Category: Information Technology

What is Nutanix?

According to Techzine, Nutanix is a so-called hyper-converged Infrastructure (HCI) solution. A software platform (cluster) that runs on top of different kinds of individual servers (nodes). All these servers are linked together through this HCI software platform. All processors, internal memory, hard disks (storage), and network interfaces are bundled in one cluster to run virtual machines.

The powerful thing about an HCI platform is the way all applications and workloads are distributed across the hardware, to optimize performance as much as possible. There is also redundancy built-in by dividing data and workloads across multiple servers. As soon as one of the nodes fails, the availability of the platform and applications is not affected.

It is now a pure software company, but this hasn’t always been the case. It once started with its own hardware appliance on which the Nutanix software ran. By buying multiple Nutanix appliances (nodes), you could build your own hyper-converged infrastructure. The company is now so big that they no longer need to develop and deliver the hardware itself. The major hardware manufacturers are now lining up to partner with them.

It is great in developing an HCI software platform, but not in building the best hardware. When companies choose Nutanix, they can call Nutanix for software issues, but call their hardware supplier for hardware-related incidents. By doing it this way, customers have the best experience and the best support.

What are the Nutanix Advanced Administration and Performance Management course about?

This course features comprehensive coverage of performance management for Nutanix clusters and details on how to improve data center performance. You’ll learn through hands-on labs how to monitor system performance as well as performance tuning. Also covered are advanced networking and storage to help optimize data center administration.

This course explains in detail how to use the major Acropolis services such as Volumes and Files. The course also explains how to define and manage assets and applications using Calm, including how to connect to clouds, automation of the Life Cycle Management (LCM) application, and how to implement and configure Self Service Portal and governance.

You will learn how to take advantage of Flash mode to improve system performance, as well as how to effectively clone and delete VMs, move them between storage containers, and how to manage VMs (sizing and migration).

This course also covers Data Protection solutions such as Metro Availability with Witness. Advanced management using the new features of Prism Central, and the command line are also covered in detail. This includes how to take advantage of machine learning with entity management and resource optimization and plan for future growth using Scenario Management using Prism Pro.

Target Audience:

IT administrators, architects, and business leaders who already manage Nutanix clusters in the data center, but who would like more in-depth knowledge of Nutanix data center administration. Anyone seeking preparation for the Nutanix Certified Advanced Professional (NCAP) certification (in developments) leaders who already manage Nutanix clusters in the data center, but who would like more in-depth knowledge of Nutanix data center administration. Anyone seeking to prepare for the Nutanix Certified Advanced Professional (NCAP) certification.

Course Objectives:

After completing this course, you should be able to:

• Implement business continuity and disaster recovery strategies
• Analyze and configure Nutanix systems for peak operational efficiency
• Use Nutanix tools to analyze workloads and optimize cluster and VM sizing
• Perform advanced virtual machine administration
• Customize security for Nutanix systems
• Anticipate and plan for future resource needs

Prerequisites:

Attendees should meet the following prerequisites:

• Nutanix Enterprise Cloud Administration 5.5 (ECA 5.5) classroom training or NCP Certification
• Basic knowledge of Nutanix datacenter administration techniques
• Familiarity with traditional virtualization storage architectures
• Comfortable with Linux command-line interface

Course Content:

Module 1: Administering Advanced Virtual Machine Deployments

Module 2: Implementing Business Continuity and Disaster Recovery

Module 3: Configuring Advanced Networking

Module 4: Enabling and Customizing Security Services

Module 5: Managing Acropolis File and Block Services

Module 6: Administering Prism Central and Prism Pro

Module 7: Managing and Optimizing Performance

Module 8: Utilizing Advanced Management Interfaces

Key learnings:

During this course you will learn how to:

Monitor data center performance and manage components to optimize system performance.

