I am studying for my AWS Cloud Practitioner Certification and I am confused with the difference between AWS Lambda & AWS Elastic Beanstalk. From my understanding, for both services you upload your code to AWS and AWS essentially manages the underlying infrastructure for you.
I know with Lambda you upload your code to a 'Lambda Function' and set triggers for when the code executes.
With AWS EB you upload your application code and EB automatically handles the deployment, capacity, provisioning, etc...
They both sound very similar as you upload your code to both and both handle underlying instances/environments.
Thanks!
Elastic beanstalk and lambda are very different though some of the features may look similar. At high level, elastic beanstalk deploys a long running application whereas lambda deploys short running code function
Lambda can at maximum run for 15 minutes, whereas EB can run continuously. Generally, we deploy websites/apps on EB whereas lambda are generally used for triggered functionality like processing image when image gets uploaded to S3.
Lambda can only handle one request at a time whereas number of concurrent requests EB can handle depends on your underlying infrastructure. So, if you are having say 100 requests, 100 lambdas will be created whereas these 100 requests can be handled by one underlying EC2 instance in EB
Lambda is serverless (underlying infra is entirely abstracted from developer). Whereas EB is automation over infra provisioning. You can still see your EC2 instances, load balancer, auto scaling group etc. in your AWS console. You can even ssh/rdp to your instance and change running services. AWS EB allows you also to have your custom AMIs.
Lambda is having issue of cold starts as in lambda, infra needs to be provisioned on demand by AWS, whereas in EB, you generally have EC2 instances already provisioned to handle your requests.
All great (and exam-specific) points by SmartCoder. If I may add a general ancillary comment:
Wittgenstein said, "In most cases, the meaning of a word is its use." I think this maxim is remarkably apt for software engineering too. In the context of your question, those two AWS services are used for significantly different purposes.
Lambda - Say you developed a photo uploading application with Node.js that uploads some processed images to an S3 bucket. The core logic for this is probably quite straightforward, and it's got a singular, distinct task. Simply take in an image, do some processing and if not for any exception, store it in a bucket. In this case, it's inefficient to waste time spinning up servers, configuring them with a runtime environment, downloading dependencies, maintenance, etc. A literal copy and paste of your code into the Lambda console while setting up a few configurations should get your job done. Plus, you save a lot of money as infrastructure is "provisioned" only when your Node.js function is invoked. Again, keep in mind the principle of this code performing a singular task.
Elastic Beanstalk - This same photo uploading system mentioned above might now mature into a more complex full-fledged software application that requires user management, authentication, and further processing of the images, which certainly requires more provisioning of resources. This application will probably do a lot of things with multiple code repositories for you to manage and deploy. And yet, you don't want to spend money on a DevOps engineer or learn to use an IaC (Infrastructure as Code) platform like CloudFormation or Terraform. In this case, Elastic Beanstalk is useful for a developer without too much in-depth DevOps knowledge as it's a PaaS (Platform as a Service) tool; it pretty much gives you a clear interface to spin up whole new production-ready systems.
Here are two good whitepapers I read a while back on the above topics.
https://docs.aws.amazon.com/whitepapers/latest/serverless-architectures-lambda/serverless-architectures-lambda.pdf
https://docs.aws.amazon.com/whitepapers/latest/introduction-devops-aws/introduction-devops-aws.pdf
Lambda is run based on specific trigger events.. and it exits as soon as its work is over.
I'm executing a Flink Job with this tools.
I think both can do exactly the same with the proper configuration. Does Kinesis Data Analytics do something that EMR can not do or vice versa?
Amazon Kinesis Data Analytics is the easiest way to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time.
Amazon Elastic Map Reduce provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances. You can also run other popular distributed frameworks such as Apache Spark, HBase, Presto, and Flink in EMR.
The major difference is maintainability and management from your side.
If you want more independent management and more control then I would say go for AWS EMR. Where its your responsibility to manage the EMR infrastructure as well as the Apache Flink cluster in it.
But if you want less control and more focus on application development and you need to deliver faster(tight deadline) then KDA is the way to go. Here AWS provides all the bells and whistles you need for running your application. This also easily sets up with AWS s3 as code source and provides a bare minimum Configuration Management using the UI.
It scales automatically as well.(Need to understand KCU though).
It provides the same Flink dashboard where you can monitor your application and AWS Cloudwatch integration for debugging your application.
Please go through this nice presentation and let me know it that helps.
Please let me know.
https://www.youtube.com/watch?v=c_LswkrwOvk
I will say one major difference between the two is that Kinesis does not provide a hosted Hadoop service unlike Elastic MapReduce (now EMR)
Got this same question also. This video was helpful in explaining with a real architecture scenario and AWS explanation here tries to explain how Kinesis and EMR can fit together with possible use cases.
I want to clear big picture about the aws Glue regarding some of the following aspects.
How AWS Glue prepare and provision its infrastructure? However it's serverless but how does it manage it?
