need a data pipeline

 

Data pipelines automate and accelerate the flow of data for faster business insight.

 

Why Use a Data Pipeline?

   

Data is growing at an unprecedented rate and will continue to proliferate. With the dramatic increase in people working and learning at home during the pandemic, the amount of data created in 2020 was unusually high, and recent models forecast a compound annual growth rate (CAGR) of 23 percent through 2025. (1) 

 

Pipelines are essential in ingesting and transforming all of this raw data efficiently for use in today’s vast and continually growing range of applications, analytics platforms, and machine learning systems. Common data pipeline use cases include:

 

  • Providing sales and marketing data to CRM platforms that enhance customer service
  • Delivering online and mobile user behavior data to systems that generate product recommendations
  • Streaming data from equipment sensors to applications that monitor performance and determine when maintenance is needed
  • Bringing data together from disparate organizational silos to speed the development of new products 

 

Types of Data Pipelines

 

For many organizations, a primary consideration in implementing a data pipeline is determining whether it will be an on-premises or cloud-based solution. Implementing an on-premises pipeline allows a company to maintain complete control over its data, but is typically a costly, resource-intensive, and time-consuming endeavor. When an organization chooses a cloud data pipeline, a third-party cloud provider provides storage, computing power, and services via the Internet.

 

Benefits of a Data Pipeline

   

Data is growing at an unprecedented rate and will continue to proliferate. With the dramatic increase in people working and learning at home during the pandemic, the amount of data created in 2020 was unusually high, and recent models forecast a compound annual growth rate (CAGR) of 23 percent through 2025. (1) 

 

Pipelines are essential in ingesting and transforming all of this raw data efficiently for use in today’s vast and continually growing range of applications, analytics platforms, and machine learning systems. Common data pipeline use cases include:

 

  • Providing sales and marketing data to CRM platforms that enhance customer service
  • Delivering online and mobile user behavior data to systems that generate product recommendations
  • Streaming data from equipment sensors to applications that monitor performance and determine when maintenance is needed
  • Bringing data together from disparate organizational silos to speed the development of new products 

 

Types of Data Pipelines

 

For many organizations, a primary consideration in implementing a data pipeline is determining whether it will be an on-premises or cloud-based solution. Implementing an on-premises pipeline allows a company to maintain complete control over its data, but is typically a costly, resource-intensive, and time-consuming endeavor. When an organization chooses a cloud data pipeline, a third-party cloud provider provides storage, computing power, and services via the Internet.

Types of data pipelines include:   

 

Batch processing


Batch processing pipelines allow organizations to schedule regular transfers of large amounts of data. Batch jobs can be scheduled to run at set intervals (every 24 hours, for example) or when data hits a certain volume. 

 

Real time


Real-time data pipelines capture and process data as it’s created at the source. Common data sources include IoT devices, real-time applications, and mobile devices.

Cloud-native


Cloud-native pipelines are designed to operate with cloud sources, cloud destinations, or both. Hosted in the cloud, these scalable solutions allow organizations to offload infrastructure costs and management burdens.   

 

Open source


Organizations seeking an alternative to commercial data pipeline solutions can create and manage their own open-source data pipelines. These pipelines are fully customizable to a company’s specific needs, but creating and managing them requires specialized expertise.   

 

Data Pipeline Components

 

Data pipelines are typically designed with the following standard elements in mind. 

 

Destination


Pipeline design begins by considering the endpoint – the destination. Where is data needed and why is it needed there? Destinations can include data stores–data warehouses, data lakes, data marts, or lakehouses–or applications. 

 

Timeliness is an important consideration driven by a data pipeline’s ultimate destination. How quickly is data needed at the endpoint? Applications may require one, a few, or all data elements in real time. However, real-time pipelines can be costly if they heavily consume cloud resources.  

 

Origin


Initial data sources, or origins, are the next consideration in pipeline design. Will data enter the pipeline from transactional systems, data stores or both?   

 

Origin and destination determinations often go hand in hand, with data source choices impacting the choice of pipeline endpoints and endpoint requirements influencing the discovery of data sources.  For example, latency constraints at the origin of a pipeline must be taken into consideration along with the timeliness requirements at its destination. 

