The concept of data engineering has transformed dramatically with the advent of big data technologies. Historically, the focus was on managing small-scale databases and servers, but today, data engineering encompasses a broad spectrum of disciplines and technologies dedicated to handling vast arrays of structured and unstructured data. 

An overwhelming 2.5 quintillion bytes of data are generated each day globally on the internet. For businesses that can get just a scoop of that—and for those who know what to do with it, the possibilities are endless. 

For tech leaders, modern data engineering provides the tools and methodologies necessary to harness the power of your data effectively. So, in this guide, we’ll look at what this means, why you should care, and how you can implement best practices to make the most of your data.

What is data engineering?

Data engineering is a discipline focused on designing, building, and maintaining the systems that collect, manage, and analyze data. It plays an important role when it comes to big data, allowing businesses to harness large volumes of data effectively and efficiently. 

Modern data engineering supports traditional data storage solutions, plus it also integrates cloud computing, real-time data processing, and automated data pipelines to cater to dynamic business needs.

Why your organization should care

According to a recent report by Harvard, companies that leverage their data report a 64% improvement in operational efficiency and productivity, and more than half see a direct improvement in financial performance.

Data engineering provides the necessary infrastructure and processes for collecting, storing, transforming, and making this data accessible. With robust data engineering practices in place, companies can ensure the reliability, scalability, and security of their data systems so they can focus on the analytics and decision-making processes that drive business growth and operational efficiency. 

How big data fits into all of this

Big data is more than just a large volume of information; it's a vital asset that drives strategic decisions and operational improvements across various sectors. Organizations that harness the power of big data can gain a competitive edge, adapt quickly to market changes, and enhance operational efficiencies.

One of the primary reasons for organizations to invest in big data is its potential to improve decision-making. With big data analytics, companies can process vast amounts of information to uncover hidden patterns, correlations, and insights that would otherwise be inaccessible. 

For optimizing operations, predicting market trends, developing targeted marketing campaigns, and improving customer service—you need massive amounts of data. But it's not just about having vast amounts of data. You need to have the ability to turn this data into actionable insights that can lead to smarter business moves and more profound, more strategic plans. In other words, you need data engineering.

Data engineering process

The data engineering process is critical for transforming raw data into a structured format that's ready for analysis. It typically involves several key stages starting with data collection, where engineers gather data from various sources. Following collection, the data undergoes cleansing to ensure its accuracy and relevancy. 

The next step is transformation, where data is converted into a format suitable for analysis. The data is then processed and stored in systems such as databases or data lakes, ready for use by data scientists and analysts.

The final stage involves maintaining data quality and integrity, which is crucial for ensuring the data remains reliable and useful over time. Monitoring systems are set up to continuously check and uphold the quality of the data, allowing for corrections and updates as needed. 

Many professionals often refer to the sum of this process as the “data pipeline,” which we’ll look at next.

Data engineering pipeline

Let’s take a look at each component in the pipeline, following the data through every step it takes.

1. Data Ingestion – This is the initial phase where data is collected from various sources. The data can be ingested in real-time or in batches depending on the requirements. Technologies like Apache Kafka or batch data ingestion tools such as Fivetran or Airbyte are commonly used.
2. Data Storage – After ingestion, the data is stored in a data storage system, which could be a database, data warehouse, or a data lake, depending on the nature and use of the data. Examples include systems like Amazon S3, Google BigQuery, or Snowflake.
3. Data Processing – This component involves transforming the ingested data into a format suitable for analysis. This may include cleaning, aggregating, and transforming data using tools like Apache Spark or by employing ETL (Extract, Transform, Load) processes.
4. Data Analysis – In this phase, the processed data is analyzed to extract insights. This could involve complex queries, machine learning models, or statistical analyses to derive meaningful information from the data​
5. Data Visualization and Reporting – The insights garnered from the analysis phase are then visualized and reported for business decision-making. Tools like Tableau or PowerBI are often used to create interactive dashboards and reports that make it easier to understand the data​.
6. Data Monitoring and Management – Throughout the pipeline, it is crucial to monitor the data flow and manage the data quality and integrity. This includes tracking data lineage, performing data quality checks, and ensuring data security and compliance.
7. Orchestration – Finally, orchestration tools are used to manage the workflow of the data pipeline to ensure that all processes are executed in a coherent and timely manner. Popular tools for this purpose include Apache Airflow and Dagster.

