Serverless vs Apache Spark: What are the differences?
Serverless: The most widely-adopted toolkit for building serverless applications. Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more; Apache Spark: Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Serverless can be classified as a tool in the "Serverless / Task Processing" category, while Apache Spark is grouped under "Big Data Tools".
"API integration " is the primary reason why developers consider Serverless over the competitors, whereas "Open-source" was stated as the key factor in picking Apache Spark.
Serverless and Apache Spark are both open source tools. Serverless with 30.5K GitHub stars and 3.38K forks on GitHub appears to be more popular than Apache Spark with 22.3K GitHub stars and 19.3K GitHub forks.
Slack, Shopify, and SendGrid are some of the popular companies that use Apache Spark, whereas Serverless is used by Droplr, Plista GmbH, and Hammerhead. Apache Spark has a broader approval, being mentioned in 263 company stacks & 111 developers stacks; compared to Serverless, which is listed in 112 company stacks and 43 developer stacks.
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