Data engineering trends report: 8 key insights in 2024

The increasing data volumes, diverse data sources, and the growing need for real-time data pipelines and analytics keep on changing how the business world thinks of data. Organizations are seeking ways to gain a competitive edge through effective data utilization and real-time analysis.

To help you get a read on the modern approach toward data, Matillion licensed the 2024 Dresner Advisory Services Data Engineering Market Study, identifying the key trends shaping the data engineering ecosystem. Read on to go through each of those key current trends one by one. 

1. Rising importance of data engineering

The most significant major trend in 2024 is the growing recognition of data engineering’s crucial role in organizations. Key findings include:

• A remarkable 77% of respondents now consider data engineering as critical or very important, a substantial increase from 61% in 2022.
• Less than 2% view it as unimportant, highlighting the near-universal acknowledgment of data engineering's value.
• 85% of industry respondents, including those without a data engineering solution, rate data engineering as critically important.

The importance of data engineering varies across industries, with the following percentages considering it highly important or critical:

• Retail and wholesale: 100%
• Consumer services: 93%
• Financial services: 79%
• Education: 75%

Organization size also influences the perceived importance:

• Large enterprises (10,000+ employees): 92%
• Small businesses (1–100 employees): 75-76%

Geographically, North America leads in both current use and expansion plans, while EMEA slightly lags behind other regions.

Notably, organizations that rate their business intelligence (BI) initiatives as successful place a much higher priority on data engineering, suggesting a strong link between effective data engineering practices and BI success. This highlights the importance of data engineering in enabling data-driven decisions and optimizing decision-making processes.

2. Hybrid deployment models gain traction

Cloud adoption remains strong, with 92% of vendors supporting SaaS/cloud deployment. That said, hybrid deployment became increasingly important from 2023 to 2024. This shift shows in the 88% of vendors who say they're independent of cloud platforms and database options.

Different regions also have different preferences:

• EMEA leads in prioritizing hybrid deployment
• Asia Pacific and Latin America favor public cloud (SaaS) deployments
• North America prefers private clouds over hybrid setups

Industry preferences also vary. The retail and wholesale sectors, for example, lean more towards hybrid deployment than other options. 

Organization size plays a role here, too:

• Larger organizations prefer hybrid and private cloud deployments
• Smaller organizations (1-100 employees) opt for public cloud solutions

These findings highlight that deployment strategies must fit an organization’s needs, location, and size. For data professionals, understanding these trends is key to making informed decisions about their data infrastructure. 

3. Integration of data science and engineering

As data becomes more complex, we see a closer relationship between data science and data engineering. This trend is shaping how organizations approach their data strategies. 

Data science and R&D teams are leading the change. These groups rate the use of data engineering for data science and augmented analytics higher than other functions. This suggests that data scientists increasingly rely on data engineering practices to support their work. 

When it comes to how organizations are using their data engineering capabilities:

• Up to 32% of respondents say they use most (over 60%) of their data engineering capabilities for data integration, cleansing, and transformation workflows. These processes support data warehouses for dashboards and reporting.
• The second most common use case is data integration and transformation services for ad-hoc query, discovery, and exploration analysis. 

This tells us that while traditional data warehousing remains a priority, there’s a growing need for flexible data engineering solutions capable of supporting more exploratory and advanced analytics. 

Additionally, the integration of data science and engineering is crucial for extracting valuable insights from vast amounts of data.

4. Emphasis on data engineering usability

As data engineering becomes more crucial, there’s a growing push to make these tools more user-friendly—to open up data engineering to a broader range of professionals, not just specialists. 

When it comes to usability, four features stand out as the most important:

• Simple to build data workflows: Users want intuitive interfaces for creating and managing data pipelines.
• Scheduler for coordinating data workflows: This is rated as the most critical feature, highlighting the need for automated, timely data processing.
• Debugger for testing: As data workflows become more complex, the ability to troubleshoot effectively becomes crucial.
• Connectors to simplify data access: Easy integration with various data sources is key for streamlined operations.

These features are crucial to enable new professionals to create efficient engineering processes, perform real-time analysis, and automate repetitive tasks.

5. Adoption of advanced data targets

Relational databases continue to reign supreme across industries. They're like the trusty Swiss Army knife in a data engineer's toolkit—reliable, versatile, and widely understood. However, there's a new trend emerging, particularly in the tech industry.

The technology sector is pushing the envelope when it comes to advanced data targets:

• Graph databases are gaining traction as they’re ideal for handling interconnected data.
• NoSQL databases are seeing increased adoption for their flexibility of unstructured data.
• Hadoop ecosystems are being embraced for their big data capabilities.

Interestingly, Apache Kafka and Apache big data services, which were once the talk of the town, are now taking a backseat. These were rated as the least important among data engineering features. 

6. Continuous growth in adaptation

Data engineering has already established itself as a big player in many organizations. A substantial 66% of respondents say they’re currently using data engineering capabilities. 

But the story doesn’t end here:

• 22% of current users are planning to expand their use of data engineering tools.
• Another 19% of respondents are gearing up to adopt data engineering tools within the next 12 months.

In other words, organizations are recognizing the value of data engineering and doubling down on their investments.

7. Frequency of data engineering use

Data engineering isn’t a one-off task for organizations. Instead, more than half (56%) of respondents say they’re using data engineering constantly or frequently. This high frequency underscores just how integral data engineering has become to daily operations of many businesses.

That said, the frequency of data engineering use varies by industry. Consumer services are leading the pack in data engineering adoption. Manufacturing is not far behind, showing a higher-than-average use of data engineering. 

Organization size also has a say in the data engineering frequency. Larger organizations are more likely to be heavy users of data engineering and employ their data engineering capabilities more frequently than their smaller counterparts. 

All in all, this trend highlights the increasing importance of real-time analytics and real-time insights across various industries.

8. Third-party data enrichment

When it comes to third-party data enrichment, the data engineering world is split down the middle. While 35% constantly or frequently enrich their datasets with third-party data, 38% steer clear of it altogether. 

Besides that, different functions view third-party data differently. For instance, data science teams are the biggest fans of third-party data enrichment—they’re leading the charge in incorporating external data into their workflows. 

Staying competitive amidst these data engineering trends

The increasing importance of data engineering across industries, the shift towards hybrid deployment models, and the focus on usability all point to a field that’s becoming even more central to business operations.

For organizations, the message is clear: investing in data engineering isn’t just about keeping up—it’s about staying ahead. In a world where data is increasingly the differentiator between success and stagnation, having the right data engineering tools can give you a significant competitive advantage. 

If you're looking for a data solution that helps you stay ahead of the curve, adapting to these trends and preparing you for what's next, Matillion offers the perfect solution in the form of Data Productivity Cloud. Start a free trial or schedule a demo today to learn more!