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O’Reilly Report: Moving Hadoop to the Cloud [Whitepaper] - Talend
Mar 08, · Download ZIP Launching GitHub Desktop. If nothing happens, download GitHub Desktop and try again. Field Guide to blogger.com HBase in blogger.com HDInsight Essentials - Second blogger.com Hadoop - the Definitive blogger.com Moving Hadoop to the Cloud - Harnessing Cloud Features and Flexibility for Hadoop Clusters - Early Release(1 Jan 01, · Moving Hadoop to the Cloud Pdf Harnessing Cloud Features and Flexibility for Hadoop Clusters. Up until recently, Hadoop deployments have existed on hardware owned and run by organizations, often alongside legacy "big-iron" hardware Jun 08, · This guide provides an overview of how to move your on-premises Apache Hadoop system to Google Cloud. It describes a migration process that not only moves your Hadoop work to Google Cloud, but also enables you to adapt your work to take advantage of the benefits of a Hadoop system optimized for cloud computing
Moving hadoop to the cloud pdf download
This guide provides an overview of how to move your on-premises Apache Hadoop system to Google Cloud. It describes a migration process that not only moves your Hadoop work to Google Cloud, moving hadoop to the cloud pdf download, but also enables you to adapt your work to take advantage of the benefits of a Hadoop system optimized for cloud computing.
It also introduces some fundamental moving hadoop to the cloud pdf download you need to understand to translate your Hadoop configuration to Google Cloud. There are many ways in which using Google Cloud can save you time, money, and effort compared to using an on-premises Hadoop solution. In many cases, moving hadoop to the cloud pdf download, adopting a cloud-based approach can make your overall solution simpler and easy to manage. Google Cloud includes Dataproc, which is a managed Hadoop and Spark environment.
You can use Dataproc to run most of your existing jobs with minimal alteration, so you don't need to move away from all of the Hadoop tools you already know. When you run Hadoop on Google Cloud, you never need to worry about physical hardware.
You specify the configuration of your cluster, and Dataproc allocates resources for you. You can scale your cluster at any time. Keeping open source tools up to date and working together is one of the most complex parts of managing a Hadoop cluster.
When you use Dataproc, much of that work is managed for you by Dataproc versioning. A typical on-premises Hadoop setup uses a single cluster that serves many purposes.
When you move to Google Cloud, you can focus on individual tasks, creating as many clusters as you need. This removes much of the complexity of maintaining a single cluster with growing dependencies and software configuration interactions. Migrating from an on-premises Hadoop solution to Google Cloud requires a shift in approach.
A typical on-premises Hadoop system consists of a monolithic cluster that supports many workloads, often across multiple business areas. As a result, the system becomes more complex over time and can require administrators to make compromises to get everything working in the monolithic cluster. When you move your Hadoop system to Google Cloud, you can reduce the administrative complexity.
However, to get that simplification and to get the most efficient processing in Google Cloud with the minimal cost, you need to rethink how to structure your data and jobs. Because Dataproc runs Hadoop on Google Cloud, using a persistent Dataproc cluster to replicate your on-premises setup might seem like the easiest solution. However, there are some limitations to that approach:. The most cost-effective and flexible way to migrate your Hadoop system to Google Cloud is to shift away from thinking in terms of large, multi-purpose, persistent clusters and instead think about small, short-lived clusters that moving hadoop to the cloud pdf download designed to run specific jobs.
You store your data in Cloud Storage to support multiple, temporary processing clusters. This model is often called the ephemeral modelmoving hadoop to the cloud pdf download, because the clusters you use for processing jobs are allocated as needed and are released as jobs finish. The following diagram shows a hypothetical migration from an on-premises system to an ephemeral model on Google Cloud. The example moves four jobs that run on two on-premises clusters to Dataproc.
The ephemeral clusters that are used to run the jobs in Google Cloud are defined to maximize efficiency for individual jobs.
The first two jobs moving hadoop to the cloud pdf download the same cluster, moving hadoop to the cloud pdf download, while the third and fourth jobs each run on their own cluster. When you migrate your own jobs, you can customize and optimize clusters for individual jobs or for groups of jobs as makes sense for your specific work.
Dataproc helps you quickly define multiple clusters, bring them online, and scale them to suit your needs, moving hadoop to the cloud pdf download. The data in the example is moved from two on-premises HDFS clusters to Cloud Storage buckets. The data in the first cluster is divided among multiple buckets, and the second cluster is moved to a single bucket.
You can customize the structure of your data in Cloud Storage to suit the needs of your applications and your business. The example migration captures the beginning and ending states of a complete migration to Google Cloud. This implies a single step, but you'll get the best results if you don't think of moving to Google Cloud as a one-time, complete migration.
Instead, think of it as refactoring your solutions to use a new set of tools in ways that weren't possible on-premises. To make such a refactoring work, we recommend migrating incrementally. The biggest shift in your approach between running an on-premises Hadoop workflow and running the same workflow on Google Cloud is the shift away from monolithic, persistent clusters to specialized, ephemeral clusters. You spin up a cluster when you need to run a job and then delete it when the job completes.
The resources required by your jobs are active only when they're being used, so you only pay for what you use. This approach enables you to tailor cluster configurations for individual jobs.
