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? 

Basic concepts in data engineering entail leveraging manual and automated operations to build systems and protocols that support a seamless flow and access to information in an organization. Businesses usually employ specialized talents known as data engineers to perform this duty. A critical aspect of this process is data governance, which ensures data availability, usability, integrity, and security. 

Key concepts in data engineering include: 

• Data Cleansing: Removing inaccuracies and inconsistencies from data to improve its quality. 

• Data Modeling: Designing data structures representing business processes and supporting data analytics. 

• Data Pipeline: Automated workflows that move data from one system to another for processing and storage. 

• Data Security: Implementing measures to protect data from unauthorized access and breaches. 

• Data Warehouse: Centralized repositories for storing large volumes of structured data, optimized for querying and analysis. 

• Data Lake: A storage repository that holds vast amounts of raw data in its native format until needed. 

• Data Accessibility: Ensuring data is easily accessible to authorized users when needed. 

• Big Data: Handling and analyzing extremely large datasets that traditional data processing software cannot manage efficiently. 

• Cloud Engineering: Designing and managing scalable, reliable cloud-based data systems. 

• Machine Learning: Utilizing algorithms and statistical models enables systems to learn from data and make predictions or decisions. 

• Programming Languages: Languages like Python, SQL, and Java are used for building and managing data systems. 

What are the 5 Vs of Data Engineering? 

The 5 Vs of data engineering represent key dimensions that describe the challenges and opportunities associated with big data. These are: 

1. Volume: Refers to the vast amounts of data generated every second. Data engineers must design systems that can efficiently store and process these large datasets.
2. Velocity: Describes the speed at which data is generated, collected, and processed. Effective data engineering ensures that data flows swiftly through the system for real-time analytics and decision-making. 
3. Variety: Involves the different types of data sources and formats, such as structured, unstructured, and semi-structured data. Data engineers need to integrate these diverse data types into a cohesive system. 
4. Veracity: Pertains to the accuracy and reliability of data. Ensuring high data quality and integrity is crucial for making informed business decisions. 
5. Value: Represents the potential insights and benefits derived from data. The ultimate goal of data engineering processes is to extract meaningful and actionable insights that drive business value. 

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.

Skills and Roles of a Data Engineer 

You already know the meaning of what is data engineering, but what are the skills and roles of a data engineer? A data engineer is responsible for designing, building, and maintaining data pipelines that enable an organization’s flow and access to data. Key skills include proficiency in programming languages such as Python, SQL, and Java; knowledge of data warehousing solutions like AWS Redshift or Google BigQuery; and expertise in ETL (Extract, Transform, Load) processes. They also need strong problem-solving abilities and a deep understanding of data governance to ensure quality and compliance. 

What is a Data Pipeline? 

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

Also, a data pipeline is one of the fundamentals of data engineering basic concepts. An experienced data engineer, well-versed in programming skills and technologies, must build one that can power around-the-clock data exchange. 

Other business applications 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 

What is the ETL process in data engineering?

ETL is the most prevalent and sought-after data pipeline architecture by businesses with a custom-built enterprise data warehouse or data mart. A typical data architecture provides a detailed and exact description of the component’s arrangement, enabling 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, 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 and segregate the data in different formats, sizes, or colors for optimal querying and analysis. Typically, this stage ensures the collected data is easily usable and discoverable. 

Loading Data 

Data extraction and transformation are followed by loading the usable data into various destinations. 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 creation. 

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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 one can be daunting, especially for first-time organizations. Here are the five prevalent challenges: 

Data Placement 

Businesses need to store their data in the right format and in the right location to enhance seamless access and 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 harness big data’s full power. 

Data Hosting 

Data hosting can 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 systems, latency requirements, and 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 

The 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, which 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 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 migrate their data during off-peak periods, such as at night, 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. 

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 databases only support structured data formatted in tables, modern applications can support structured and unstructured data formats. Unstructured data, in this case, includes information formatted and presented as images, PDF files, or even audio files. 

These data banks act as a single point of truth and information in an organization. Unlike retrieving data from multiple storage, data warehousing allows business analysts to report similar results and create near-accurate metrics for predictive analytics. 

Understanding Data Warehouse Architecture 

A typical architecture includes three basic components: 

Data Warehouse Storage 

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

The former option is ideal for organizations that want to process data at high querying speeds and uncompromised security. On the other hand, cloud-based data banks 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 integrated into the warehouse architecture to facilitate access to and interactions of the stored data with end-users. Based on the model, these tools might include querying, reporting, or data mining tools. 

Management Tools 

Data warehouse tools help businesses perform automated administrative duties. 

Data Marts 

Data marts are smaller warehouses usually employed by big organizations using enterprise-scale warehouses. They 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 facilitate easy access by the relevant departments. 

Data marts exist in three prevalent types including: 

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

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, unlike OLTP. 

OLAP cubes are multidimensional structures that represent data. 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. 

Big Data Concepts 

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

Prevalent big data concepts include: 

• Data Lake 
• Hadoop 
• Enterprise Data Hub 

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Data Architecture as a Service 

Data architecture-as-a-service is an approach business data users take to circumvent data bottlenecks by building local repositories. Typically, the process doesn’t sacrifice enterprise data integrity or consistency. This service can be an extension data 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, you must be able to manage your data. Take advantage of the insights provided in this article in your business. 

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