Data engineering challenges 2024: Insights into benefits and solutions

June 7, 2024

Principal Data Architect

Data & AI Evangelist

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In today’s digital world, businesses are inundated with vast amounts of data. This raw data must be cleansed and transformed to support data-driven decisions, making the role of data engineering more crucial than ever.

Data engineers are responsible for ensuring that data is not only accessible but also meaningful, secure, and ready to drive innovation. They play a vital role in building and maintaining the infrastructure required to collect, store, and analyze data efficiently.

In fact, due to data engineers’ role in handling massive amounts of data, organizing it, and making it ready for analysis, the demand for big data engineers has soared up to 45%. Despite its importance, certain data engineering challenges stand between businesses and their profitable outcomes. These challenges substantially impact the business’s ability to leverage data for strategic insights.

In this blog post, we will walk you through the common data engineering challenges and solutions for data engineering problems, guiding you on how to effectively analyze large datasets to extract valuable insights for informed business decision-making.

What is data engineering?

Data engineering is designing, building, and maintaining the data processing flows that allow data to be extracted, stored, curated, and analyzed efficiently. It involves setting up pipelines and workflows that transport data from various sources to a central repository, where it can be cleaned, transformed, and made accessible for analysis. This robust infrastructure, created by principal data engineers, is a foundation for any data-centric organization.

Key data engineer responsibilities

Data collection and ingestion: Creating pipelines to collect data from various sources, such as databases, APIs, and real-time streaming systems.
Data storage and organization: Designing and implementing storage solutions like data warehouses, data lakes, and data Lakehouses to organize data efficiently.
Data transformation and cleaning: Ensuring that raw data is integrated, cleansed, transformed, and formatted for analysis. This process often involves removing duplicates, handling missing values, augmenting, and converting data into usable formats.
Data pipeline development: Building automated workflows and pipelines to move data between systems to ensure its processing and availability for analysis in a timely manner.
Scalability and performance: Ensuring that data infrastructure can scale with increasing data volumes and maintain high performance.
Integration and interoperability: Integrating data systems with existing technologies and ensuring seamless data flow across different platforms and tools.
Data security and compliance: Implementing security measures to protect data and ensure compliance with relevant regulations and standards.

Importance of Data engineering in driving data-driven decisions

Data engineering is the backbone of modern data-driven organizations, providing the tools and infrastructure necessary to harness the power of data for strategic advantage. Data engineers play an important role in ensuring data infrastructure operates reliably, scales effectively, and performs optimally. Their efforts are indispensable to any organization that relies on data, ensuring data is reliable, accessible, and easy to use. To ensure data integrity, data engineers design and manage data pipelines and storage systems to allow organizations to leverage most of their data for strategic decision-making and operational efficiency.

Therefore, as organizations increasingly leverage data for success, they’re recognizing the transformative power of data engineering solutions. These solutions go beyond technical details, their true value lies in the tangible impact they have on various aspects of a business, helping them to make the most out of their data to drive improved business outcomes.

However, for businesses uncertain about the impact of data engineering solutions, we have highlighted scores of benefits data engineering brings to your organization.

Make trustworthy decisions with reliable data

Data engineering ensures the reliability of data used within an organization. Raw data collected through disparate sources is inherently inconsistent and error prone. In such instances, data engineers implement data reliability engineering procedures to cleanse and validate this data to eliminate inaccuracies and ensure the trustworthiness of the data.

Data democracy empowers everyone

Data engineering fosters the accessibility of data throughout an organization. Data engineers create data pipelines and build scalable data solutions that store massive amounts of data. These solutions ensure its availability for data analysts, data scientists, and other stakeholders, allowing them to leverage the power of data, analytics, and AI for generation of insights.

Gain insights at lightning speed

Moreover, data engineering enhances data usability by transforming raw data into formats optimized for analysis. This usability becomes imperative for organizations to derive meaningful insights that drive business innovation and competitive advantage. Data engineers empower professionals to extract meaningful patterns and trends by ensuring data usability, propelling the organization toward data-driven success.