Because you aren't maintaining and configuring a persistent cluster, you reduce the costs of resource use and cluster administration. With your data stored persistently in Cloud Storage, you can run your jobs on ephemeral Hadoop clusters managed by Dataproc. In some cases, it might make more sense to move data to another Google Cloud technology, such as BigQuery or Cloud Bigtable. But most general-purpose data should persist in Cloud Storage.
More detail about these alternative storage options is provided later in this guide. Dataproc makes it easy to create and moving hadoop to the cloud pdf download clusters so that you can move away from using one monolithic cluster to using many ephemeral clusters. This approach has several advantages:. You can use Dataproc initialization actions to define the configuration of nodes in a cluster. This makes it easy to maintain the different cluster configurations you need to closely support individual jobs and related groups of jobs, moving hadoop to the cloud pdf download.
You can use the provided sample initialization actions to get started. The samples demonstrate how to make your own initialization actions. The point of ephemeral clusters is to use them only for the jobs' lifetime. When it's time to run a job, follow this process:. If you can't accomplish your work without a persistent cluster, you can create one. This option may be costly and isn't recommended if there is a way to get your job done on ephemeral clusters.
Your migration is unique to your Hadoop environment, so there is no universal plan that fits all migration scenarios. Make a plan for your migration that gives you the freedom to translate each piece to a cloud-computing paradigm. Start with the least critical data that you can. In the early stages, it's a good approach to use backup data and archives.
One kind of lower-risk job that makes a good starting test is moving hadoop to the cloud pdf download by running burst processing on archive data. You can set up jobs that fill gaps in processing for data that existed before your current jobs were in place. Starting with burst jobs can often provide experience with scaling on Google Cloud early in your migration plan. This can help you when you begin migrating more critical jobs, moving hadoop to the cloud pdf download.
The example has two major components. First, scheduled jobs running on the on-premises cluster push data to Cloud Storage through an internet gateway. Second, backfill jobs run on ephemeral Dataproc clusters.
In addition to backfilling, you can use ephemeral clusters in Google Cloud for experimentation and creating proofs of concept for future work. So far, this guide has assumed that your goal is to move your entire Hadoop system from on-premises to Google Cloud. A Hadoop system running entirely on Google Cloud is easier to manage than one that operates both in the cloud and on-premises.
However, a hybrid approach is often necessary to meet your business or technological needs. An issue you must address with a hybrid cloud solution is how to keep your systems in sync.
That is, how will you make sure that changes you make to data in one place are reflected in the other? You can simplify synchronization by making clear distinctions between how your data is used in the different environments. For example, you might have a hybrid solution where only your archived data is stored in Google Cloud. You can set up scheduled jobs to move your data from the on-premises cluster to Google Cloud when the data reaches a specified age. Then you can set up moving hadoop to the cloud pdf download of your jobs that work on the archived data in Google Cloud so that you never need to synchronize changes to your on-premises clusters.
Another way to divide your system is to move all of the data and work for a specific project or working group to Google Cloud while keeping other work on premises. Then you can focus on your jobs instead of creating complex data synchronization systems. You might have security or logistical concerns that complicate how you connect your on-premises cluster to Google Cloud.
One solution is to use a Virtual Private Cloud connected to your on-premises network using Cloud VPN. The example setup uses Cloud VPN to connect a Google Cloud VPC to an on-premises cluster. The system uses Dataproc inside the VPC to manage persistent clusters that process data coming from the on-premises system.
This might involve synchronizing data between the systems. Those persistent Dataproc clusters also transfer data coming from the on-premises system to moving hadoop to the cloud pdf download appropriate storage services in Google Cloud.
For the sake of illustration, the example uses Cloud Storage, BigQuery, and Bigtable for storage—those are the most common destinations for data processed by Hadoop workloads in Google Cloud. The other half of the example solution shows multiple ephemeral clusters that are created as needed in the public cloud. Those clusters can be used for many tasks, including those that collect and transform new data. The results of these jobs are stored in the same storage services used by the clusters running in the VPC.
By contrast, moving hadoop to the cloud pdf download, a cloud-native solution is straightforward. Because you run all of your jobs on Google Cloud using data stored in Cloud Storage, you avoid the complexity of data synchronization altogether, although you must still be careful about how your different jobs use the same data.
The example system has some persistent clusters and some ephemeral ones. Both kinds of clusters share cloud tools and resources, including storage and monitoring. Dataproc uses standardized machine images to define software configurations on VMs in a cluster. You can use these predefined images as the basis for the VM configuration you need. The example shows most of the persistent clusters running on version 1. You can create new clusters with customized VM instances whenever you need them.
This lets you isolate testing and development environments from critical production data and jobs.
Migrate Hadoop to Cloud Webinar
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Mar 08, · Download ZIP Launching GitHub Desktop. If nothing happens, download GitHub Desktop and try again. Field Guide to blogger.com HBase in blogger.com HDInsight Essentials - Second blogger.com Hadoop - the Definitive blogger.com Moving Hadoop to the Cloud - Harnessing Cloud Features and Flexibility for Hadoop Clusters - Early Release(1 Jan 01, · Moving Hadoop to the Cloud Pdf Harnessing Cloud Features and Flexibility for Hadoop Clusters. Up until recently, Hadoop deployments have existed on hardware owned and run by organizations, often alongside legacy "big-iron" hardware Overview. Move Hadoop to the Cloud: Harness Cloud Features and Flexibility for Hadoop Clusters This hands-on guide covers how to architect clusters that work with cloud-provider features. It shows you how to avoid pitfalls, and also take full advantage of these services
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