How it's using apache spark and hadoop to solve so many ETL jobs at a time, Almost jobs of hundreds of AWS Glue customers from every region.
Thanks
AWS Glue uses EMR underneath. It spawns a new cluster with required number of executors (depending on configured DPU) when a new job starts. However, to improve cold start time they have a buffer of already provisioned EMR clusters for the most common number of DPUs. To manage all this they have a set of automated services that monitor state of each cluster, start a new ones etc.
We have a long running EMR cluster that has multiple libraries installed on it using bootstrap actions. Some of these libraries are under continuous development and their codebase is on GitHub.
I've been looking to plug Travis CI with AWS EMR in a similar way to Travis and CodeDeploy. The idea is to get the code on GitHub tested and deployed automatically to EMR while using bootstrap actions to install the updated libraries on all EMR's nodes.
A solution I came up with is to use an EC2 instance in the middle, where Travis and CodeDeploy can be first used to deploy the code on the instance. After that a lunch script on the instance is triggered to create a new EMR cluster with the updated libraries.
However, the above solution means we need to create a new EMR cluster every time we deploy a new version of the system
Any other suggestions?
You definitely don't want to maintain an EC2 instance to orchestrate a CI/CD process like that. First of all, it introduces a number of challenges because then you need to deal with an entire server instance, keep it maintained, deal with networking, apply monitoring and alerts to deal with availability issues, and even then, you won't have availability guarantees, which may cause other issues. Most of all, maintaining an EC2 instance for a purpose like that simply is unnecessary.
I recommend that you investigate using Amazon CodePipeline with a Lambda Step Function.
The Step Function can be used to orchestrate the provisioning of your EMR cluster in a fully serverless environment. With CodePipeline, you can setup a web hook into your Github repo to pull your code and spin up a new deployment automatically whenever changes are committed to your master Github branch (or whatever branch you specify). You can use EMRFS to sync an S3 bucket or folder to your EMR file system for your cluster and then obtain the security benefits of IAM, as well as additional consistency guarantees that come with EMRFS. With Lambda, you also get seamless integration into other services, such as Kinesis, DynamoDB, and CloudWatch, among many others, that will simplify many administrative and development tasks, as well as enable you to have more sophisticated automation with minimal effort.
There are some great resources and tutorials for using CodePipeline with EMR, as well as in general. Here are some examples:
https://aws.amazon.com/blogs/big-data/implement-continuous-integration-and-delivery-of-apache-spark-applications-using-aws/
https://docs.aws.amazon.com/codepipeline/latest/userguide/tutorials-ecs-ecr-codedeploy.html
https://chalice-workshop.readthedocs.io/en/latest/index.html
There are also great tutorials for orchestrating applications with Lambda Step Functions, including the use of EMR. Here are some examples:
https://aws.amazon.com/blogs/big-data/orchestrate-apache-spark-applications-using-aws-step-functions-and-apache-livy/
https://aws.amazon.com/blogs/big-data/orchestrate-multiple-etl-jobs-using-aws-step-functions-and-aws-lambda/
https://github.com/DavidWells/serverless-workshop/tree/master/lessons-code-complete/events/step-functions
https://github.com/aws-samples/lambda-refarch-imagerecognition
https://github.com/aws-samples/aws-serverless-workshops
In the very worst case, if all of those options fail, such as if you need very strict control over the startup process on the EMR cluster after the EMR cluster completes its bootstrapping, you can always create a Java JAR that is loaded as a final step and then use that to either execute a shell script or use the various Amazon Java libraries to run your provisioning commands. In even this case, you still have no need to maintain your own EC2 instance for orchestration purposes (which, in my opinion, still would be hard to justify even if it was running in a Docker container in Kubernetes) because you can easily maintain that deployment process as well with a fully serverless approach.
There are many great videos from the Amazon re:Invent conferences that you may want to watch to get a jump start before you dive into the workshops. For example:
https://www.youtube.com/watch?v=dCDZ7HR7dms
https://www.youtube.com/watch?v=Xi_WrinvTnM&t=1470s
Many more such videos are available on YouTube.
Travis CI also supports Lambda deployment, as mentioned here: https://docs.travis-ci.com/user/deployment/lambda/
I am using Google Data Flow to implement an ETL data ware house solution.
Looking into google cloud offering, it seems DataProc can also do the same thing.
It also seems DataProc is little bit cheaper than DataFlow.
Does anybody know the pros / cons of DataFlow over DataProc
Why does google offer both?
Yes, Cloud Dataflow and Cloud Dataproc can both be used to implement ETL data warehousing solutions.
