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Data Pipelines: The Complete Guide

Data uncovers deep insights, enhances efficient processes, and fuels informed decisions. But with data coming from numerous sources, in varying formats, stored across cloud, serverless, or on-premises infrastructures, data pipelines are the first step to centralizing data for reliable business intelligence, operational insights, and analytics. Learn what a data pipeline is, architecture basics, and how to choose the right tools for your organization.

What is a Data Pipeline?

A data pipeline aggregates, organizes, and moves data to a destination for storage, insights, and analysis. Modern data pipeline systems automate the ETL (extract, transform, load) process and include data ingestion, processing, filtering, transformation, and movement across any cloud architecture and add additional layers of resiliency against failure.

How it Works:

To understand how a data pipeline works, let’s take any pipe that receives something from a source and carries it to a destination. This process of transporting the data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization, is known as data ingestion.

Along the way of transportation, the data undergoes different processes depending on the business use case and the destination itself. A data pipeline may be a simple process of data extraction and loading, or, it may be designed to handle data in a more advanced manner, such as a data warehouse for predictive analytics or machine learning.

As the data moves through the pipeline, there are four processes that occur: collect, govern, transform, and share.

Each dataset is a collection or extraction of raw datasets pulled from any number of sources. The data comes in wide-ranging formats, from database tables, file names, topics (Kafka), queues (JMS), to file paths (HDFS). There is no structure or classification of the data at this stage; it is a data dump, and no sense can be made from it in this raw form.

Once the data is collected, it needs to be organized at scale, and this discipline is called data governance. By linking the raw data to its business context, it becomes meaningful. Enterprises then take control of its data quality and security and fully organize it for mass consumption.

The process of data transformation cleanses and changes the datasets to bring them the correct reporting formats. This includes eliminating unnecessary or invalid data and enriching the remaining data in accordance with a series of rules and regulations determined by the business’ needs.

After the data is transformed, trusted data is finally ready to be shared. It is often output into a cloud data warehouse or endpoint application for easy access by multiple parties.

Architecture Basics:

Data pipelines can be architected in different ways. The most common examples are batch data processing, streaming data, and multi-cloud pipelines. Unlike a batch-based pipeline, a streaming pipeline could feed outputs from the pipeline to data stores, marketing applications, and CRMs as well as back to the point of sale system itself as continuous data flow, allowing for real-time data analytics.

Real-Time Streaming Data Pipelines

Modern businesses prefer this architecture because it factors in both real-time data streaming use cases and historical batch analysis. Lambda architecture encourages storing data in raw format so that you can continually run new data pipelines to correct any code errors in previous pipelines. You can also create new data destinations that enable new types of queries.

Multi-Cloud Data Pipelines

Today's organizations require data pipelines with real-time streaming capabilities, and the ability to route data across cloud, on-prem, or even serverless architectures. Cloud data warehouses like Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure SQL Data Warehouse allow enterprises to scale compute and storage resources with minimal latency.

Preload transformations can thus be skipped and all of the organization’s raw data can be directly loaded into the data warehouse. Transformations can then be defined in SQL and run in the data warehouse at query time.

Streaming data pipelines are thus ideal for replicating data cost-effectively in cloud infrastructure. It removes the need to write complex transformations as a part of the data pipeline. But most importantly, streaming pipelines give analytic teams more freedom to develop ad-hoc transformations according to their particular needs in the data pipeline without waiting for data to be processed, transformed, mapped, or stored.

Empowering Real-Time Data Pipelines for the Enterprise

There are many ETL tools that help you build data pipelines, but Confluent is the only data streaming platform that not only automates powerful, real-time data pipelines, but includes streaming data integration with 120+ pre-built connectors. With unlimited scalability, infinite storage and retention, and enterprise grade security and support, Confluent's technology is trusted by over 70% of the Fortune 500.

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To win in today’s digital-first world, businesses must deliver exceptional customer experiences and data-driven, backend operations. This requires the ability to react, respond, and adapt to continuous, ever-changing data from across an organization in real time. However, for many companies, much of that data still sits at rest in silos across their organizations.

Used by Walmart, Expedia, and Bank of America, today, Confluent is the only complete data streaming platform designed to stream data from any source, at any scale. Built by the original creators of Apache Kafka, today, its streaming technology is used by 80% of the Fortune 100. Capable of not only real-time data ingestion, but large scale, streaming data pipelines that automate real-time data flow across any system, application, or data store with 120+ pre-built connectors.