- Link: blogstudiio.com
- Author: emilyjoe
- Publication date: June 15, 2023
Data-centric potentials and insights drive digital transformation and automation in any company. But, only 30% of the companies have diligently followed data strategy and only 29% of organizations will achieve complete transformation with data engineering skills. Still, the data engineering discipline can’t neglect due to its immense benefits. Big data engineers play a significant role in this process as they are the backbone of modern data-driven business.
In this blog, we explore the fundamentals of data engineering and approach data pipeline and warehouse in detail.
Concepts of Data Engineering
Data engineering fundamentals entail leveraging a set of manual and automatic tasks, which helps create systems and protocols that support smooth flow and access to varied data. Generally, organizations employ specialized talents called data engineers to conduct this duty.
What is Data Warehouse?
A Data Warehouse is a central repository that stores data in queryable formats. From a technical perspective, a data warehouse is a relational database optimized for reading, aggregating, and querying huge volumes of data. DWs have traditionally only contained data that could be arranged into tables or structured data. Modern DWs can support unstructured information (such as images and pdf files).
However, a data warehouse structure includes four basic components:
· Data Warehouse Storage
Data warehouse architecture relies on a database to store all enterprise data, allowing users to gain valuable insights. Data architects decide on the best DW for their business based on how they can benefit. The cloud is cheaper, more flexible, and doesn’t have a set structure. However, the querying speed of on-prem systems is superior.
The first option is perfect for organizations who want to process data with high querying speed and without compromising security. Cloud-based data warehouses, on the other hand, support any data structure and automatic scaling. These cloud-based data warehouses are relatively more affordable than on-premise alternatives. Data architects can also assist you in building collective storage options which run parallel to a central warehouse. This is a good approach to use when you want to increase scalability.
· Metadata
Metadata is a set of guidelines and information that can be used to change and process data before it’s loaded into a warehouse.
· Access Tool
The tools are built into the warehouse architecture and are designed to make it easier for users to access the data. These tools may include data mining, data reporting or querying tools, depending on the model for the data warehouse.
· Management Tool
The use of data warehouses by businesses allows them to automate administrative tasks.
Data Pipeline
A data pipeline is a collection of tools for big data and protocols that transfer data from one system to another. This data will be stored or handled in the new system. Data pipeline technology allows teams to access data more easily by combining it from different sources.
Data pipelines are fundamental concepts in data engineering. A data engineering professional with a good understanding of programming and technology must create a data pipe to power data exchange around the clock.
· Data pipelines can be used for a variety of other business purposes, including:
· Data migration from the cloud to the data warehouse
· Data integration for IoT or connected devices
· Data centralization drives business decisions
· Data wrangling is especially important in machine learning projects
ETL Data Pipeline Steps
ETL is the data pipeline architecture most commonly used by companies with a custom-built data warehouse. A typical data structure describes the components that enable real-time data extraction, processing and information delivery.
Here are the steps in the ETL data pipeline.
· Extracting Data
The first step in an ETL pipeline is to retrieve raw data from various sources, including social media and corporate websites. Data engineers program codes that run data extraction cycles scheduled for specific times.
· Transforming Data
This step involves modifying raw and inconsistent data extracted during the first stage in the ETL pipeline. Data engineers transform and separate the data into different formats, sizes or colours to optimize querying and analyses. This stage is usually used to ensure that the data collected can be easily accessed and discovered.
· Loading Data
The data is then loaded into various destinations, such as a warehouse. Some data engineers use Hadoop or relational database management systems (RDBMS). The ETL process can be completed by storing the data in a different system. Business leaders and other key stakeholders can use it for analysis, report creation, and visual creation.
Conclusion
A data engineer is responsible for designing, implementing, and maintaining the systems that store, process, or analyze data. As data engineering is still a new field, it is not possible to apply a one-size fits all approach. Data engineers must stay on top of the latest technologies and trends to apply them in the ever-growing ecosystem. You’ll discover much more to learn as you learn about the data ecosystem and how data engineers fit into it. This should provide a solid foundation for you to build on.