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[marketingactivo]

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[quarkempresas]

Megatendencias #tecnológicas: La movilidad, la internet de las cosas, la información en la nube y el Big data

[irq]

We currently use Amazon’s Elastic MapReduce (EMR) distribution of Hadoop. Our use of S3 as the data warehouse enables us to spin up multiple Hadoop clusters for different workloads, all accessing the exact same data. A large (500+ node) "query" cluster is used by engineers, data scientists and analysts to perform ad hoc queries. Our "production" (or “SLA”) cluster, which is around the same size as the query cluster, runs SLA-driven ETL (extract, transform, load) jobs. We also have several other “dev” clusters that are spun up as needed. If we had used HDFS as our source of truth, then we would need a process to replicate data across all the clusters. With our use of S3, this is non-issue because all clusters have instant access to the entire dataset. We dynamically resize both our query and production clusters daily. Our query cluster can be smaller at night when there are fewer developers logged in, whereas the production cluster must be larger at night, when most of our ETL is run. We do not have to worry about data redistribution or loss during expand/shrink because the data is on S3. And finally, although our production and query clusters are long-running clusters in the cloud, we can treat them as completely transient. If a cluster goes down, we can simply spin up another identically sized cluster (potentially in another Availability Zone, if needed) in tens of minutes with no concerns about data loss.

The Netflix Tech Blog: Hadoop Platform as a Service in the Cloud