Data Pipelining Explained: Why Do We Need This and What are Its Tools?

For a large business, a routine may include placing orders, fulfilling these, and managing operational efficiency. For an online business, it means that your website must be able to effectively manage and maintain the data (including details like order ID, user ID, credit card details, etc.). Choosing the right online transaction processing system helps to manage aspects like shopping cart, etc, and ensure that your business runs smoothly minus data pileup.

Such a data processing system may aid in evaluating the business performance and customer details like order frequency, etc. Thus your business needs to go beyond transactional data and then move it from the database that supports such transactions using a high-quality system to handle and manage data. It is also common to transform it before loading to the final storage destination. Once all these actions are completed, your business can use a dedicated server to analyze the data.

However, your business still needs a set of tools and activities to transport data from the source to its final destination. It also uses an effective data storage and processing system to further store data on multiple channels. Selecting the right pipelines means allowing for automatic information transportation and transformation before bringing in a high-performance data storage system.

Each business tries to seek unique yet affordable ways to integrate the data from multiple sources to gain useful insights and competitive advantage.

Thus, the data pipelining is a set of actions that can ingest raw data from disparate sources and then move the same to another destination for adequate storage and maintenance. This pipeline can also include filtering and features that can shield the business against failures.

What is data pipelining and why does it matter?

Before we explain the term What is data pipelining and its usage, let’s imagine a scenario –

Focus on any pipeline that gets something from its source and then is carried away to its destination. Thus may be designed to take things away for effective storage and management.

Similarly, a data pipeline is a simple process that uses various methods of data extraction, loading, etc. It may be designed to handle data using any advanced tools like training datasets for machine learning.

Data pipeline vs ETL system

An ETL system refers to a specific data pipeline that can “Extract, Transform and Load”. It is the process of moving data from one source like an application to a destination like a Waterhouse where operations like the following are performed

  • Extract – pulling the data out from the source
  • Transform – Modifying the data adequately so that it can be loaded into the destination
  • Load – inserting the data into the destination

ETL has historically been used for managing batch workloads, especially on an extremely large scale. However, an advanced version of the same is also available that helps to attain real-time data management and streaming event data.

Considerations of the data pipeline

Data pipeline architecture requires the assessment of critical aspects of data storage and management. The storage depends on the following aspects –

  • Does the pipeline need to handle streaming data?
  • What is the rate of data that you expect to manage and maintain?
  • How much and types of processing requirements need to occur during the process?
  • Is the data generated in the cloud or on-premises?
  • Where will the collected data move to?
  • How do you plan to construct the entire pipeline while managing microservices?

Understanding big data pipelines

As the volume, variety, and intensity of data inside the organization grows, data architects have been assessing new ways to handle and manage huge data.

This big data has a huge volume of data that needs to be maintained and stored properly to use in case of –

  • Predictive analysis
  • Real-time reporting
  • Alerting, etc.

Like other components of the data architecture, these have evolved to support big data and are designed to incorporate any one or more components of big data.

The velocity of the big data makes it appealing to build streaming data pipelines for big data. Such data can be captured in real-time to ensure that the right actions can occur.

The volume of such big data necessitates that the pipelines must be scalable as the volume of the same is variable over time. This is because the same means that a lot of data events can occur simultaneously so the pipeline must be scalable to process significant amounts of data concurrently. This variety of big data requires that big data pipelines are easily recognized and process data in different formats like structured, semi-structured, and unstructured.

Tools of the data pipeline

Businesses are modernizing themselves and their data infrastructure by adopting special cloud-native tools. Automated data pipelines are a key component of the modern data stack and enable businesses to embrace new data sources and ultimately improve business intelligence.

The modern data stack consists of the following –

  • Any automated data pipeline tool like Fivetran
  • A cloud data destination like Snowflake, AWS Redshift, Databricks, Lakehouse, etc.
  • Any business intelligence engine like Tableau, Chartio, etc.
  • A post-load transformation tool like the data build tool by Fishtown Analytics, etc.

Data pipelines enable the easy and effective transfer of data from the source platform to its destination. The data can be consumed by analysts and data scientists to procure valuable information.

Some of the basic steps of data transfer include – 

  • Reading from the source

Sources of data input can include production databases like MySQL or MongoDB, etc, or web applications like Salesforce or MailChimp.

A data pipeline reads from these API endpoints at the scheduled intervals.

  • Define the destination

Destinations may include specific data warehouses like Lakehouse, BigQuery, Databricks, etc.

  • Transformation of data

Data professionals need structured and accessible data that in turn can be interpreted so that it makes sense. Data transformation enables you to alter data and format to make it relevant and meaningful for your business. Such transformation can include the following steps –

  • Constructive processes like adding, copying, and replication of data
  • Destructive processes like – deleting records, fields, or columns
  • Aesthetics like standardizing salutations, names of areas, or data cleansing
  • Transformation to make data well-formed and organized. Use of tools like dbt to standardize, sort, and validate data brought in from the pipeline.

A data pipeline must have a series of data processing steps to ensure that the same is not ingested at the beginning of the pipeline itself. There must be a series of steps to ensure that the same reaches the next step easily and efficiently until the entire process is complete. In specific data pipelines, these destinations may be termed sinks and pipelines ensure that the flow reaches from the data lake to either an analytical database or a payment processing system. These pipelines may also have the same source or sink. Thus, anytime the data is processed between A to point B, there will be a data pipeline between these two points. Common steps in each pipeline can include –

  • Data transformation
  • Augmenting
  • Enrichment
  • Filtering
  • Grouping
  • Aggregator
  • Running algorithms, etc.