Challenges with data processing platforms
Data processing or data transformation is an essential part of any data pipeline, and data engineers play a big role in making sure that the data reaches its final destination, where it’s ready for consumption. In the recent decade, the volume, velocity, and variety of data have made data processing challenging. Data turned into big data, and processing all this data in a sequential manner using powerful monolithic systems turned out to be inefficient. Data processing techniques took a positive direction when a horizontal scaling framework using Apache Hadoop was created. Hadoop was able to process big data much more efficiently using many commodities’ hardware.
Even though Hadoop was promising, the MapReduce way of processing big data was not fast enough for many organizations. The creation of Apache Spark changed the way we process data, and even today, many modern data processing systems and platforms primarily use Spark...