Set up and configure advanced VM administration features such as:

• Self Service Restore
• Configuration of Nutanix Guest Tools (NGT)
• Working with Nutanix storage containers to delete and move vDisks

Implement advanced solutions for business continuity and data protection in Nutanix data centers such as:

• Cloud Connect
• Metro Availability
• Advanced API
• REST API V3

Configure advanced networking features including:

• Bridge and uplink management
• Load balancing across multiple NICs Network visualization
• Physical switch topology and configuration

Customize Nutanix security features such as:

• Creating and installing SSH keys for Prism Lockdown Mode
• Two-factor authentication
• Using Security Technical Implementation Guides (STIGs)

Eliminate the requirement for a third-party file server when sharing files across user work stations or VMs (Nutanix Files) or designing a scale-out storage solution (Nutanix Volumes).

Use Prism Central to:

• Identify and fix cluster health problems
• Exploit machine learning for entity management and resource optimization
• Plan for future growth
• Manage assets and applications using Calm, Life Cycle Management (LCM), and Self-Service Portal

Practice advanced data center management procedures using hands-on labs.

Get the most out of Nutanix systems by maximizing configuration and operation for peak efficiency.

Guarantee business continuity through advanced data protection strategies.

Validate your new skills by preparing for and completing the (NCAP) certification (in development).

Conclusion:

If you are looking to learn how to work with one of the most feasible and robust hybrid and multi-cloud solutions, this Nutanix course is the finest there is.

To enroll, contact P2L today!

What is Apache Spark?

According to IBM, Apache Spark (Spark) is an open-source data-processing engine for large data sets. It is designed to deliver the computational speed, scalability, and programmability required for Big Data—specifically for streaming data, graph data, machine learning, and artificial intelligence (AI) applications.

Spark’s analytics engine processes data 10 to 100 times faster than alternatives. It scales by distributing processing work across large clusters of computers, with built-in parallelism and fault tolerance. It even includes APIs for popular programming languages among data analysts and data scientists, including Scala, Java, Python, and R.

What is Apache Hadoop?

The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

What is the Cloudera Developer Training for Spark & Hadoop course about?

This four-day hands-on training course delivers developers’ key concepts and expertise to use Apache Spark to develop high-performance parallel applications. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with “big data” stored in a distributed file system and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.

Course Objectives:

• How the Apache Hadoop ecosystem fits in with the data processing lifecycle
• How data is distributed, stored, and processed in a Hadoop cluster
• How to write, configure, and deploy Apache Spark applications on a Hadoop cluster
• How to use the Spark shell and Spark applications to explore, process, and analyze distributed data • How to query data using Spark SQL, DataFrames, and Datasets
• How to use Spark Streaming to process a live data stream

Prerequisites:

This course is designed for developers and engineers who have programming experience, but prior knowledge of Spark and Hadoop is not required. Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.

Get Certified!

Upon completion of the course, attendees are encouraged to continue their studies and register for the CCA Spark and Hadoop Developer exam. Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.

Topics:

• Introduction
• Introduction to Apache Hadoop and the Hadoop Ecosystem
• Apache Hadoop Overview
• Apache Hadoop File Storage
• Distributed Processing on an Apache Hadoop Cluster
• Apache Spark Basics
• Working with DataFrames and Schemas
• Analyzing Data with DataFrame Queries
• RDD Overview
• Transforming Data with RDDs

Conclusion:

Apache Spark and Apache Hadoop are two of the most promising and prominent distributed systems for processing big data in the machine learning world today.

To get a good understanding and learn the difference between the two systems, opt for this comprehensive course that sheds light on how to work with big data.

To enroll, contact P2L today!

What is Google TensorFlow?

According to Guru99, Google TensorFlow is an open-source end-to-end platform for creating Machine Learning applications. It is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on the training and inference of deep neural networks. It allows developers to create machine learning applications using various tools, libraries, and community resources.

What is the history of TensorFlow?

According to Guru99, a couple of years ago, deep learning started to outperform all other machine learning algorithms when giving a massive amount of data. Google saw it could use these deep neural networks to improve its services:

• Gmail
• Photo
• Google search engine

They build a framework called Tensorflow to let researchers and developers work together on an AI model. Once developed and scaled, it allows lots of people to use it.

It was first made public in late 2015, while the first stable version appeared in 2017. It is open source under the Apache Open-Source license. You can use it, modify it, and redistribute the modified version for a fee without paying anything to Google.

