Hadoop is more cost effective processing massive data sets.
Apache Pig is a platform for analysing large sets of data.
It can also do micro-batching using Spark Streaming (an abstraction on Spark to perform stateful stream processing).
Apache Spark vs Hadoop; Apache Spark: Apache Hadoop: Easy to program and does not require any abstractions. I described the architecture of Apache storm in my previous post[1]. However, it is important to consider the total cost of ownership, which includes maintenance, hardware and software purchases, and hiring a team that understands cluster administration.
Both Spark and Hadoop are available for free as open-source Apache projects, meaning you could potentially run it with zero installation costs.
It includes a high level scripting language called Pig Latin that automates a lot of the manual coding comparing it to using Java for MapReduce jobs. Apache Spark utilizes RAM and isn’t tied to Hadoop’s two-stage paradigm. The Five Key Differences of Apache Spark vs Hadoop MapReduce: Apache Spark is potentially 100 times faster than Hadoop MapReduce.
Spark is a framework to perform batch processing.
It’s worth pointing out that Apache Spark vs. Apache Hadoop is a bit of a misnomer. Apache Spark is an open-source distributed general-purpose cluster-computing framework.Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since.
It is used for generating reports that help find answers to historical queries. Apache Spark works well for smaller data sets that can all fit into a server's RAM.
You’ll find Spark included in most Hadoop distributions these days. In this article, we discuss Apache Hive for performing data analytics on large volumes of data using SQL and Spark as a framework for running big data analytics.
Difficult to program and requires abstractions. A detailed description of the architecture of Spark & Spark Streaming is available here. Programmers can perform streaming, batch processing and machine learning ,all in the same cluster. Apache hive uses a SQL like scripting language called HiveQL that can convert queries to MapReduce, Apache Tez and Spark jobs.
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