Establishing a Route from Ingestion to Analytics Using Data Pipeline Architecture

Business intelligence (BI) and analytics tools employ data warehouses to store raw data transferred from database sources and software-as-a-service (SaaS) platforms. Developers have two options: either they write their own code and manually interface with the source databases to create pipelines or use a SaaS data pipeline to avoid creating a pipeline from scratch.

Let's explore the basic parts and stages of data pipeline architecture and the technologies available for replicating data to comprehend how much of a revolution data pipeline-as-a-service is and how much labor is involved in building an antiquated data pipeline.

Data Pipeline Architecture

The design and organization of software and systems that copy, purge, or convert data as necessary and then route it to target systems like data warehouses and data lakes is known as data pipeline architecture.

The following three elements influence how quickly data flows through a data pipeline:

• A pipeline's rate usually referred to as throughput, describes how much data it can process in a specific amount of time.

• Reliability: For a data pipeline to operate dependably, each system inside the pipeline must be fault-tolerant. Data quality may be ensured by utilizing a robust data pipeline with integrated auditing, logging, and validation processes.

• The time it takes for a singular unit of data to move through a data pipeline is known as latency. While latency and reaction time are related, volume and throughput are less. Maintaining low latency may be an expensive endeavor in terms of both pricing and processing resources, and a corporation should establish a balance in order to maximize the value it gets from analytics.

Designing a Data Pipeline

A data pipeline architecture is layered. Every subsystem feeds into the next one until the data reaches its target.

Sources of Data

Data sources are the lakes, streams, and wells, where corporations initially gather data because we are discussing pipelines here. Each firm hosts dozens of additional sources on its networks, while SaaS companies support thousands of potential data sources. Data sources are crucial to the design of a data pipeline because they are the first layer. There is nothing to ingest and pass through the pipeline without high-quality data.


The operations that read data from data sources—the aqueducts and pumps in our plumbing analogy—make up a data pipeline's ingestion components. Utilizing the application programming interfaces (API) each data source offers, an extraction procedure reads from each data source. Data profiling, which involves studying data for its features and structure and assessing how well it matches a business objective, is a procedure that must be completed before you can create code that accesses APIs.

Following profiling, the data is consumed either in batches or as streaming.

Streaming Ingestion and Batch Ingestion

This is known as batch processing, when groups of records are extracted and processed collectively. Batch processing is sequential, and the ingestion technique reads, processes, and generates sets of records in accordance with standards that developers and analysts have previously established. The procedure operates on a timetable or responds to external triggers rather than continuously scanning for new records and advancing them.

A disparate data ingestion paradigm called streaming involves data sources sending out records or pieces of information one at a time automatically. Businesses only use streaming ingestion when they need near-real-time data for usage with analytics or apps that need the lowest latency. All organizations employ batch ingestion for a wide variety of data types.

The data is either transferred into a staging area or delivered directly along its flow, based on an enterprise's requirements for data transformation.


Data may need to change its structure or format after being extracted from the source systems. The data pipeline's desalination units, treatment facilities, and individual water filters are processes that change data.

Filtering, aggregating, and transferring coded values to more descriptive values are all examples of transformations. One particularly significant kind of transformation is combination. It comprises database joins, where connections between linked columns, multiple tables, and records can be made using relationships expressed in relational data models.

Whether an organization chooses to employ ETL (extract, transform, load) or ELT as their data replication procedure will determine the timing of any transformations (extract, load, transform). Before data is loaded to its destination, it can be transformed using ETL, an older method employed with on-premises data warehouses. ELT feeds data into contemporary cloud-based data warehouses without undergoing any changes. In a data lake or data warehouse, data consumers can then implement their transformations to the data.


The data pipeline's water towers and storage tanks serve as destinations. The primary location for data duplicated via the pipeline is a data warehouse. These specialist databases house all of an organization's cleansed, mastered data in one place for analysts and executives to use for reporting, business intelligence, and analytics.

Less structured data might enter data lakes, where data scientists and analysts can access vast amounts of valuable and mineable data.

The last option is for an organization to feed data directly into an analytics tool or service.


Data pipelines are intricate systems made up of networking, hardware, and software parts that are prone to failure. Developers must create monitoring, logging, and alerting code to assist data engineers in managing performance and resolving any issues to maintain the pipeline functional and capable of loading and extracting data.

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