It is incredible how rapidly data processing technologies and tools are evolving today. The pace of the evolution is drastically changing the nature of the data engineering discipline. Tools and technologies we are using today are far different from what we used ten or even five years ago. Overall, the industry is leading toward data management environments that produce insights from AI and machine learning while leveraging the power of cloud for agility.
Gartner defines data engineering as – “Data engineering is the practice of making the appropriate data accessible and available to various data consumers (including data scientists, data analysts, business analytics and business users). It is a discipline that involves collaboration across business and IT.”
Data management at scale is the hardest challenge for AI and advanced analytics. Today, the scale of data has far outpaced the technologies that traditionally managed it. MapReduce, Hadoop, Yarn, HDFS, are among the principal technologies that enabled businesses to handle wide varieties, high volumes and various types of data.
The adoption of cloud computing and the advent of technologies such as Kafka, Spark and serverless have all ushered in the era of data engineering, efficiently uncoupling storage and compute, enabling more agile processing of multi-latency petabyte-scale data with auto-tuning and auto-scaling.
The Key Disruptors of Data Engineering:
Cloud – Cloud computing has been one of the greatest disruptors of big data. It brings huge cost savings and efficiency in the processing of data engineering pipelines by separating storage and compute, and by making it easy to scale and tune servers.
Spark – Apache Spark has been another major disruptor which grew exponentially over the last couple of years. Spark is a distributed processing engine for data engineering workloads at petabyte scale, enabling Analytics and machine learning. Speed is the most influential advantage of Spark.
Serverless – The serverless capability enables organizations to build applications comprised of microservices that run in response to events, auto-scale as per your requirement, and only charge you when they are used. This reduces the total cost of maintaining your applications, enabling you to build more logic, faster.
Kafka – Apache Kafka is an emerging technology that is capable of handling trillions of events a day. The open-source stream-processing software provides a high-throughput, unified, low-latency platform for handling real-time data feeds.
Technologies like Spark, Cloud, serverless, and Kafka, among others, have made big data near-obsolete when it comes to data management and analytics. Heavy adoption of these technologies by key providers like Microsoft Azure, Amazon Web Services, and Databricks advanced the evolution of big data to data engineering.