Data Pipeline

Data Pipeline Definition

A data pipeline is the series of automated, consecutive data processing steps involved in ingesting and moving raw data from disparate sources to a destination. Data pipeline software facilitates the seamless, automated flow of data from one system to another, with common steps including: aggregating, augmentation, data transformation, enrichment, filtering, grouping, and running algorithms. A common data pipeline example is the etl data pipeline.

This image shows an outline of a data pipeline from the beginning of the building process to the data that is analyzed.

What is a Data Pipeline?

Data pipeline is a broad term referring to the chain of processes involved in the movement of data from one or more systems to the next. Data pipeline tools and software enable the smooth, efficient flow of data; automate processes such as loading data, processing data, extraction, transformation, validation, and combining; protect data integrity; and prevent bottlenecks and latency.

The steps that occur in between the data sources and the destination depend entirely on the use case. A simple data pipeline may only include a single, static data source, extraction, loading, and a single data warehouse.

A complex data ingestion pipeline may involve ingesting and processing multiple data streams in parallel, real-time data sources, transformation, training datasets for machine learning, a visual analytics destination, and multiple pipelines that in turn feed into other pipelines or applications. The best data pipeline solution depends on the nature of the project and business objectives.

Data Pipeline Solutions

A well managed data pipeline infrastructure is a crucial element in data science and data analytics. Data flow is susceptible to disruption, and useful analysis is dependent upon data reaching its intended destination uncorrupted and in a timely manner. An optimized data processing pipeline facilitates reliable delivery of data sets that are centralized, structured, and accessible to data scientists for further analysis. 

Some of the most popular data pipeline architectures include: 

  • Batch Processing: Batch data pipeline processing is ideal for work that does not require real-time data. Large amounts of data are moved at consistent intervals. 
  • Real-Time: Also known as stream processing, a real-time pipeline is ideal for streaming data that is being created in real-time.  
  • Cloud Native: A cloud native pipeline is hosted in the cloud and works with cloud-based data, relaying on the hosting vendor’s infrastructure. This is particularly useful for time-sensitive business intelligence applications.
  • Open Source: Open-source is best for teams that are looking to lower upfront costs and also have data engineers with the technological expertise to develop and modify the public tools available in an open-source pipeline.

Building a Data Pipeline

Some companies have thousands of different data analysis pipelines running concurrently at any given moment. But the most basic steps for building data pipelines typically include, but are not always limited to: identifying data sources; extraction and joining data from disparate sources; data categorization; standardization; data cleansing and filtering; loading data into the destination; and automating the process so that it runs continuously and on schedule.

Data pipeline monitoring tools should be integrated into the architecture to preserve data integrity and alert administrators of failures such as network congestion or an offline destination.

What is a Big Data Pipeline?

Data pipelines in big data are pipelines developed to accommodate the volume, velocity, and variety of big data. Big data analysis pipelines often have a stream process architecture that is scalable, can capture and process data in real-time, and can recognize both structured and unstructured data formats.

An example of big data pipelines is interactions on social media. A single post on a social media platform could generate a series of pipelines branching off into multiple other pipelines, such as a sentiment analysis application, a word map chart application, and a social media mentions counting report.

Does OmniSci Offer a Data Pipeline Solution?

OmniSci provides an agile data pipeline that moves big datasets from the source to the data analyst in milliseconds. OmniSciDB skips ingest-slowing pre-computation by using the supercomputing level of parallelism provided by CPUs and GPUs to ingest the entire dataset into the system, where it can be queried and evaluated on a real-time analytics dashboard. OmniSci Immerse is ideal for operational use cases with high-velocity data constantly streaming at the organization.