What is data platform engineering?

In today’s data-driven world, companies require robust and scalable infrastructure to manage and process vast amounts of data. Data platform engineering, a specialized subset ofplatform engineering, comes into play here.

Data platform engineering focuses on designing, building, and maintaining the infrastructure and tools that enable efficient data processing, storage, and analysis. As a critical component of platform engineering, it ensures that data is accessible, reliable, and usable for various business needs.

In this article, we explore data platform engineering and how engineering intelligence tools can streamline this critical part of a modern tech organization.

What a data platform engineer does

A data platform engineer’s role includes a wide range of responsibilities. Here are some of the key tasks they carry out:

Designing data architecture

Data platform engineers design the architecture of data platforms. Their responsibilities include selecting the appropriate technologies and tools, defining schemas, and establishing data governance practices. They ensure the architecture is scalable, secure, and efficient, enabling seamless data flow across the company.

Building and maintaining ETL pipelines

One of the primary tasks of data platform engineers is to build and maintain ETL pipelines. These pipelines extract data from various sources, transform it into a schema or usable format, and load it into database storage systems like Cloud Data Warehouses. Engineers must ensure that these pipelines are reliable, efficient, and capable of handling large volumes of data, avoiding potential data loss.

Implementing data security and compliance

Data security is paramount in any company. Data platform engineers implement security policies to protect sensitive information from unauthorized access and breaches. They also ensure compliance with data privacy regulations, such as GDPR and CCPA, by implementing appropriate data handling, testing, and storage practices.

Optimizing data storage and retrieval

Efficient data storage and retrieval are crucial for performance and cost-effectiveness. Data platform engineers optimize storage solutions to ensure quick access to data while minimizing storage costs. Their work involves selecting the right storage technologies, such as Snowflake for storage or Amazon Web Services, and implementing business logic indexing and partitioning strategies.

Key differences between data engineering and platform engineering

Data platform engineering, data engineering, and platform engineering are closely related but distinct disciplines. Understanding the differences between them is essential for grasping the unique role of modern data platform engineers.

Data engineering vs. data platform engineering

Data engineering focuses on the development and management of data pipelines and workflows. Data engineers are responsible for extracting, transforming, and loading (ETL) data from various sources into data storage systems. They work on ensuring data quality, consistency, and reliability.

Data platform engineering, on the other hand, encompasses a broader scope. While it includes data engineering tasks, it also involves designing and managing the entire data platform. Responsibilities include selecting and integrating various tools and technologies, implementing security measures, and optimizing data storage and retrieval. Data platform engineers have a more holistic view of the data ecosystem, ensuring all components work seamlessly together.

Platform engineering vs. data platform engineering

Platform engineering involves designing and maintaining the underlying infrastructure and tools that support software development and deployment. Platform teams focus on building and managing platforms, such as Kubernetes clusters or CI/CD pipelines, to enable efficient software delivery.

Data platform engineering is a specialized subset of platform engineering. It focuses on the infrastructure and tools required for data processing and analysis. Data platform engineers ensure that data platforms are scalable, reliable, and secure, enabling efficient data workflows and analytics.

How this fits into a platform engineering organization

Data platform engineering enables data-driven decision-making and analytics in a platform engineering organization. Here’s how it fits into the larger organizational structure:

Collaboration with data teams

Data platform engineers work closely with data scientists, analytics engineers, and DataOps Engineers. They provide data exploration, analysis, and modeling infrastructure and tools. By ensuring that data objects are easily accessible and reliable, they enable data teams to focus on generating actionable insights and driving business value.

Integration with software engineering teams

Data platform engineers collaborate with software engineering teams to integrate data platforms with other operational systems and applications. Their responsibilities include building APIs and data connectors ensuring seamless data flow between different cross-functional teams. By enabling smooth data integration, they support the development of data-driven applications and digital services.

Supporting business intelligence and analytics

Business intelligence (BI) and analytics are crucial for informed decision-making. Data platform engineers provide the infrastructure and tools for BI and analytics platforms, such as Tableau or Power BI. They ensure that data is readily available for analysis, enabling stakeholders to make data-driven decisions and gain valuable insights.

