What is Big Data Pipeline?

The term "data pipeline" describes a set of processes that move data from one place to another place. While data travels through the pipeline, it can undergo a variety of transformations, such as data enrichment and data duplication.

Big data pipelines perform the same job as smaller data pipelines. With big data pipelines, though, you can extract, transform, and load (ETL) massive amounts of information. The difference matters because experts expect a tremendous increase in data production as time goes by.

In other words, big data pipelines are subsets of ETL solutions. Much like typical ETL solutions, they can work with structured data, semi-structured data, and unstructured data. The flexibility makes it possible for you to extract data from practically any source.

Big data pipelines can also use the same transformations and load data into a variety of depositories, including relational databases, data lakes, and data warehouses.

The most important difference between big data pipelines and regular pipelines is the flexibility to process massive amounts of data. A big data pipeline may process data in batches, stream processing, or other methods. All approaches have their pros and cons. Whatever the method, a data pipeline must have the ability to scale based on the needs of the organization in order to be an effective big data pipeline. Without scalability, it could take the system days or weeks to complete its job.

Related Reading: The Five Types of Data Processing

Big Data Pipelines by Industry

Some businesses rely on big data more than others. Those include:

  • Online and brick-and-mortar retail stores that track consumer trends.
  • Healthcare organizations that analyze massive amounts of data to find effective treatments.
  • Banking and finance institutions that use big data to predict trends and improve customer services.
  • Construction companies that track everything from material costs to hours worked.
  • Transportation companies that analyze traffic and help commuters reach their destinations as quickly as possible.
  • Organizations that work in entertainment, media, and communications use big data in several ways, such as providing real-time social media updates, improve HD media streaming, and improving connections between smartphone users.
  • Schools, colleges, and universities measure student demographics, predict enrollment trends, improve student success, and determine which educators excel.
  • Manufacturing and natural resource groups need big data pipelines to streamline their activities to lower overheads, deliver the products that consumers need, and identify potential dangers.
  • The government uses big data pipelines in a huge range of ways, such as analyzing data to track changes in the environment, detect fraud, process disability claims, and identify illnesses before they affect thousands of people.
  • Energy companies use big data pipelines to manage workers during crises, identify problems quickly so they can start finding solutions, and give consumers information that can help them use lower amounts of energy.

As the usefulness of big data becomes more apparent, more and more companies adopt the technology so they can streamline processes and give consumers the products they need when they need them.

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Glossary of Terms

A guide to the nomenclature of data integration technology.