How does TensorFlow work?

According to Guru99, TensorFlow enables you to build dataflow graphs and structures to define how data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.

Why is it called TensorFlow?

According to Guru99, It is called Tensorflow because it takes input as a multi-dimensional array, also known as tensors. You can construct a sort of flowchart of operations (called a Graph) that you want to perform on that input. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output.

Therefore, it is called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.

What is the Scalable Deep Learning with TensorFlow and Apache Spark course about?

This course starts with the basics of the tf.keras API including defining model architectures, optimizers, and saving/loading models. You then implement more advanced concepts such as callbacks, regularization, TensorBoard, and activation functions.

After training your models, you will integrate the MLflow tracking API to reproduce and version your experiments. You will also apply model interpretability libraries such as LIME and SHAP to understand how the network generates predictions. You will also learn about various Convolutional Neural Networks (CNNs) architectures and use them as a basis for transfer learning to reduce model training time.

Substantial class time is spent on scaling your deep learning applications, from distributed inference with pandas UDFs to distributed hyperparameter search with Hyperopt to distributed model training with Horovod. This course is taught fully in Python.

Course Duration:

Two days

Objectives:

Upon completion of the course, students should be able to:

• Build deep learning models using Keras/TensorFlow
• Scale the following:
  • Model inference with pandas UDFs & pandas function API
  • Hyperparameter tuning with HyperOpt
  • Training of distributed TensorFlow models with Horovod
• Track, version, and reproduce experiments using MLflow
• Apply model interpretability libraries to understand & visualize model predictions
• Use CNNs (convolutional neural networks) and perform transfer learning & data augmentation to improve model performance
• Deploy deep learning models

Target Audience:

• Data scientist
• Machine learning engineer

Prerequisites:

• Intermediate experience with Python/pandas
• Familiarity with machine learning concepts
• Experience with PySpark

Additional Notes:

• The appropriate, web-based programming environment will be provided to students
• This class is taught in Python only

Topics:

• Intro to Neural Networks with Keras
• MLflow
• Convolutional Neural Networks
• Deep Learning Pipelines
• Horovod

Conclusion:

Google’s TensorFlow is currently the most famous and sought-after deep learning library because of its high accessibility. Google aims to provide its users with the best AI experience which it achieves with TensorFlow.

If you want to learn more about this deep learning framework then this course is ideal for you.

To enroll, contact P2L today!

What is Apache Spark?

Infoworld describes Spark as a data processing framework that can quickly perform processing tasks on very large data sets and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. These two qualities are key to the big data and machine learning worlds, which require the marshaling of massive computing power to crunch through large data stores. Spark also takes some of the programming burdens of these tasks off the shoulders of developers with an easy-to-use API that abstracts away much of the grunt work of distributed computing and big data processing.

What is the story of Spark?

As per Towards Data Science, in the 2010s, when RAM prices came down, Spark was born with a big design change to store all intermediate data to RAM, instead of disk.

Spark was good for both:

1. i) Data-heavy tasks: as it was using HDFS &
2. ii) Compute-heavy tasks: as it uses RAM instead of disk, to store intermediate outputs. E.g.: Iterative solutions

As Spark could utilize RAM, it became an efficient solution for iterative tasks in Machine Learning like Stochastic Gradient Descent (SGD). So is the reason, Spark MLlib became so popular for Machine Learning, in contrast to Hadoop’s Mahout.

Furthermore, to do Distributed Deep-Learning with TF you can use,

1. Multiple GPUs on the same box (or)
2. Multiple GPUs on different boxes (GPU Cluster)

While today’s supercomputers use GPU Cluster for compute-intensive tasks, you can install Spark in such a cluster to make it suitable for tasks such as distributed deep-learning, which are both compute and data-intensive.

What is the Scalable Machine Learning with Apache Spark course all about?

In this course, you will experience the full data science workflow, including data exploration, feature engineering, model building, and hyperparameter tuning. You will have built an end-to-end distributed machine learning pipeline ready for production by the end of this course.