How engineering intelligence and developer experience can improve data platform engineering

Engineering intelligence and developer experience are essential for enhancing the effectiveness of data platform engineering. Here’s how they contribute to improving this critical function:

Leveraging engineering intelligence

Engineering intelligence uses data and analytics to optimize engineering processes and workflows. In data platform engineering, engineers can apply this in several ways:

• Performance monitoring and optimization: By continuously monitoring the performance of data pipelines and storage systems, engineers can identify bottlenecks and optimize processes for better efficiency and speed.
• Predictive maintenance: Using predictive analytics, engineers can anticipate and address potential issues before they become critical, ensuring the reliability and availability of data platforms.
• Capacity planning: Engineering intelligence enables better capacity planning by analyzing usage patterns and predicting future demand. Such analysis helps in scaling data infrastructure proactively, avoiding performance degradation.

Enhancing developer experience

A positive developer experience is crucial for the productivity and satisfaction of engineering teams. In data platform engineering, this involves:

• Providing robust tools and frameworks: Data platform engineers should offer reliable and user-friendly data processing, storage, and analysis tools. Their responsibilities include developing internal frameworks and libraries that simplify everyday tasks and reduce development time.
• Streamlining workflows: By automating repetitive tasks and seamlessly integrating tools, engineers can streamline workflows and reduce developers’ cognitive load. This enables them to focus on more strategic and value-added activities.
• Fostering collaboration: Collaboration between data platform engineers and other business teams is essential. By establishing clear communication channels and promoting a collaborative culture, organizations can ensure that all stakeholders are aligned and working towards common goals.

Best practices for data platform engineering

Implementing best practices is critical to ensuring the success of data platform engineering efforts. Here are some recommended practices:

Adopting a modular architecture

A modular architecture allows for flexibility and scalability as a company size grows. Data platform engineers should design systems with loosely coupled components that can be independently developed, deployed, and scaled. Such design enables more manageable maintenance and upgrades and better fault isolation.

Ensuring data quality

Data quality is critical for reliable analysis and decision-making. Engineers should implement robust data validation and cleansing processes to ensure data is accurate, complete, and consistent. These processes include setting up automated data quality checks and monitoring systems to detect and address issues promptly.

Emphasizing security and compliance

Data security and compliance should be a top priority. Engineers must implement encryption, access controls, and audit logging to protect sensitive information. They should also stay up-to-date with regulatory requirements and ensure data handling practices comply with relevant laws and standards.

Investing in monitoring and observability

Effective monitoring and observability are essential for maintaining the health and performance of data platforms. Engineers should set up comprehensive database management and monitoring systems to track key metrics, detect anomalies, and troubleshoot issues. These systems should include logging, tracing, and alerting mechanisms to ensure timely identification and resolution of problems.

Future trends in data platform engineering

Data platform engineering is constantly evolving, with new trends and technologies emerging. Here are some future trends to watch out for:

Adoption of cloud-native data platforms

Common data platforms native to the cloud offer scalability, flexibility, and cost-efficiency. Companies increasingly adopt cloud-based solutions, such as AWS Redshift, Google BigQuery, and Azure Synapse, for their data processing and storage needs. Data platform engineers must stay abreast of this modern data stack and leverage their capabilities to build robust and scalable data platforms.

Integration of AI and machine learning

AI and machine learning are transforming data engineering. Data platform engineers are incorporating AI and ML techniques to automate data processing, enhance data quality, and enable advanced analytics. Their work includes using advanced AI applications and ML models for data validation, anomaly detection, and predictive analytics.

Focus on real-time data processing

Many companies are increasingly requiring real-time data processing. Data platform engineers are leveraging technologies like Apache Kafka, Apache Flink, and AWS Kinesis to build business-ready data pipelines that serve as a single source of truth. This enables timely insights and decision-making based on up-to-date business insights.

How DX can help with real-time platform feedback

Data platform engineering is essential for companies to harness the power of data with a strong analytics system. By designing, building, and maintaining reliable data infrastructure, platform engineering leaders ensure data is accessible, dependable, and secure. Understanding the nuances of this role and its place in a broader platform engineering organization is key to leveraging its full potential.

DX enhances your platform engineering organization by providing real-time insights and actionable feedback that improve data platform engineering. By treating the platform as a product, DX ensures increased ROI through comprehensive usage, adoption, and satisfaction metrics. It integrates seamlessly into developer portals, code libraries, and command-line tools, offering a birds-eye view of developer platform usage. This enables platform engineering teams to optimize workflows, streamline processes, and make data-driven decisions that boost efficiency and developer productivity.