which of these is a managed spark and hadoop service that lets you benefit from open source data tools for batch processing, querying, streaming, and machine learning?

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What is Dataproc?

Dataproc Documentation Guides Was this helpful?

What is Dataproc?

Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.

Why use Dataproc?

When compared to traditional, on-premises products and competing cloud services, Dataproc has a number of unique advantages for clusters of three to hundreds of nodes:

Low cost — Dataproc is priced at only 1 cent per virtual CPU in your cluster per hour, on top of the other Cloud Platform resources you use. In addition to this low price, Dataproc clusters can include preemptible instances that have lower compute prices, reducing your costs even further. Instead of rounding your usage up to the nearest hour, Dataproc charges you only for what you really use with second-by-second billing and a low, one-minute-minimum billing period.Super fast — Without using Dataproc, it can take from five to 30 minutes to create Spark and Hadoop clusters on-premises or through IaaS providers. By comparison, Dataproc clusters are quick to start, scale, and shutdown, with each of these operations taking 90 seconds or less, on average. This means you can spend less time waiting for clusters and more hands-on time working with your data.Integrated — Dataproc has built-in integration with other Google Cloud Platform services, such as BigQuery, Cloud Storage, Cloud Bigtable, Cloud Logging, and Cloud Monitoring, so you have more than just a Spark or Hadoop cluster—you have a complete data platform. For example, you can use Dataproc to effortlessly ETL terabytes of raw log data directly into BigQuery for business reporting.Managed — Use Spark and Hadoop clusters without the assistance of an administrator or special software. You can easily interact with clusters and Spark or Hadoop jobs through the Google Cloud console, the Cloud SDK, or the Dataproc REST API. When you're done with a cluster, you can simply turn it off, so you don’t spend money on an idle cluster. You won’t need to worry about losing data, because Dataproc is integrated with Cloud Storage, BigQuery, and Cloud Bigtable.Simple and familiar — You don’t need to learn new tools or APIs to use Dataproc, making it easy to move existing projects into Dataproc without redevelopment. Spark, Hadoop, Pig, and Hive are frequently updated, so you can be productive faster.

What is included in Dataproc?

For a list of the open source (Hadoop, Spark, Hive, and Pig) and Google Cloud Platform connector versions supported by Dataproc, see the Dataproc version list.

Getting Started with Dataproc

To quickly get started with Dataproc, see the Dataproc Quickstarts. You can access Dataproc in the following ways:

Through the REST API

Using the Cloud SDK

Using the Dataproc UI

Through the Cloud Client Libraries

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All you need to know about Google Cloud Dataproc

If you are using Hadoop ecosystem and want to make it easier to manage then Dataproc is the tool to checkout. Dataproc automation helps you create clusters quickly, manage them easily, and save money…

BEST CHEATSHEET TO ANSWER “WHAT IS DATAPROC?”

All you need to know about Google Cloud Dataproc

BEST CHEATSHEET TO ANSWER “WHAT IS DATAPROC?” All you need to know about Google Cloud Dataproc Managed Hadoop & Spark #GCPSketchnote

If you are using Hadoop ecosystem and want to make it easier to manage then Dataproc is the tool to checkout.

Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning.

Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don’t need them. With less time and money spent on administration, you can focus on what matters the most — your DATA!

In this video I summarize the what Dataproc offers in 2 mins.

In this video I summarize the what Dataproc offers in 2 mins. #GCPSketchnote

Erin and Sam are part of growing data science team using Apache Hadoop ecosystem and are dealing with operational inefficiencies! So, they are looking at Dataproc which installs a Hadoop cluster in 90 seconds, making it simple, fast and cost effective to gain insights as compared to a traditional cluster management activities. It supports:

Open source tools — Hadoop, Spark ecosystem

Customizable virtual machines that scale up and down as needed

On demand ephemeral clusters to save cost

Tightly integrates with other Google Cloud services.

To move you Hadoop/Spark jobs, all you do is copy your data into Google Cloud Storage, update your file paths from HDFS to GS and you are are ready!

Dataproc cheatsheet #GCPSketchnote

Brief explanation of how does Dataproc works:

It disaggregates storage & compute. Say an external application is sending logs that you want to analyze, you store them in a data source. From Cloud Storage(GCS) the data is used by Dataproc for processing which then stores it back into GCS, BigQuery or Bigtable. You could also use the data for Analysis in a notebook and send logs to Cloud Monitoring and Logging.

Since storage is separate, for a long-lived cluster you could have one cluster per job but to save cost you could use ephemeral clusters that are grouped and selected by labels. And finally, you can also use the right amount of memory, CPU and Disk to fit the needs of your application.

sumber : medium.com

Google Cloud Dataproc

Google Cloud Dataproc is a managed Spark and Hadoop service for batch processing, querying, streaming, and machine learning.