This course guides students through the process of building machine learning solutions using Spark. You will build and tune ML models with SparkML using transformers, estimators, and pipelines. This course highlights some of the key differences between SparkML and single-node libraries such as sci-kit-learn. Furthermore, you will reproduce your experiments and version your models using MLflow.

You will also integrate 3rd party libraries into Spark workloads, such as XGBoost. In addition, you will leverage Spark to scale inference of single-node models and parallelize hyperparameter tuning. This course includes hands-on labs and concludes with a collaborative capstone project. All the notebooks are available in Python, and Scala as well where available.

Skills Gained:

• Create data processing pipelines with Spark
• Build and tune machine learning models with SparkML
• Track, version, and deploy models with MLflow
• Perform distributed hyperparameter tuning with Hyperopt
• Use Spark to scale the inference of single-node models

Who Can Benefit?

• Data scientist
• Machine learning engineer

Prerequisites:

Intermediate experience with Python Beginning experience with the PySpark DataFrame API (or have taken the Apache Spark Programming with Databricks class) Working knowledge of machine learning and data science

Conclusion:

If you’re looking to learn a big data platform that is fast, flexible, and developer-friendly, then Apache Spark is the answer! It has an in-memory data engine which means that it can perform tasks up to one hundred times faster than other datasets processing big data. It is one of the most preferred open-source analytics engines that is used by banks, telecommunications companies, games companies, governments, and all the major tech giants such as Apple, Facebook, IBM, and Microsoft.

To enroll, contact P2L today!

What is Apache Spark?

Databricks defines Apache Spark as a lightning-fast unified analytics engine for big data and machine learning. Since its release, Apache Spark, the unified analytics engine, has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Netflix, Yahoo, and eBay have deployed Spark at a massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. It has quickly become the largest open-source community in big data, with over 1000 contributors from 250+ organizations.

What is the Apache Spark Programming with Databricks all about?

This course uses a case study-driven approach to explore the fundamentals of Spark Programming with Databricks, including Spark architecture, the DataFrame API, query optimization, and Structured Streaming. First, you will become familiar with Databricks and Spark, recognize their major components, and explore datasets for the case study using the Databricks environment. After ingesting data from various file formats, you will process and analyze datasets by applying a variety of DataFrame transformations, Column expressions, and built-in functions. Lastly, you will execute streaming queries to process streaming data and highlight the advantages of using Delta Lake.

What is the duration of the course?

The course is two days long.

Course Objectives:

• Upon completion of the course, students should be able to meet the following objectives:
• Define the major components of Spark architecture and execution hierarchy
• Describe how DataFrames are built, transformed, and evaluated in Spark
• Apply the DataFrame API to explore, preprocess, join, and ingest data in Spark
• Apply the Structured Streaming API to perform analytics on streaming data
• Navigate the Spark UI and describe how the catalyst optimizer, partitioning and caching affect Spark’s execution performance

Target Audience:

• Data engineer
• Data scientist
• Machine learning engineer
• Data architect

Prerequisites:

• Familiarity with basic SQL concepts (select, filter, group by, join, etc.)
• Beginner programming experience with Python or Scala (syntax, conditions, loops, functions)

Additional Notes:

All ​participants ​will ​need-

• An ​internet ​connection
• A ​device ​that is compliant with the following supported internet browsers ​

NOTE: GoToTraining ​is ​our chosen online ​platform ​through which the ​class ​will ​be ​delivered and ​prior ​to ​attendance, ​each ​registrant ​will ​receive ​GoToTraining ​log-in ​instructions.

