Data engineering is a dynamic field, constantly evolving to meet the challenges of the data-driven world, says Sneha Sharma.

Data is the soul of any project or organisation. From personalised recommendations on streaming platforms to complex financial models predicting market trends, data fuels decision-making and innovation. But raw data needs to be collected, cleaned, transformed, and organised before it can deliver valuable insights. That's where data engineering comes in.

Data engineering is the practice of designing, building, and maintaining the infrastructure that enables the collection, processing, and storage of data. Data engineers work to ensure that data is readily available, reliable, and secure for data scientists, analysts, and other stakeholders. They build and manage complex systems that handle massive volumes of data from various sources, ensuring its quality and accessibility. Demand for data engineering is surging, especially for vertical-specific solutions. BFSI offerings include real-time processing, data governance frameworks, and AI/ML integration. Healthcare solutions focus on unified data platforms, automated processing, and data mesh architectures.

Some factors that has led to the rise of data engineering:

Rise of cloud and its impact

Cloud has revolutionised data engineering. Cloud platforms like AWS, Azure, and GCP offer scalable and cost-effective solutions for storing and processing data. Cloud-native data engineering tools and services have also emerged, simplifying the development and deployment of data pipelines.

Growing popularity of data lakes and data lake houses

Volume and variety of data have exploded in recent years like social media feeds, sensor data, and log files. Data lakes have emerged as a solution for storing this raw, diverse data in its native format. Recently, the data lake house has gained traction, combining the flexibility of data lakes with the structure and governance of data warehouses. This hybrid approach enables organisations to analyse all types of data efficiently.

Advancements in data processing frameworks

Frameworks like Apache Spark and Apache Flink are at the forefront of data processing. These frameworks are constantly evolving, incorporating new features and optimisations to handle the ever-increasing demands of data processing.

Data engineering is a dynamic field, constantly evolving to meet the challenges of the data-driven world. As data volumes continue to grow and new technologies emerge, data engineers will play a crucial role in enabling organisations to unlock the full potential of their data. By staying ahead of the latest trends and technologies, data engineers can build robust, scalable, and secure data infrastructure that drives business success.

Article Courtesy: NASSCOM Community – an open knowledge sharing platform for the Indian technology industry: https://community.nasscom.in/index.php/communities/data-science-ai-community/datas-explosive-growth-demands-increasingly-crucial-data

Sneha Sharma is an analyst at NASSCOM. Her current focus areas include: IT services, AIOps, 5G, cloud, and project management. She also specialises in application rationalisation, cost optimisation, benchmarking, report writing, and market research.