Data Engineering Concepts, Approaches, Data Pipeline, Data Warehouse

Data-centric insights and capabilities are the drivers of digital transformation and automation in any organization. But still, only 30% of organizations have a meticulous data strategy, and only 29% of businesses achieve full digital transformation with data engineering concepts.

Nonetheless, the data engineering discipline cannot be overlooked, thanks to its various business benefits. This guide highlights the basics of data engineering concepts, and approaches, as well as the flow process and nuances of data pipeline and data warehouse infrastructures. Keep reading to learn more.

What is Data Engineering Concepts? 

Data engineering basic concepts entail leveraging a set of manual and automated operations to build systems and protocols that support a seamless flow, as well as access to information in an organization. Businesses usually employ specialized talents known as data engineers to perform this duty.

Skills and Roles of a Data Engineer

You already know the meaning of what is data engineering. Let’s delve deeper into the skills and roles of a data engineer.

Data Engineering vs. Data Science

Although some people might unknowingly use data engineering and data science interchangeably, the two are different. Data engineering tech stack is a component of data science technology but often exists alongside the latter alongside modern business operations.

Here is a tabular representation of data engineering vs. data science

What is Data Engineering Process?

A typical data engineering process includes:

• Data Flow: This process enhances a standard data flow through a data pipeline to streamline data-driven models, such as ML models for real-time analysis.
• Data Normalization and Modeling: This process entails transforming data into easily accessible and usable formats to drive business decisions.
• Data Cleaning: Data cleaning elminiates incorrectly formated, incomplete, or corrupted data from a data set when merging multiple data sources.
• Data Accessibility: This includes enhancing the experience of data access, as well as visualization using custom tools, charts, and illustrations.

What are the key steps to data engineering?

Here are the key steps to data engineering:

1. Data Pipeline

As the name suggests, a data pipeline includes big data tools and protocols used to move data from one storage system to another one, usually for further storage or handling. Data pipeline technology combines data from multiple sources by capturing and transferring it into another tool, app, or database for more seamless access by collaborating teams.

A data pipeline is one of the fundamentals of data engineering concepts. It takes an experienced data engineer, well versed with programming skills and technologies to build a data pipeline that can power around-the-clock data exchange.

Other business applications of a data pipeline include:

• Data migration to the cloud and data warehouse
• Data integration from IoT systems or interconnected devices
• Data centralization to drive business decisions
• Data wrangling, especially in machine learning projects

ETL Data Pipeline Steps

ETL is the most prevalent and sought-after data pipeline architecture by businesses dealing with a custom-built enterprise data warehouse or data mart. A typical data architecture gives a detailed and exact description of components arrangement that enables real-time raw data extraction, processing, and information delivery.

That said, here are the steps of the ETL data pipeline:

Extracting Data

This is the first step of an ETL data pipeline, where raw data is retrieved from multiple incoming channels, such as social media and business websites. Data engineers usually program codes to run scheduled data extraction cycles for specific periods.

Transforming Data

This step involves modifying raw and inconsistent data extracted at the first stage of the ETL data pipeline. Data engineers usually transform the data and segregate it in different formats, sizes, or even colors for optimal querying and analysis. Typically, this stage ensures that the collected data is easily usable and discoverable.

Loading Data

Data extraction and transformation are followed by loading the usable data into various destinations, such as a data warehouse. Some data engineers prefer using Hadoop or a relational database management system (RDBMS).

A complete ETL data pipeline process can be followed by storage in another system, where key organization leaders can access the same for business intelligence analysis, report generation, and visual creations.

Data Pipeline Challenges

Data pipelines are beneficial to businesses in many ways, especially when it comes to real-time and predictive analytics. However, building a custom or standard data pipeline can be pretty daunting, especially for first-time organizations. Here are the five prevalent data pipeline challenges:

Data Placement

Businesses need to store their data in the right format and in the right location to enhance seamless access, as well as usability. Making the right decision can be challenging, given that businesses must use multiple tools and connect them to numerous data stores and formats, especially if they are going to harness the full power of big data.

Data Hosting

Data hosting can either be done in the cloud or on-premise. Choosing the right hosting service can be challenging, especially if you have to modify the data into a specific format. That’s why some organizations choose to self-host their servers, but this option also comes with operating system, latency requirements, as well as memory and disk challenges.

