Hi, My two cents here.
Hadoop as I understand has two components namely HDFS (Hadoop Distributed File System) and MapReduce. Whatever we use I still think we need to store data on HDFS (excluding standalones like MongoDB etc.). Now moving to MapReduce as the execution engine that is replaced by TEZ (basically MapReduce with DAG) or with Spark which uses in memory capabilities and DAG. MapReduce is the one moving sideways. To me Spark besides being versatile is a powerful tool. Remember tools are just tools, not solutions so we can discuss this all day. Effectively I would argue that with Spark as the front end tool with Hive and its organisation for metadata plus HDFS as the storage layer, you have all three components to create a powerful solution. HTH Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com On 14 April 2016 at 20:22, Andy Davidson <a...@santacruzintegration.com> wrote: > Hi Ashok > > In general if I was starting a new project and had not invested heavily in > hadoop (i.e. Had a large staff that was trained on hadoop, had a lot of > existing projects implemented on hadoop, …) I would probably start using > spark. Its faster and easier to use > > Your mileage may vary > > Andy > > From: Ashok Kumar <ashok34...@yahoo.com.INVALID> > Reply-To: Ashok Kumar <ashok34...@yahoo.com> > Date: Thursday, April 14, 2016 at 12:13 PM > To: "user @spark" <user@spark.apache.org> > Subject: Spark replacing Hadoop > > Hi, > > I hear that some saying that Hadoop is getting old and out of date and > will be replaced by Spark! > > Does this make sense and if so how accurate is it? > > Best > >