Data engineering tools

To make this complex data lifecycle function, from ingestion and storage to processing and analysis, you need a lengthy list of tools. Not only to streamline operations but also to enhance the accuracy, efficiency, and reliability of your data systems.

For Workflow Orchestration

Apache Airflow is renowned for its capability to automate and orchestrate complex workflows, ensuring that data tasks are executed in the correct sequence. It's highly scalable and user-friendly, though it does have a steep learning curve and requires ongoing maintenance.​

Prefect and Luigi are other popular tools for workflow management. Prefect offers a hybrid execution model combining cloud management with local execution, while Luigi, developed by Spotify, excels in managing long-running batch jobs.

For Data Storage and Warehousing:

Snowflake provides a cloud-based data warehousing solution that separates storage and compute, allowing each to scale independently, thus optimizing performance and cost​.

Amazon Redshift and Google BigQuery are other key players in this space, offering massive parallel processing and serverless data warehousing capabilities.

For Real-Time and Batch Processing:

Apache Spark is a powerful engine for both batch processing and real-time data processing, widely used for its fast processing capabilities and support for multiple programming languages​.

Apache Flink and Apache Storm provide robust solutions for stream processing, handling massive data streams with low latency and ensuring reliable data processing.

Data Integration and Transformation:

DBT specializes in transforming data within your data warehouse, utilizing SQL code to automate and version control data transformations, ensuring data consistency and quality​.

Airbyte offers a low-code/no-code solution for data integration, easing the data pipeline construction with pre-built connectors and a user-friendly interface.

Analytics Monitoring and Security

Databricks combines data engineering with advanced analytics capabilities, facilitating collaborative workspaces for data scientists and engineers to streamline machine learning lifecycle management​.

Prometheus is a monitoring tool that excels at collecting and visualizing metrics from various data sources, crucial for ensuring the health and performance of data pipelines​.

Elasticsearch serves as both a search and analytics engine, known for its fast data retrieval capabilities and flexibility in handling different types of data.

How to implement a data engineering strategy

Implementing a robust data engineering strategy involves several key practices to ensure the system is scalable, efficient, and secure. Here are the steps to consider:

Understand and Assess Your Data Sources

Before processing, you’ll need to understand the source of the data to assess its quality and prepare for any complexities in cleaning and transformation. Analyzing the source helps in detecting anomalies and understanding the data relationships, which are essential for designing scalable solutions.

Selecting and Implementing the Right ETL Tools

Efficient data pipelines are built using robust ETL tools that match your organization's performance, scalability, and cost needs. It's important to choose tools that integrate well with your existing systems and can handle the anticipated data loads.

Automate Data Workflows

To increase efficiency and reduce errors, automating data pipelines is critical. Use workflow orchestration tools like Apache Airflow or Prefect to manage complex data workflows. Automation ensures consistency across data handling and allows scaling as data volumes increase.

Data Storage Solutions 

Choosing the right data storage solution—whether cloud-based like Amazon Redshift and Google BigQuery or on-premises—depends on your organization's size, data complexity, and specific needs. Cloud solutions offer flexibility and scalability, while on-premises storage might be preferred for enhanced control over data security.

Data Processing and Transformation

Employ ETL or ELT processes to ensure the data is in the right format for analysis. The choice between ETL and ELT depends on the computational resources of the destination system and the specific needs of your data workflow.

Ensuring Data Security and Compliance 

Implement strong encryption protocols, conduct regular security audits, and establish strict access controls to protect data and comply with regulations like GDPR or HIPAA.

Monitoring and Optimization

Continuously monitor data workflows for any potential inefficiencies or bottlenecks. Utilize tools for performance monitoring to refine and optimize the data processes, ensuring they remain efficient and aligned with business needs.

Scalability of Data Solutions

Ensure that your data architecture can scale with your organization. This might involve using cloud-based services for their scalability or adopting technologies like distributed computing frameworks for processing large datasets efficiently.

