Data is always a critical component for any application. It is used to design an efficient and robust pipeline for the delivery and management of business information. In such a scenario, data engineering holds a core value in building data pipelines to gather massive amounts of data. To make informed decisions and derive insights, companies should seek reliable tactics to store, process, analyze, and manage their data.
As the technologies like data engineering and machine learning are ramping up, building scalable data pipelines is becoming much easier. In this blog, you will dive deep into the fundamentals of data engineering and why it is important for creating robust and scalable data pipelines.
What Exactly is Data Engineering?
Data engineering is the technical process to plan, construct and manage the infrastructure as well as the systems of the organization for storing, analyzing, and managing a massive amount of data. The purpose of data engineering is to make the business data accessible for transforming the raw data into clear and actionable insights that boost the business’s credibility.
When it comes to making data-based decisions for the business, it is not possible to implement a solid strategy without data engineering. Data pipelines, distributed computing systems, and data storage & retrieval systems are a few components that are developed to organize big data.
What is a Data Pipeline?
A data pipeline is a series of operations that involves moving data containing business information from one system to another. In data engineering, data pipelines are considered the crucial step to gather, process and integrate all the data from diverse resources. The key elements of the data pipeline are the source, a series of operations, and the destination. Here, the series of operations refers to the gathering, processing, transformation, verification, loading, and storing of data. Every component in the data pipeline relies on the specific needs of the user.
Key Considerations for Building Scalable and Robust Data Pipelines
- Gather and Store the Data
Initially, you need to gather the data from the source such as databases, human data input, or API (application programming interfaces), and store it. Once the data is collected, it is stored in the repository to get quick access through a pipeline. You can save the data in the flat files, data lakes, and data warehouses.
- Fetch and Process the Data:
After the data has been collected and stored in one place, it can be fetched and processed before making it accessible for building the pipeline. It entails data cleaning, filtering, merging, and adding from various sources. Typically, data can be processed through 2 different practices-
- Stream Processing
- Batch Processing
- Load the Data:
Data is loaded in the pipeline in a suitable format. It is loaded in the distributed computing framework or memory cache. It is the essential process after data extraction. The data collected must be easy to access to make a clear analysis.
- Plan for the Data Pipeline Architecture:
Now, the next step is to design an architecture for the data pipeline before proceeding to the development phase. The architecture consists of the source, processing engine, and scheduler which determine how the information will be handled and moved into the pipeline.
- Data Pipeline Development:
Now, the data engineering company develop a data pipeline after choosing the relevant design. It involves setting the data processing logic and integrating and implementing the pipeline components. At this step, pipeline testing is also conducted to ensure the proper flow.
- Measuring and Improving the Performance:
Now, it is required to monitor and improve the performance of that pipeline. If any slowdowns or errors are detected, they must be fixed while modifying the data processing algorithm or replacing the hardware. Constant monitoring is needed to ensure the peak performance of the pipeline.
To Wrap Up
In today’s technology-driven business world, knowing the fundamentals of data engineering and building data pipelines have become indispensable parts of making informed decisions. As the data is growing at a rapid rate, it must be handled by proven & efficient tactics.