Overcoming data engineering challenges for business success

With data analytics engineering as the foundation of data-driven organization, it is important to recognize and address the challenges that come with it. Therefore, overcoming these data engineering challenges is necessary to ensure that data is reliable, accessible, and useful – the qualities that make data valuable.

Let’s explore some of these challenges that consistently get in the way of data engineers and identify best practices to overcome them that empower them to thrive in the face of change.

Challenge 1: The deluge of data

The first stage of data engineering, data ingestion, involves acquiring data from different sources with varying formats and structures into a data layer where it can be integrated, further processed, and analyzed. As businesses collect data from a multitude of sources, the volume and velocity of data becomes overwhelming. This real-time ingestion presents challenges in data engineering as it demands high processing.

Therefore, to address this potential challenge, principal data engineers must develop efficient data ingestion systems. These systems should handle the volume and speed of data while maintaining high performance and reliability.

Challenge 2: Breaking down silos

Data silos across the organization keep multiplying, which hinders its potential use for comprehensive analysis, decision-making, and effective collaboration. Different data sources often have varying formats, structures, and quality levels, complicating the integration process. To minimize this, data silos need to be broken down and data needs to be maintained as a single source of truth. To achieve seamless data integration, ETL (Extract, Transform, and Load) tools to normalize data and ensure its consistency.

To overcome these complexities, data engineers must navigate these challenges to provide a comprehensive, accurate, and real-time view of the organization’s data landscape.

Challenge 3: Managing scalable data storage

Businesses dealing with large volumes of data daily must manage scalable data solutions. The ever-growing mountain of data poses a major hurdle for big data engineers. The International Data Corporation (IDC) forecasts a CAGR of 26.3% through 2024, signifying an exponential rise in data volume. This necessitates the development of scalable and performance-optimized data engineering processes to handle the coming deluge.

To address this challenge, organizations must transition from traditional solutions to cloud-based solutions that are scalable, cost-effective, and optimized for storage and retrieval, ensuring that data remains accessible and manageable as the organization grows.

Challenge 4: Optimization of data processing workflow

One of the biggest challenges in data engineering is real-time data processing. Raw data holds immense value, so it must be processed and transformed before it can be truly useful. To overcome this, data engineers design and implement pipelines that clean, transform, and prepare data for analysis.

This processing requires careful optimization to ensure timely access to insights without incurring excessive costs. Data engineers fine-tune these pipelines and adopt stream processing technologies to maximize efficiency. Data engineering makes it possible to process data in real-time or near real-time, allowing companies to make prompt, well-informed decisions in response to the growing demand for real-time insights.

Challenge 5: Maintaining order and trust

Maintaining data quality presents another big challenge to data engineers with excessive amounts of data to work with. Gartner predicts that over 40% of businesses lag in achieving their long-term goals due to poor data quality. However, as data volume and complexity increase, implementing data governance strategies becomes essential to handling data quality concerns.

Data governance establishes policies and procedures for data management to ensure its accuracy, security, and accessibility. Data engineers implement these frameworks, safeguarding the integrity of the data and fostering trust in its use across the organization.

The future of data engineering: A look toward 2024 and beyond…

As we look towards 2024 and beyond, data engineering services are evolving rapidly, with new tools and techniques emerging to address the ever-growing data landscape. The future of data engineering is being shaped by the integration of AI and ML, the adoption of cloud computing, and the rise of data mesh architectures. These trends drive significant changes in how data is managed, processed, and used, offering new opportunities for innovation and efficiency to the organizations.

As data volumes continue to grow and the demand for real-time insights increases, data engineers will need to stay at the forefront of these trends. They must continuously evolve their skills and approaches to meet the challenges of a data-driven world.

Master data engineering challenges with Confiz’s expertise

Understanding the fundamentals of data engineering has become imperative for addressing data engineering challenges effectively. However, to overcome these challenges, organizations can benefit from a partner that specializes in data engineering consultancy to build and maintain a resilient data infrastructure.

Ready to transform your data into a strategic asset and overcome the challenges of data engineering? Choose Confiz for data management and analytics services and hire data engineers from our exceptional talent pool. Reach out to us now at marketing@confiz.com to meet your staff augmentation needs.