Course Outline:

Day 1: DataFrames

• Introduction: Databricks Ecosystem, Spark Overview, Case Study
• Databricks Platform: Databricks Concepts, Databricks Platform, Lab
• Spark SQL: Spark SQL, DataFrames, SparkSession, Lab
• Reader and Writer: Data Sources, DataFrameReader/Writer, Lab

Day 2: DataFrames and Transformations

• DataFrame and Column: Columns and Expressions, Transformations, Actions, Rows, Lab
• Aggregation: Groupby, Grouped Data Methods, Aggregate Functions, Math Functions, Lab
• Datetimes: Dates and Timestamps, Datetime Patterns, Date Functions, Lab
• Complex types: String Functions, Collection Functions
• Additional Functions: Non-aggregate Functions, Na Functions, Lab

Day 3: Transformations and Spark Internals

• Transformations: UDFs: UDFs, Vectorized UDFs, Performance, Lab
• Spark Architecture: Spark Cluster, Spark Execution, Shuffling, Query Optimization, Catalyst Optimizer, Adaptive Query Execution
• Query Optimization: Query Optimization, Catalyst Optimizer, Adaptive Query Execution
• Partitioning: Partitions vs. Cores, Default Shuffle Partitions, Repartition, Lab
• Review: Review of lab

Day 4: Structured Streaming and Delta

• Streaming Query: Streaming Concepts, Streaming Query, Transformations, Monitoring, Lab
• Processing Streams: Lab
• Delta Lake: Delta Lake Concepts, Batch and Streaming

Conclusion:

Are you looking to learn the mechanics of an analytics platform that accelerates innovation by unifying data science, engineering, and business? Then look no further. The Apache Spark Programming with Databricks training course will shed light on the basics of creating Spark jobs, loading data, and working with data.

To enroll, contact P2L today!

What is Veeam® Backup & Replication?

Veeam help center defines Veeam Backup & Replication as comprehensive data protection and disaster recovery solution. With Veeam Backup & Replication, you can create image-level backups of virtual, physical, cloud machines and restore from them. The technology used in the product optimizes data transfer and resource consumption, which helps to minimize storage costs and the recovery time in case of a disaster.

Veeam Backup & Replication provides a centralized console for administering backup/restore/replication operations in all supported platforms (virtual, physical, cloud). Also, the console allows you to automate and schedule routine data protection operations and integrate with solutions for alerting and generating compliance reports.

What are Veeam® Backup & Replication’s main features?

As per the Veeam help center, the main functionality of Veeam Backup & Replication includes:

• Backup: creating image-level backups of virtual, physical, cloud machines and backups of NAS share files.
• Restore: performing a restore from backup files to the original or a new location. Veeam Backup & Replication offers several recovery options for various disaster recovery scenarios, including instant VM recovery, image-level restore, file-level restore, restore of application items, and so on.

• Replication: creating an exact copy of a VM and maintaining the copy in sync with the original VM.
• Continuous Data Protection (CDP): replication technology that helps you protect mission-critical VMs and reach recovery point objective (RPO) up to seconds.
• Backup Copy: copying backup files to a secondary repository.
• Storage Systems Support: backing up and restoring VMs using capabilities of native snapshots created on storage systems.
• Tape Devices Support: storing copies of backups in tape devices.
• Recovery Verification: testing VM backups and replicas before recovery.

What is the Veeam® Backup & Replication™ v11: Architecture and Design course about?

The two-day, Veeam® Backup & Replication™ v11: Architecture and Design training course focuses on teaching IT professionals how to effectively architect a Veeam solution through attaining technical excellence following the Veeam Architecture Methodology used by Veeam’s own Solution Architects. During the two days, attendees will explore requirement gathering and infrastructure assessment goals and use that information to design Veeam solutions within team exercises. Attendees will analyze considerations when turning logical designs into physical designs and describe the obligations to the implementation team that will implement that design. Other topics covered will include security, governance, and validation impacts when architecting a Veeam solution and how to build these into the overall design. Attendees should expect to contribute to team exercises, present designs, and defend decision-making.

Certification:

Completion of this course satisfies the prerequisite for taking the Veeam Certified Architect (VMCA) exam, the highest level of Veeam certification. VMCA certification proves knowledge of architecture and design concepts, highlighting the level of skill required to efficiently architect a Veeam solution in a range of real-world environments.

Target Audience:

Senior Engineers and Architects responsible for creating architectures for Veeam environments.

Prerequisites:

Ideally, VMCE certified, attendees should have extensive commercial experience with Veeam and a broad sphere of technical knowledge of servers, storage, networks, virtualization, and cloud environments.