Data Scaling

Data scaling can be challenging, given that modern businesses handle up to 2.5 quintillion bytes of data generated by consumers every day. At the same time, the number of data sources, whether sensors or IoT devices, may increase unexpectedly. With this in mind, organizations should have data storage options that are automatically scalable.

However, data scaling issues are more challenging among organizations that use on-premise storage solutions. For instance, overwhelming data velocity and volume sharding and replication create more space for incoming data. In the long haul, these processes can prove costly in terms of operations because a single technical hitch can mean hours of troubleshooting the whole system.

Data Flexibility

A whole data pipeline system relies on the entire ETL (Extract-Transform-Load) process. Although this process is often meticulous, a single hitch in one step can cause hours of downtime, something that can affect data quality. The situation even gets trickier if a business deals with dynamic data sources and events, which might mean setting up schemas for real-time data analytics. At the same time, an ETL data pipeline that is used for data analytics must be optimally elastic for compatibility with various data types and schemas.

Data Migration

Data migration techniques depend on how an organization uses its data. However, most businesses choose to migrate their data during off-peak periods, such as at night, so as to minimize unnecessary downtime. Although this might sound convenient, it gets challenging when it comes to real-time analytics, as the migrated data will be from the previous day.

2.Data Warehouse Definition

A data warehouse is a central repository, usually, a relational database, modified and optimized to support data reading, aggregation, and querying. Although traditional data warehouses only supported structured data formatted in tables, modern applications can support both structured and unstructured data formats. Unstructured data in this case include information formatted and presented as images, PDF files, or even audio files.

Data warehouse concepts as a single point of truth and information in an organization. As opposed to retrieving data from multiple storage, data warehousing allows business analysts to report similar results and create near-accurate metrics for predictive analytics.

Data Warehouse Architecture

A typical data warehouse architecture includes three basic components:

Data Warehouse Storage

A central repository or a database is the bloodline of a custom or standard data warehouse architect, as all business data is stored where. Business leaders and other employees can then access the data warehouse storage to draw valuable insights from its contents. Businesses have the option of either an on-premise or cloud-based data warehouse storage.

The former option is ideal for organizations that want to process their data at high querying speeds and uncompromised security. On the other hand, cloud-based data warehouses support automatic scalability and any data structure. They are also relatively affordable than their on-premise counterparts.

Some data architects might also help you build collective storage options that run parallel as a centralized warehouse. This approach is usually ideal when enhancing scalability.

Metadata

Metadata contains the information and guidelines for changing and processing data when loading it into a warehouse environment.

Access Tools

These are tools that are integrated into the warehouse architecture to facilitate access, as well as interactions of the stored data with end-users. These tools might include querying, reporting, or data mining tools, based on the model of the data warehouse model.

Management Tools

Data warehouse tools help businesses perform automated administrative duties.

3.Data Marts

Data marts are smaller warehouses, usually employed by big organizations using enterprise-scale warehouses. Data marts come in handy when an organization wants to segment its data. For instance, marketing and sales data can be stored in different data marts to enhance easy access by the relevant departments.

Data marts exist in three prevalent types, including:

• Hybrid data marts
• Independent data marts
• Dependent data marts

4.OLAP and OLAP Cubes

OLAP is a simple abbreviation for Online Analytical Processing. This computational program allows business analysts and data engineers to take a multidimensional approach to data analysis. In other words, this tech helps organizations vast data from different angles, as opposed to OLTP.

OLAP cubes are the multidimensional structures that represent data. However, unlike traditional database representation (usually in rows and columns), which can be generated automatically, OLAP cubes must be custom-built for individual reporting and analytical querying.

5.Big Data Concepts

Big data engineering concepts are built around the four Vs, including volume, velocity, variety, and veracity. The architecture used in big data applications varies accordingly with the amount of data involved. Organizations that employ big data technology usually use a data lake to power the infrastructure, instead of a traditional data warehouse.

Prevalent big data concepts include:

• Data Lake
• Hadoop
• Enterprise Data Hub

6.Data Architecture as a Service

Data architecture-as-a-service is an approach taken by business data users to circumvent data bottlenecks by building local repositories. Typically, the process doesn’t sacrifice enterprise data integrity or consistency. This service can exist as an extension model or a self-service data engineering approach.

The Takeaway

In today’s data-driven world, we are all surrounded by data in every aspect of life. To keep up and stay ahead of the curve, it’s imperative that you are able to manage your data. Be sure to take advantage of the insights provided in this article in your business.

FAQs on Data Engineering Concepts