A Simple Step by Step Guide to Data Pipeline Architecture
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Though it may seem unlikely, data has the same personality as you. Put yourself in its shoes, and you will see why? Like us, data also ask these questions to itself- What my purpose is? What values do I have? How can I be beneficial to others? And most importantly, Why do I need transformation? How can I reach from my place to my target? To make data valuable, transfer it to the destination with transformation, and without any intervention of a hand-coding, we can use a data pipeline.
It is a fully managed system that routes data from disparate sources to a final destination and transforms along the route. With the help of a data pipeline, the data can reach its targeted destination with a transformed personality. This blog will explain how a data pipeline handles the data flow from the source to the destination and convert data into a competitive advantage for future decision-making.
What is a data pipeline?
A data pipeline enables companies to consolidate data from multiple sources into one place to understand the full potential of the data. It is consists of three components- a source, transformation step or steps, and a destination.
- A data source might include databases such as PostgreSQL, MySQL, MongoDB; local files, such as JSON, CSV; cloud platforms such as Google sheets, REST API, or Salesforce and other external data sources.
- Data transformation can be done by performing tools. The raw data is extracted from the sources and quickly loaded into the data warehouse where transformation occurs.
- The Destination is a repository and final stage of a data pipeline in which data is stored once extracted.
You can achieve more with unified, standardised, and transformed data.
All about data pipeline architecture
We define data pipeline architecture as the arrangement of the objects that capture, transform, and routes source data to the targeted destination for obtaining accurate and valuable insights. We have broken down a data pipeline architecture into a series of parts, including:
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1. Sources
This is the first step that welcomes data from diverse data sources such as cloud, APIs, local files, Hadoop, and NoSQL to start the data flow journey.
2. Joins
In this part, the data that we have collected from disparate data sources get combined. The criteria and logic of how data is combined are specified in joins.
3. Standardisation
Data standardisation is a stage where all the data bring into a uniform format. For example, you have some data in kilometres and other data in meters; standardisation puts different variables on the same scale and standardises them into a common format.
4. Correction
Many times data comes with errors. For example, you have a customer dataset containing the fields that no longer exist, such as area codes or telephone numbers. In this correction phase, all the corrupt records will get removed or modified in a different process.
5. Loads
After the data is corrected or cleaned up, it is loaded into the appropriate analytical system, typically a data warehouse, Hadoop framework, or Relational database.
6. Automation
The automation process is used in a data pipeline either continuously or on a schedule. Error detection, status reports, and monitoring are all covered in the automation process.
Data pipeline architecture examples
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The following are the most critical data pipeline examples
Batch processing
Batch processing is commonly used when you have a massive amount of data, or a data source is a legacy system that cannot provide you data in streams. Batch processing is useful when you don't need real-time analytics, and it is more important to process enormous volumes of data than get faster analytical results.
Use cases for batch processing
- Payroll
- Transactions
- Customer orders
Stream processing
Stream processing is suitable if you want real-time analytics. It is a data pipeline architecture that handles millions of events at scale. Hence, you will be able to collect, analyse and store a lot of data. This functionality enables real-time applications, analytics, and reporting.
Use cases for stream processing
- Real-time fraud and anomalies detection
- Location data
- Customer/user activity
Getting started with a data pipeline
Before you try or build a data pipeline, it is imperative to understand your business objectives and what data sources and destinations you will be using. You will be glad after knowing that, setting up a data pipeline is not difficult or time-consuming. There are so many reputable data pipeline tools in the market that help you get the most out of your data flow seamlessly.
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