An overview of why each of these products exist can be found in the Google Cloud Platform Big Data Solutions Articles
Quick takeaways:
Cloud Dataproc provides you with a Hadoop cluster, on GCP, and access to Hadoop-ecosystem tools (e.g. Apache Pig, Hive, and Spark); this has strong appeal if you are already familiar with Hadoop tools and have Hadoop jobs
Cloud Dataflow provides you with a place to run Apache Beam based jobs, on GCP, and you do not need to address common aspects of running jobs on a cluster (e.g. Balancing work, or Scaling the number of workers for a job; by default, this is automatically managed for you, and applies to both batch and streaming) -- this can be very time consuming on other systems
Apache Beam is an important consideration; Beam jobs are intended to be portable across "runners," which include Cloud Dataflow, and enable you to focus on your logical computation, rather than how a "runner" works -- In comparison, when authoring a Spark job, your code is bound to the runner, Spark, and how that runner works
Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values
Here are three main points to consider while trying to choose between Dataproc and Dataflow
Provisioning
Dataproc - Manual provisioning of clusters
Dataflow - Serverless. Automatic provisioning of clusters
Hadoop Dependencies
Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem.
Portability
Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.
This flowchart from the google website explains how to go about choosing one over the other.
https://cloud.google.com/dataflow/images/flow-vs-proc-flowchart.svg
Further details are available in the below link
https://cloud.google.com/dataproc/#fast--scalable-data-processing
Same reason as why Dataproc offers both Hadoop and Spark: sometimes one programming model is the best fit for the job, sometimes the other. Likewise, in some cases the best fit for the job is the Apache Beam programming model, offered by Dataflow.
In many cases, a big consideration is that one already has a codebase written against a particular framework, and one just wants to deploy it on the Google Cloud, so even if, say, the Beam programming model is superior to Hadoop, someone with a lot of Hadoop code might still choose Dataproc for the time being, rather than rewriting their code on Beam to run on Dataflow.
The differences between Spark and Beam programming models are quite large, and there are a lot of use cases where each one has a big advantage over the other. See https://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison .
Cloud Dataflow is a serverless data processing service that runs jobs written using the Apache Beam libraries. When you run a job on Cloud Dataflow, it spins up a cluster of virtual machines, distributes the tasks in your job to the VMs, and dynamically scales the cluster based on how the job is performing. It may even change the order of operations in your processing pipeline to optimize your job.
So use cases are ETL (extract, transfer, load) job between various data sources / data bases. For example load big files from Cloud Storage into BigQuery.
Streaming works based on subscription to PubSub topic, so you can listen to real time events (for example from some IoT devices) and then further process.
Interesting concrete use case of Dataflow is Dataprep. Dataprep is cloud tool on GCP used for exploring, cleaning, wrangling (large) datasets. When you define actions you want to do with your data (like formatting, joining etc), job is run under the hood on Dataflow.
Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values.
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.
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.
If you want to migrate from your existing Hadoop/Spark cluster to the cloud, or take advantage of so many well-trained Hadoop/Spark engineers out there in the market, choose Cloud Dataproc; if you trust Google's expertise in large scale data processing and take their latest improvements for free, choose DataFlow.
Here are three main points to consider while trying to choose between Dataproc and Dataflow
Provisioning
Dataproc - Manual provisioning of clusters
Dataflow - Serverless. Automatic provisioning of clusters
Hadoop Dependencies
Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem.
Portability
Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.
Cloud Dataproc and Cloud Dataflow can both be used for data processing, and there’s overlap in their batch and streaming capabilities. You can decide which product is a better fit for your environment.
Cloud Dataproc is good for environments dependent on specific Apache big data components:
- Tools/packages
- Pipelines
- Skill sets of existing resources
Cloud Dataflow is typically the preferred option for green field environments:
- Less operational overhead
- Unified approach to development of batch or streaming pipelines
- Uses Apache Beam
- Supports pipeline portability across Cloud Dataflow, Apache Spark, and Apache Flink as runtimes.
See more details here https://cloud.google.com/dataproc/
Pricing comparision:
DataProc
Dataflow
If you want to calculate and compare cost of more GCP resources, please refer this url https://cloud.google.com/products/calculator/
One of the other important difference is:
Cloud Dataproc:
Data mining and analysis in datasets of known size
Cloud Dataflow:
Manage datasets of unpredictable size
see
Cloud Dataflow
Is a serverless data processing service that runs jobs written using
the Apache Beam libraries.
When you run a job on Cloud Dataflow it gets operated like this:
It spins up a cluster of virtual machines
Distributes the tasks in your job to the VMs, and dynamically scale the cluster based on how the job is performing
Dataflow may even change the order of operations in your processing pipeline to optimize your job.
It supports both batch and streaming Jobs. So use cases are ETL (extract, transfer, load) jobs between various data sources/databases.
For example, load big files from Cloud Storage into Big Query.
Streaming works based on subscription to Pub-Sub topic, so you can listen to real-time events (for example from some IoT devices) and then further process the data.
An interesting concrete use case of Dataflow is Data prep.
Data prep is a cloud tool on GCP used for exploring, cleaning, and wrangling (large) datasets. When you define the actions you want to perform on your data (like formatting, joining etc.), the job run under the hood on Dataflow.
Cloud Dataflow also offers the ability to create jobs based on "templates" which can help simplify common tasks where the differences are parameter values.
Data proc
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.
Data proc 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.