Data Engineering involves the design, development, and management of data infrastructure and pipelines. It focuses on collecting, processing, transforming, and storing data in a scalable and efficient manner. Data Engineers build systems that enable data analytics, machine learning, and other data-driven applications, ensuring reliable and timely access to high-quality data for insights and decision-making.
For big data and complex data processing, data pipelines have emerged as a popular solution for managing and manipulating data. They provide a systematic approach to extract, transform, and load (ETL) data from various sources, enabling organizations to derive valuable insights. However, as with any...
Building data pipelines can offer strategic advantages to the business. It can be used to power new analytics, insight, and product features. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.
Developing Extract–transform–load (ETL) workflow is a time-consuming activity yet a very important component of data warehousing process. The process to develop ETL workflow is often ad-hoc, complex, trial and error based. It has been suggested that formal modeling of ETL process can alleviate...
ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. With the arrival of new cloud-native tools and platform, ETL...
Kubernetes has emerged as go to container orchestration platform for data engineering teams. Kubernetes has a massive community support and momentum behind it. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of...
