What emerging data engineering trends and technologies should you know for your portfolio?

1 Cloud-native data platforms

One of the major trends in data engineering is the shift to cloud-native data platforms, which are designed to run on cloud infrastructure and leverage its scalability, elasticity, and reliability. Cloud-native data platforms offer several advantages, such as lower costs, faster deployment, easier integration, and better performance. Some of the popular cloud-native data platforms include AWS, Azure, Google Cloud, and Databricks. To showcase your cloud-native data engineering skills, you should be familiar with the core services and tools of these platforms, such as storage, compute, networking, security, and analytics.

2 Data mesh architecture

Another emerging trend in data engineering is the adoption of data mesh architecture, which is a decentralized and distributed approach to data management and governance. Data mesh architecture treats data as a product, and assigns domain-specific teams to own, produce, and consume data. Data mesh architecture aims to overcome the challenges of traditional data architectures, such as silos, bottlenecks, and complexity. To demonstrate your data mesh architecture skills, you should be able to design, implement, and monitor data products that follow the principles of data mesh, such as domain-driven, self-serve, interoperable, and observable.

3 DataOps and MLOps

DataOps and MLOps are two related trends that apply the principles and practices of DevOps to data engineering and machine learning respectively. DataOps and MLOps focus on improving the quality, speed, and reliability of data pipelines and models, by using automation, collaboration, and monitoring. DataOps and MLOps also emphasize the alignment of data and machine learning projects with business objectives and outcomes. To highlight your DataOps and MLOps skills, you should be able to use tools and frameworks that support these methodologies, such as Airflow, Kubeflow, MLflow, and Dagster.

4 Streaming data processing

Streaming data processing is a trend that involves processing data in real-time or near-real-time, as it is generated or received from various sources. Streaming data processing enables faster and more responsive data analysis and decision making, as well as new use cases and applications, such as fraud detection, anomaly detection, and personalization. To showcase your streaming data processing skills, you should be able to use technologies and platforms that enable streaming data ingestion, processing, storage, and visualization, such as Kafka, Spark, Flink, and Kinesis.

5 Data quality and observability

Data quality and observability are two essential aspects of data engineering that ensure the reliability, accuracy, and usability of data. Data quality refers to the degree to which data meets the expectations and requirements of its consumers and stakeholders. Data observability refers to the ability to monitor and measure the health and performance of data pipelines and platforms. To demonstrate your data quality and observability skills, you should be able to use tools and techniques that enable data validation, testing, profiling, lineage, cataloging, and alerting, such as Great Expectations, dbt, Datahub, and Amundsen.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?