What is a Data Pipeline?

If you ask anybody, they will tell you that data is valuable. 

But what they are likely referring to is data analytics (i.e. reporting, dashboarding, predictive analytics, etc.). This is the output of data. To get to this stage, where data is centralized and made easily available for analysis, you need a data pipeline. 

This is where data engineering comes in. 

A data engineer focuses on the sources of the data, the quality of the data, the structure, formatting, accessibility, security, governance, and so on. All of these are extremely important to generate value for the organization. 

In this post, we look at what is a data pipeline, how it works, and what is its underlying architecture.

Data is water

To better understand what a data pipeline is, let’s start with the simple illustration below. Suppose there’s a village with different natural water sources such as rivers, lakes, and wells. To get the water, people must manually fetch the water, first walking to the source and then carrying it back to the village.

This is a perfectly suitable solution and achieves the desired result. But, as the village expands and their water needs increase, these traditional methods may become too inefficient. 

What’s needed is a new delivery method, one that increases the water supply easily and with far less effort. 

A more efficient solution would be to build a pipeline that automatically takes the water from these sources and delivers it to the village. This may even open up new uses for water that were previously not possible. An analogy we like to use here at Infostrux is ‘data is water’.

We view data engineers similar to plumbers, creating a network of pipes that connect and deliver water to the consumer.

Thinking about data in this way, data engineers lay the ‘info’structure for reliable and trusted data to be delivered so that it is available and easy to use for business intelligence, reports, dashboards, and other applications.

It’s not just about piping data, a data engineer needs to be thoughtful about the quality of the data that is being passed through the pipes and how it will be used for business analysis. 

This ensures only quality water (or data) is being delivered.

What is a data pipeline?

A data pipeline is similar to the above water pipeline example. Essentially, it’s a much more efficient mechanism to transfer data from point A to point B, with some intermediary steps in between.

The data producer (A) represents the various data sources. The in-between steps (C, D, and E) are where the data is ingested from the sources, cleaned, integrated, stored, modelled, documented, secured, and governed.

Finally, the data becomes usable by the data consumer (B) who creates reports, dashboards, and other data applications with the data.

Now, let’s take a closer look at the data pipeline so we can understand each of the layers and how they work together.

Data sources

The first section, Data Sources, may include a wide array of data producers with varying formats such as:
  • Google Analytics
  • CRMs
  • ERPs
  • Various ad platforms
  • Sensor data
  • IoT
  • Cloud APIs

Here are some common data sources that a company may want to ingest into a data pipeline focusing on marketing data.

Data pipeline

The data pipeline consists of multiple layers.

Raw – Data is ingested from the data sources. The raw data can be >structured, semi-structured, or unstructured. Using Snowflake, data can be loaded from the raw area with high velocity, high variety, and high volumes with ease.

Clean – Cleaning, or wrangling data, is the process of finding errors in your dataset and fixing them in preparation of analysis. This includes: typos, inconsistencies (e.g. CA, CAD, CAN, Canada, or LA, Los Angeles) incomplete information (e.g. dates that are missing the year), inaccuracies, irrelevancies, etc. This is where the raw data is transformed to more curated datasets.

Model – Data modeling is the process in which data is further refined into consistent structures of defined measures (things you can measure such as revenue, cost of goods, gross margin, units sold, etc.) and dimensions (things that cannot be measured such as area codes, sales associates, location, product type, date, etc.). In the modelling stage, you can also enhance your data by establishing hierarchies, setting units and currencies, and adding formulas to make your analysis easier and consistent.

Store – Data is stored in a data repository such as a data lake, data warehouses, or data marts. Depending on the needs of the business, each of these data stores serve different purposes.

Work & Staging – The Work & Staging areas are typically used by data engineers and data scientists to handle operational tasks for managing the data pipeline, preparing and testing some of the transformations.

Data consumers

After the data has been ingested, cleaned, modelled, and stored, it is ready for consumption. 

Data consumers can now use clean and reliable data in meaningful ways from data visualization and reporting in a Business Intelligence (BI) tool, data science applications such as predictive and ML models, and ad hoc SQL queries.

ETL vs ELT?

A digital transformation has been taking place with many organizations migrating from on-premise servers to the cloud. 

The transformation brings a host of benefits, which include a shift from traditional on-premise ETL data pipeline process to a cloud-based ELT process.

what is an etl pipeline Infostrux

ELT stands for “extract, load, and transform” — it describes a data pipeline process where data is taken from a source, replicated, placed in a data storage, and then transformed.

In an ETL model, transformations occur prior to the load phase, which results in a more complex data replication process. However, it is ideal when a destination requires a specific data format. ETL tools require processing engines prior to loading data into the destination. For organizations looking for data compliance and privacy, ETL is ideal since it cleans sensitive and secure data before sending it to the data warehouse.

With ELT, organizations can use a BI tool to transform data, thus removing a step and making the data loading process more streamlined. For cloud data warehouses, ELT is an optimal approach since organizations can transform their raw data at any time and avoid a step in the data pipeline.

One important advantage to using Snowflake data cloud platform is that transformations and data modeling use SQL, which is a common programming language familiar to most data teams. This allows data scientists and analysts in most organizations to work with the data in a language they all understand.

The benefits of ELT

ELT and cloud-based data warehouses and data lakes have several benefits over ETL and on-premises hardware. For further reading, check out: 5 Mistakes Organizations Commonly Make with Data

The Infostrux solution

At Infostrux, our team of SnowPro Certified Data Engineers and Data Architects build and manage automated data pipelines so that your data is centralized, cleaned, and modelled in alignment with your specific business requirements. Whether your data strategy is to drive growth, reduce costs, or mitigate risks, we can help. From small to medium-sized businesses with fewer data sources to large enterprises facing big data challenges, our team of data navigators can deliver data you can trust.
Scroll to Top