![]() There seem to be no limits to the amount of data it can process (if you need things faster, simply add more DPUs, or "Data Processing Units"). It leads the industry with massive computing capacity, and is incredibly fast. Processing them in "real-time" is critical to getting the data when you need it. Comparison SpeedĮTL and Data Warehousing involve large datasets. This may be a tougher decision than we imagined! Let's look at some of the features of both, and see if either stands out. No muss, no fuss, they simply "Smash" it. It would be nice to have someone solve this problem on our behalf. But after a pause, his face contorts with anger. Timidly, we laid out the problem we're looking to solve. He is sitting on a log, pondering the murky water beneath his feet. After a long slog through a creepy marsh, we find him. Inquiring around local taverns and inns, we heard frightening tales of a monster living in a nearby forest. Seamlessly integrates with other AWS Services, including S3 and Amazon Redshift Spectrum.įrankenstein was a bit more difficult to research.Manages Jobs to move the data, allowing for powerful scheduling and retry possibilities.AWS Glue uses crawlers to inspect your various schemas, and auto-generates the necessary code to transform from source to destination. Builds transformations between data sources.This includes where the data is stored, and the underlying schema of that data. Collects information about your data sources.Amazon offers a service for everything, don't they? From humans doing small tasks to Fargate, their container service, they offer it. ![]() AWS GlueĪccording to AWS documentation, AWS Glue is "a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics". Let's start with an introduction to both. We hope to make this tough decision easier for those facing the same question. We will examine pros and cons of both, and highlight differences between them.
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