mikeaddison93/hoya Implementing the SaaS Maturity Model - SaaS Addict When it comes to SaaS maturity model, maturity is not an all-or-nothing proposition, as a SaaS application can possess one or two important attributes and still manage to fit the typical definition and meet the essential business requirements. So in that case the app architects may choose not to meet or fulfill other attributes, especially if in so doing the action would be rendered cost ineffective. Broadly speaking, SaaS maturity can be demonstrated using a delivery model with 4 distinct levels, with each level distinguished from all other previous ones by simply adding one, two or more attributes. The four levels are briefly described below. Level I: Custom/ Ad Hoc This level of SaaS maturity resembles the conventional ASP (application service provider) software delivery model with its origin in the 1990s. Level II: Configurable This is the 2nd level SaaS maturity is where your SaaS vendor hosts a totally different instance of the SaaS application for each tenant or customer.
cpitman/spark-drools-example: A simple example of how to integrate drools into an Apache Spark job mikeaddison93/hadoop-20 antlr4/README.md at master · antlr/antlr4 Giraph - Welcome To Apache Giraph! Process Data Using Amazon EMR with Hadoop Streaming - AWS Data Pipeline You can use AWS Data Pipeline to manage your Amazon EMR clusters. With AWS Data Pipeline you can specify preconditions that must be met before the cluster is launched (for example, ensuring that today's data been uploaded to Amazon S3), a schedule for repeatedly running the cluster, and the cluster configuration to use. The following tutorial walks you through launching a simple cluster. In this tutorial, you create a pipeline for a simple Amazon EMR cluster to run a pre-existing Hadoop Streaming job provided by Amazon EMR and send an Amazon SNS notification after the task completes successfully. Pipeline Objects The pipeline uses the following objects: EmrActivity Defines the work to perform in the pipeline (run a pre-existing Hadoop Streaming job provided by Amazon EMR). EmrCluster Resource AWS Data Pipeline uses to perform this activity. A cluster is a set of Amazon EC2 instances. Schedule Start date, time, and the duration for this activity. SnsAlarm
JetS3t Deep Dive into Spark SQL’s Catalyst Optimizer Spark SQL is one of the newest and most technically involved components of Spark. It powers both SQL queries and the new DataFrame API. At the core of Spark SQL is the Catalyst optimizer, which leverages advanced programming language features (e.g. Scala’s pattern matching and quasiquotes) in a novel way to build an extensible query optimizer. We recently published a paper on Spark SQL that will appear in SIGMOD 2015 (co-authored with Davies Liu, Joseph K. Bradley, Xiangrui Meng, Tomer Kaftan, Michael J. To implement Spark SQL, we designed a new extensible optimizer, Catalyst, based on functional programming constructs in Scala. At its core, Catalyst contains a general library for representing trees and applying rules to manipulate them. Trees The main data type in Catalyst is a tree composed of node objects. As a simple example, suppose we have the following three node classes for a very simple expression language: Rules Applying this to the tree for x+(1+2) would yield the new tree x+3.