Data Quality Management

Maintain high data quality through regular cleansing, validation, and consistency checks. High data quality is crucial for accurate analytics and decision-making​.

Collaboration and Version Control

Foster a collaborative environment using data version control tools that allow team members to work on data projects without affecting each other's work. This supports CI/CD practices by ensuring that any data version that fails a quality check is not moved to production until fixed.

Common challenges and how to avoid them

Implementing a data engineering strategy comes with several common challenges, but understanding these can help in devising effective strategies to mitigate them.

Data Security and Compliance

One of the biggest challenges in data engineering is ensuring data security and compliance with evolving regulations. As organizations expand, managing secure access to data without slowing down operations becomes crucial. Implementing robust security practices such as centralizing access control and utilizing data lakes for streamlined access management can help mitigate security risks.

Also keep in mind, that as data volumes grow and migrate to the cloud, making sure that sensitive data is protected becomes more complex, requiring scalable data access policies that align with security initiatives—which brings us to the next challenge.

Scalability and Cost Management

As data volume increases, maintaining the scalability of data systems without escalating costs is a significant challenge. Organizations need to leverage cost-effective solutions like serverless computing and optimize cloud service usage regularly​. Effective management of data storage costs also plays a crucial role, requiring strategies that differentiate between hot, warm, and cold data to optimize access and storage costs.

Data Silos and Integration

Breaking down data silos is essential for providing a unified view of information across an organization. This requires the integration of various data sources and systems, often necessitating custom ETL pipelines, which can be complex and difficult to maintain, especially as the number of data sources increases.

Managing Data Quality

Ensuring high data quality remains a persistent challenge in data engineering. Poor data quality can lead to inaccurate analyses and business decisions. Addressing this involves implementing rigorous data validation rules and comprehensive governance policies to maintain data integrity, completeness, and consistency.

Human and Resource Constraints

As with anything, there is always a human factor that comes into play. The availability of skilled personnel and effective technology is crucial for managing and securing large volumes of data. However, many organizations face resource constraints that can lead to burnout among data engineering teams, affecting productivity and the ability to secure and manage data effectively.

FAQs

What do data engineers do?

Data engineers are responsible for designing, building, and maintaining the architecture used for data generation, collection, and analysis. They develop large-scale processing systems that manage, transform, and transport data into usable formats for analysis by data scientists and business analysts. Their role is crucial in ensuring that data flows efficiently through pipelines, and is accessible and secure.

What is dbt data engineering?

DBT, (data build tool) is a command-line tool that enables data engineers and analysts to transform data in their warehouses more effectively. It allows users to write modular SQL queries, which it then runs on top of the data warehouse in the order specified by the user, ensuring dependencies are respected. dbt helps in streamlining and versioning transformations, providing a solid framework for any data modeling you wish to do.

What is Apache Airflow in data engineering?

Apache Airflow is an open-source tool used by data engineers to manage complex workflows and data processing pipelines. Airflow allows engineers to programmatically author, schedule, and monitor workflows as Directed Acyclic Graphs (DAGs), where each node in the graph represents a task. It is highly versatile and can integrate with cloud services and other data technologies to orchestrate complex, multi-stage data processing pipelines.

What is the difference between data science and data engineering?

While data science focuses on extracting insights and making predictions using data, data engineering provides the necessary infrastructure and tools for data collection, storage, and preliminary processing. Data engineers build and maintain the systems that allow data scientists to perform analyses efficiently. Essentially, data engineers prepare and optimize data systems for analysis, which data scientists then use to create value through insights and models​.

Big Takeaway

Data engineering has evolved from simple database management to a cornerstone of modern business technology, addressing the challenges presented by massive volumes of diverse data. 

Effective data engineering is not just about managing data but also transforming that data into a strategic asset. Leveraging modern tools and practices can enhance decision-making, optimize operations, and maintain a competitive advantage for your business. In a world where data's value is ever-increasing, the ability to efficiently process, store, and analyze data is indispensable.

For tech leaders looking to stay informed and adaptable, we invite you to contact us for a consultation. We’ll explore how you can go from “just keeping up” with the pace of change, to driving it.