Data from disparate sources is getting the most focus and more organizations are wanting to embed data integration and analytics into their business workflow for garnering business insights. With this, the significance of data pipelines is increasing rapidly. As a set of network connections that oversee performing data movements from source systems to target systems, a data pipeline is accessible to users for further analysis.

Businesses can have thousands of data pipelines that can assist in lessening complications of data management. They just need a GUI-driven development infrastructure with appropriate version control competencies, a data pipeline monitoring app, and defined processes to develop, maintain, and manage data pipelines.

This article details everything about data pipelines – an overview, its salient benefits, architecture, types, processes involved, and its comparison with ETL. Let us have a look at what is a data pipeline first.

What is a Data Pipeline?

In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. – Wikipedia

A data pipeline is a continual series of actions, tools, and processes for preparing enterprise-level data for further analysis. It consists of different technologies that can confirm, summarize, and extract data patterns for tasks like machine learning tasks, data visualization, etc. It automates the movement of data between the source and the target systems.

Once transferred, data pipelines convert data into a single, consolidated data repository. This empowers users to quickly access desired data whenever they wish to make any strategic decision. Users could be data scientists, data engineers, data analysts, BI developers and analysts, product officers, operational workers, marketing officers, etc. As per its name, data pipelines act as a ‘pipe’ for the projects through which a variety of data from different sources can pass through.

Data pipelines can be implemented either on-premises or using cloud services. For the on-premises pipeline, you must purchase hardware and software for your data center. It is time-consuming but offers you complete control. For a cloud-based pipeline, you can avail the service provider’s storage space and computing power and pay only for the resources utilized.

Salient Benefits of a Data Pipeline

Data pipelines are highly critical to any organization, dealing with data. Here are some of the key advantages of using it:

• Elimination of manual activities
• Automation of data flow
• Effective and real-time data analytics
• On-premises or cloud-based storage
• Data storage in multiple sources
• Enhanced data quality
• Smooth and complete data integration
• Offers cost-saving business processes
• Continuous accessibility and disaster recovery
• Extensible data processing
• Self-service management

How Does a Data Pipeline Work?

A data pipeline works like what a routine pipeline does, carrying data from one end to the other, and transferring it to the destination. It includes the following components:

• Source/Origin – the point of entry for data in a data pipeline, with data sources such as IoT devices, transaction processing, social media, APIs, data sets, data warehouses, etc.
• Destination – the final point to which data is being transferred, which could be data visualization or analytical tool or a data lake/data warehouse
• Dataflow – the movement of data from the source to its destination and the data stores that it moves through
• Storage – the systems wherein data is stored when it is on its way through the pipeline
• Processing – the steps taken for data ingestion from sources, further processing, and making it reach the destination
• Workflow – the series of activities and their dependencies in a pipeline, along with upstream and downstream jobs
• Monitoring – keeping a strict view of how the pipeline and its internal tasks are performing

Types of Data Pipelines

There are three types of data pipelines:

• Batch processing pipelines

Data processing and storage in batches or large volumes, mainly for high-volume tasks. It is based on a series of commands that are executed sequentially. The pipeline loads the complete batch into a data warehouse or store.

• Streaming data pipelines

A continuous, incremental sequencing of small data packets that showcase a series of events occurring one after the other, for real-time analytics. Data is processed even if some data packets are unavailable.

• Lambda architecture

This type of data pipeline showcases the properties of both – batch and streaming pipelines. It is used in Big Data environments where there are different use cases with varied nature.

Data Pipeline Vs ETL Pipeline

Since both these technologies look at data similarly, they are used synonymously. But they both serve different purposes. Data pipeline is a widely used category for data moving between systems whereas ETL is a specific pipeline. Data pipelines don’t need to execute in batches always whereas ETL pipelines usually execute in batches.

Data pipelines don’t need to undertake data transformation, they may, or may not whereas ETL pipelines must transform data before loading it. Data pipelines may keep working even after loading the data, they may stream or analyze data. However, the ETL pipeline stops once data is loaded in the target store. ETL pipeline follows a specified sequence of extracting, transforming, and then loading. The data pipeline need not follow the same sequence.

Some Popular Data Pipeline Tools

A range of data pipeline tools contribute to the smooth execution of the entire process. They belong to different categories as below:

• ETL/data integration tools – Oracle Data Integrator, Talend Open Studio, etc.
• Data warehousing tools – Amazon Redshift, Snowflake, etc.
• Data lakes – Microsoft Azure, Google Cloud, etc.
• Real-time data streaming tools – Apache Storm, Amazon Kinesis, etc.
• Workflow schedulers – Azkaban, Luigi, etc.
• Big Data tools – Hadoop, Spark, etc.

On a Concluding Note

With the growing volume and complexity of data, data pipeline optimization has become important. The future of data pipelines and their optimization is bright and with the advent of newer technologies and approaches, the efficiency levels and business outputs are going to enhance. Data scientists and data engineers can make the most of data pipelines for optimal business performance and insightful data analytics.

Ridgeant’s data warehousing services incorporate consultation, implementation, migration, and managed services to facilitate organizations to combine data in capable data warehousing solutions. Our developers leverage the best of progressed platforms and next-gen technologies to offer custom solutions and services that solve today’s real-world tasks.

Our adaptable engagement models, tailored approach, and use of the latest tools and technologies result in quicker time-to-market, efficient, and secure data pipelines. Associate with Ridgeant for any kind of data-related needs and leverage our potential to serve you the best.