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- AI Converged Infrastructures
Data Pipelines, Data Lakes and Management
- NetApp AI Control Plane
- Distributed training in Azure - Click-Through Rate Prediction
- Conversational AI using NVIDIA
- Anthos with NetApp
- DevOps with NetApp Astra
Red Hat OpenShift with NetApp
- NetApp Storage Integrations Overview
Solution Validation and Use Cases
- Red Hat OpenShift Virtualization with NetApp ONTAP
- VMware Tanzu with NetApp
- Data Migration and Data Protection
- Oracle Database
- SnapCenter for Databases
- Modern Data Analytics
NetApp Hybrid Multicloud with VMware
- VMware for Public Cloud
- NetApp for GCP / GCVE
- NetApp Hybrid Multicloud with Red Hat OpenShift
- VMware vSphere Automation
- Virtual Desktops
- Artificial Intelligence
Apache Spark is a popular programming framework for writing Hadoop applications that works directly with the Hadoop Distributed File System (HDFS). Spark is production ready, supports processing of streaming data, and is faster than MapReduce. Spark has configurable in-memory data caching for efficient iteration, and the Spark shell is interactive for learning and exploring data. With Spark, you can create applications in Python, Scala, or Java. Spark applications consist of one or more jobs that have one or more tasks.
Every Spark application has a Spark driver. In YARN-Client mode, the driver runs on the client locally. In YARN-Cluster mode, the driver runs in the cluster on the application master. In the cluster mode, the application continues to run even if the client disconnects.
There are three cluster managers:
Standalone. This manager is a part of Spark, which makes it easy to set up a cluster.
Apache Mesos. This is a general cluster manager that also runs MapReduce and other applications.
Hadoop YARN. This is a resource manager in Hadoop 3.
The resilient distributed dataset (RDD) is the primary component of Spark. RDD recreates the lost and missing data from data stored in memory in the cluster and stores the initial data that comes from a file or is created programmatically. RDDs are created from files, data in memory, or another RDD. Spark programming performs two operations: transformation and actions. Transformation creates a new RDD based on an existing one. Actions return a value from an RDD.
Transformations and actions also apply to Spark Datasets and DataFrames. A dataset is a distributed collection of data that provides the benefits of RDDs (strong typing, use of lambda functions) with the benefits of Spark SQL’s optimized execution engine. A Dataset can be constructed from JVM objects and then manipulated using functional transformations (map, flatMap, filter, and so on.). A DataFrame is a dataset organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python. DataFrames can be constructed from a wide array of sources such as structured data files, tables in Hive/HBase, external databases on-premises or in the cloud, or existing RDDs.
Spark applications include one or more Spark jobs. Jobs run tasks in executors, and executors run in YARN containers. Each executor runs in a single container, and executors exist throughout the life of an application. An executor is fixed after the application starts, and YARN does not resize the already allocated container. An executor can run tasks concurrently on in-memory data.