Objectives:

After completing this course attendees should be able to:

• Design and architect a Veeam solution in a real-world environment
• Describe best practices, review an existing infrastructure, and assess business/project requirements
• Identify relevant infrastructure metrics and perform component (storage, CPU, memory) quantity sizing
• Provide implementation and testing guidelines in line with designs
• Innovatively address design challenges and pain points, matching appropriate Veeam Backup & Replication features with requirements

Course outline:

• Introduction
• Discovery
• Conceptual design
• Logical design
• Physical/tangible design
• Implementation and Governance
• Validation and Iteration

Conclusion:

If you’re looking for a comprehensive course that helps you design and architect a Veeam solution in a real-world environment, then this is the ideal course for you.

To enroll, contact P2L today!

What is Veeam®?

As per Global Security Mag, Veeam delivers Backup as a Service (BaaS) and Disaster Recovery as a Service (DRaaS) to the market thanks to partnerships with leading cloud and managed service providers in over 180 countries. To ensure these services are seamlessly integrated into V11, NEW Veeam Service Provider Console v5 offers service providers a web-based platform for centralized management, monitoring, and customer self-service access of data protection operations. Version 5 now features expanded backup management for Linux and Mac, monitoring and reporting cloud-native AWS and Azure backups, enhanced security with multi-factor authentication (MFA), and powerful insider protection services.

What is Veeam Availability Suite™ v11?

As per Global Security Mag, New Veeam Availability Suite™ v11 combines the expansive backup and recovery features of Veeam Backup & Replication v11 with the monitoring, reporting, and analytics capabilities of Veeam ONE™ v11, offering businesses complete data protection and visibility enabling customers to achieve unparalleled data availability, visibility, and governance across multi-cloud environments. Furthermore, adding Veeam DR Pack, which includes Veeam Disaster Recovery Orchestrator (formerly Veeam Availability Orchestrator), to a new or previous purchase of either Veeam Availability Suite or Veeam Backup & Replication provides site recovery automation and DR testing to ensure business continuity.

What is the Veeam Availability Suite™ v11 course all about?

The Veeam® Availability Suite™ v11: Configuration and Management training course is a technical deep-dive focused on teaching IT professionals the skills to configure, manage and support a Veeam Availability Suite v11 solution. With extensive hands-on labs, the class enables administrators and engineers to effectively manage data in an ever-changing technical and business environment, bringing tangible benefits to businesses in the digital world.

What is the duration of the Veeam Availability Suite™ v11 course?

The course is three days long.

Skills Gained:

After completing this course, attendees should be able to:

• Describe Veeam Availability Suite components usage scenarios and relevance to your environment.
• Effectively manage data availability in on-site, off-site, cloud, and hybrid environments.
• Ensure both Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs) are met.
• Configure Veeam Availability Suite to ensure data is protected effectively.
• Adapt with an organization’s evolving technology and business data protection needs.
• Ensure recovery is possible, effective, efficient, secure, and compliant with business requirements.
• Provide visibility of the business data assets, reports, and dashboards to monitor performance and risks.

Target audience:

This course is suitable for anyone responsible for configuring, managing, or supporting a Veeam Availability Suite v11 environment.

Prerequisites:

Students should be experienced professionals with solid knowledge of servers, storage, networking, and virtualization.

• Recommended: Veeam Availability Suite

Course Details:

• Introduction
• Building backup capabilities
• Building replication capabilities
• Secondary backups
• Advanced repository capabilities
• Protecting data in the cloud
• Restoring from backup
• Recovery from replica
• Testing backup and replication
• Veeam Backup Enterprise Manager and Veeam ONE
• Configuration backup

Conclusion:

With most organizations adopting multi-cloud ecosystems and workers increasingly operating remotely, it has become harder to manage and control data than ever before. To ease the process, formulate a successful backup strategy, and create, modify, optimize, and delete backup jobs, opt for the newly modified Veeam Availability Suite™ v11.

If you’re looking for a course that helps you understand the functions of Veeam Availability Suite™ v11, then this is the perfect one for you.

To enroll, contact P2L today!