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.
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.
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.
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.
Modern businesses prefer this architecture because it factors in both real-time 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.
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.
There are many quality tools to automate and simplify data pipelines for fast and easy data integrations, regardless of the format or source. Confluent Cloud not only simplifies real-time pipelines, they help you solve your biggest data collection, extraction, transformation, and transportation challenges at scale without the complexity of traditional ETL tools.
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