How Data Teams Can Build Faster Pipelines in 2024

The escalating demands for data efficiency pose challenges for organizations and data engineering teams – particularly in managing costs associated with data provisioning. According to TDWI survey results, 50% of organizations report that project teams spend over 61% of their time on data integration, pipeline development, and preparation. 

Organizations also face mounting pressure to develop advanced analytics and AI initiatives while effectively collaborating with data scientists, analysts, and developers. On top of everything, most leaders and teams are simultaneously tasked with managing and reducing costs. There are lots of moving parts and, sometimes, no blueprint for how to do it correctly. 

This post covers the pressures and barriers of building fast pipelines while offering solutions so that data engineering teams can become more efficient and derive business value fast rather than get bogged down by operational tasks. 

The Challenges 

Disconnected and Data-Siloed Integration

There are often excessive tools and jobs required to track and manage an organization's data. At the same time, there can be significant barriers due to a lack of technical expertise for both low and no-code users that forces reliance on the more technical and experienced team members. Data lineage and documentation are often incomplete and inconsistent making it hard for different teams to collaborate or pass off particular tasks. 

Scalability and Orchestration 

Data pipelines play a central role in enabling organizations to achieve faster insights. The path to faster insights relies on robust data pipelines and seamless connectivity. Managing and locating all the pipelines, connectors, and ETL/ELT jobs is difficult and time-consuming. Fixing coding errors manually also eats into a data engineering team's workload. 

Data Governance and Security

Addressing data governance and security poses significant challenges for organizations. According to TDWI’s recent research, 29% find monitoring exposure risks very challenging, while 40% find it somewhat challenging. Monitoring exposure risks as data moves through pipelines and connectivity remains a pressing issue.

Identifying and Eliminating Unnecessary Workloads

Organizations struggle to identify and eliminate unnecessary workloads within their data pipelines and connectivity setups. Twenty-one percent find this challenge very demanding, with 48% rating it as somewhat challenging, highlighting the need for optimization and cost reduction efforts.

How to Address These Challenges 

Improving performance, scalability, and orchestration remains a key focus area for organizations, with 22% finding it very challenging and 42% rating it as somewhat challenging. Setting and following strategic priorities and leveraging emerging trends will help teams address these data engineering challenges. Here are some tips to help get you started: 

Centralize Data Integration Management 

Data lineage and documentation for data and AI governance is critical to successfully managing data and integration. When orchestrating jobs be sure to consider and manage any and all dependencies, and address all possible bottlenecks. 

Modernize Your Tech Stack 

While teams are still unearthing AI use cases, automating the process of putting data pipelines into production is a great place to start. But that is only really the start. The potential of generative AI for improving productivity is vast and changes daily. For more use cases and how Matillion is thinking about generative AI, read through all of Matillion’s most recent AI offerings

Optimize Productivity with DevOps and DataOps

DevOps and DataOps methodologies are pivotal in optimizing productivity and collaboration within data engineering. DevOps and DataOps leverage frameworks borrowed from software development lifecycles to enhance productivity in data engineering, including data pipeline development and connectivity: 

  • Adopting DevOps and DataOps Frameworks: DevOps and DataOps leverage frameworks borrowed from software development lifecycles to enhance productivity in data engineering, including data pipeline development and connectivity.
  • Importance of Data Observability: Data observability is critical for gaining end-to-end visibility across platforms and systems, reducing monitoring gaps, and understanding how issues impact business use cases.
  • Emphasizing Leadership and Cross-Functional Collaboration: Effective leadership fosters cross-functional collaboration, enabling teamwork and synergy among team members in executing data engineering tasks.


While the list of challenges to gaining insights from data is long, data management strategies and prioritizing cross-functional collaboration can address many of these issues. 

For more tips and details on this topic, be sure to listen to the full TDWI webinar: Drive Faster Insights from Your Data. To start implementing some of these practices with a data productivity tool such as Matillion, try Matillion out for free.  

Niamh Sedgwick
Niamh Sedgwick

Product Marketing Coordinator

Niamh Sedgwick is a Product Marketing Coordinator at Matillion. Niamh is responsible for meticulously planning, executing and evaluating the effectiveness of content marketing campaigns, whilst also serving as a content strategist and analyst. She ensures the team’s organization in Asana to optimize workflow efficiency.