Google Cloud Dataproc

AUGUST 1, 2021 ~ LAST UPDATED ON : AUGUST 12, 2021 ~ JAYENDRAPATIL

Table of Contents hide

Google Cloud Dataproc

Dataproc Cluster High Availability

Dataproc Cluster Scaling

Dataproc Cluster Autoscaling

Dataproc Workers

Dataproc Initialization Actions

Dataproc Cloud Storage Connector

Cloud Dataproc vs Dataflow

GCP Certification Exam Practice Questions

Google Cloud Dataproc

Cloud Dataproc is a managed Spark and Hadoop service that lets you take advantage of open-source data tools for batch processing, querying, streaming, and machine learning.

Dataproc automation helps to create clusters quickly, manage them easily, and save money by turning clusters on and off as needed.

Dataproc helps reduce time on time and money spent on administration and lets you focus on your jobs and your data.

Dataproc clusters are quick to start, scale, and shutdown, with each of these operations taking 90 seconds or less, on average

Dataproc has built-in integration with other GCP services, such as BigQuery, Cloud Storage, Bigtable, Cloud Logging, and Monitoring

Dataproc clusters support preemptible instances that have lower compute prices to reduce costs further.

Dataproc supports connectors for BigQuery, Bigtable, Cloud Storage

Dataproc also supports Anaconda, HBase, Flink, Hive WebHcat, Druid, Jupyter, Presto, Solr, Zepplin, Ranger, Zookeeper, and much more.

Dataproc Cluster High Availability

Dataproc cluster can be configured for High Availability by specifying the number of master instances in the cluster

Dataproc supports two master configurations:

Single Node Cluster – 1 master – 0 Workers (default, non HA)

provides one node for both master and worker

if the master fails, the in-flight jobs will necessarily fail and need to be retried, and HDFS will be inaccessible until the single NameNode fully recovers on reboot.

High Availability Cluster – 3 masters – N Workers (Hadoop HA)

HDFS High Availability and YARN High Availability are configured to allow uninterrupted YARN and HDFS operations despite any single-node failures/reboots.

All nodes in a High Availability cluster reside in the same zone. If there is a failure that impacts all nodes in a zone, the failure will not be mitigated.

Dataproc Cluster Scaling

Dataproc cluster can be adjusted to scale by increasing or decreasing the number of primary or secondary worker nodes (horizontal scaling)

Dataproc cluster can be scaled at any time, even when jobs are running on the cluster.

Machine type of an existing cluster (vertical scaling) cannot be changed. To vertically scale, create a cluster using a supported machine type, then migrate jobs to the new cluster.

Dataproc cluster can help scale

to increase the number of workers to make a job run faster

to decrease the number of workers to save money

to increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage

Dataproc Cluster Autoscaling

Dataproc Autoscaling provides a mechanism for automating cluster resource management and enables cluster autoscaling.

An Autoscaling Policy is a reusable configuration that describes how clusters using the autoscaling policy should scale.

It defines scaling boundaries, frequency, and aggressiveness to provide fine-grained control over cluster resources throughout cluster lifetime.

Autoscaling is recommended for

on clusters that store data in external services, such as Cloud Storage

on clusters that process many jobs

to scale up single-job clusters

Autoscaling is not recommended with/for:

HDFS: Autoscaling is not intended for scaling on-cluster HDFS

YARN Node Labels: Autoscaling does not support YARN Node Labels. YARN incorrectly reports cluster metrics when node labels are used.

Spark Structured Streaming: Autoscaling does not support Spark Structured Streaming

Idle Clusters: Autoscaling is not recommended for the purpose of scaling a cluster down to minimum size when the cluster is idle. It is better to delete an Idle cluster.

Dataproc Workers

Primary workers are standard Compute Engine VMs

Secondary workers can be used to scale with the below limitations

Processing only

Secondary workers do not store data.

can only function as processing nodes

useful to scale compute without scaling storage.

No secondary-worker-only clusters

Cluster must have primary workers

Dataproc adds two primary workers to the cluster, by default, if no primary workers are specified.

Machine type

use the machine type of the cluster’s primary workers.

Persistent disk size

are created, by default, with the smaller of 100GB or the primary worker boot disk size.

This disk space is used for local caching of data and is not available through HDFS.

Asynchronous Creation

Dataproc manages secondary workers using Managed Instance Groups (MIGs), which create VMs asynchronously as soon as they can be provisioned

Dataproc Initialization Actions

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Dataproc supports initialization actions in executables or scripts that will run on all nodes in the cluster immediately after the cluster is set up

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