 

Dataflow


While origin and destination determine what enters a pipeline and what comes out, dataflow describes the movement of data through the pipeline. In other words, dataflow is the sequence of processes and stores that data moves through as it travels from source to endpoint.  

 

A dataflow may comprise just processes, with no intermediate data stores, but it cannot consist solely of stores without processes. Stores are useful in a data flow when one process is dependent on another process completing or when data serves multiple purposes and will be accessed by a number of processes.   

 

Storage


Storage refers to the systems where intermediate data persists as it moves through the pipeline and to the data stores at pipeline endpoints. 

 

Volume of data and number of queries to the system are often the key factors in choosing data storage types, but other considerations can include data structure and format, duration of data retention, uses of the data, governance constraints, security requirements, and disaster recovery needs.

 

Processing

 

Processing refers to the steps and activities performed to ingest, transform, and deliver data across the pipeline. By executing the right procedures in the right sequence, processing turns input data into output data.


Ingestion processes export or extract data from source systems, and transformation processes improve, enrich, and format data for specific intended uses. Other common data pipeline processes include blending, sampling, and aggregation tasks.

 

Workflow

 

A pipeline’s workflow defines and manages the sequencing of its processes and their dependencies on each other. A workflow handles sequencing and dependencies at two levels–the level of individual tasks performing a specific function and the level of units, or jobs combining multiple tasks.

 

Monitoring


Monitoring involves the observation of a data pipeline to ensure efficiency, reliability, and strong performance. Considerations in designing pipeline monitoring systems include what needs to be monitored, who will be monitoring it, what thresholds or limits are applicable, and what actions will be taken when these thresholds or limits are reached. 

 

Alerting


Alerting systems inform data teams when any events requiring action occur in a data pipeline.  

 

 

 

Data Pipeline Tools and Infrastructure

 

The tools and infrastructure used with a data pipeline depend on an organization’s size, industry, data volumes, data use cases, and security requirements. In working with data pipelines, commonly used elements include:

 

Batch schedulers 


With batch schedulers, users can set processing jobs to run at regular intervals or at certain data-volume thresholds.  

 

Data lakes 


Data lakes are centralized repositories for storing data in its raw format, regardless of source or structure. 

 

Data warehouses


Data warehouses are management structures that contain relational data and are typically designed to support business intelligence activities. 

 

ETL applications 


ETL applications ingest data from original sources, transform it into compatible formats for various uses, and deliver it to systems and destinations. 

 

Programming languages 


Programming languages – often Java, Python, Ruby, or Scala – are used by developers to define and write processes for data pipelines. 

 

Frameworks for streaming data 

 

Frameworks for streaming data include Apache Spark, Flink, Kafka, and Storm platforms, which process continuously generated data from sensors, devices, and systems. 

 

Matillion and data pipelines

 

You’ve determined that your business needs a data pipeline. How do you get started? 

 

With the Matillion Data Loader platform, you can create a data pipeline in just a few steps. Designed for both technical and non-technical users, the solution offers a low-friction SaaS platform for moving data quickly and cost-effectively.

 

Leverage the power of Matillion Data Loader to:

  • Load data into Snowflake, Amazon Redshift, or Google BigQuery environments in minutes
  • Migrate data between cloud platforms with ease
  • Schedule custom data loading and notification frequencies
  • Monitor your pipeline runs in real time with intuitive dashboards
  • Run your pipelines with a click and refresh your data on demand
  • Save time and reduce errors with self-healing pipelines that automatically adapt to schema drift

 

Read how marketing solution provider Knak increased team agility and productivity by implementing Matillion Data Loader for its pipeline needs. Knak implemented the platform to centralize data from multiple sources, relieve the burden on its IT team, and gain critical business insight far more quickly.  

 

Get started with Matillion Data Loader for free

 

Learn more about Matillion Data Loader or jump right in and get started now, for free.

 

Get started with Matillion Data Loader

 

 

Sources

(1) “Data Creation and Replication Will Grow at a Faster Rate than Installed Storage Capacity, According to the IDC Global DataSphere and StorageSphere Forecasts,” IDC, March 24, 2021