Aws data pipeline vs emr. AWS Glue vs. n this video, we will guide you through the process of building a robust data pipeline using Amazon EMR, AWS Glue, and Apache Airflow on the AWS cloud platfo Apr 5, 2024 · AWS Data Pipeline is a web service that lets you process and moves data at regular intervals between AWS computing and storage services, as well as on-premises data sources. Oct 7, 2023 · Workflow vs ETL: Data Pipeline is more about defining and scheduling workflows, whereas AWS Glue is designed specifically for ETL tasks. Bitbucket, GitHub, S3). A Redshift Serverless environment. For more information, see IAM roles in the IAM User Guide. Magnitude Angles using this comparison chart. bucket Oct 12, 2021 · For the ETL pipeline in this post, we keep the flow simple; however, you can build a complex flow using different features of Step Functions. Related EMR features include easy provisioning, managed scaling, and reconfiguring of clusters, and EMR Studio for collaborative development. With AWS Data Pipeline, you can regularly access your data where it’s stored, transform and process it at scale, and efficiently transfer the results to AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. EMR. Jobs can launch on a schedule, manually Data Nodes. Minitab Connect in 2024 by cost, reviews, features, integrations, deployment, target market, support options, trial offers, training options, years in business, region, and more using the chart below. Create a streaming data pipeline for real-time ingest (streaming ETL) into data lakes and analytics tools with Amazon Kinesis Data Firehose. Jun 29, 2022 · AWS describes EMR as follows. Structured data generated and processed by legacy on-premises platforms - mainframes and data warehouses. PDF. AWS Data Pipeline gathers the data and creates steps through which data collection is processed on the other hand with Amazon Kinesis you can collectively analyze and process data from a different source. Perform an Athena select query I'm prototyping a basic AWS Data Pipeline architecture where a new file placed inside an S3 Bucket triggers a Lambda that activates a Data Pipeline. Provision clusters in minutes: You can launch an EMR cluster in minutes. Sep 21, 2018 · Additionally, you can use Amazon EMR to transform and move large amounts of data into and out of other AWS data stores and databases, such as Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB. When a schedule's start time is in the past, AWS Data Pipeline backfills your pipeline and begins scheduling runs immediately beginning at the specified start time. Oct 7, 2014 · For information about migrating from AWS Data Pipeline, please refer to the AWS Data Pipeline migration documentation. If you’ve already created a pipeline in this region, the console displays a page that lists your pipelines for the region. AWS Data Pipeline vs. Here are few important things to remember: AWS Data Pipeline can create complex data processing workloads that are fault tolerant, repeatable, and highly Jan 25, 2024 · AWS offers a wide range of serverless technologies for Big Data processing. cores 4 … May 12, 2022 · Modernizing the data pipeline with Amazon EMR 6. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed workflow orchestration service for Apache Airflow that you can use to set up and operate end-to-end data pipelines in the cloud at scale. EMR File System (EMRFS) Using the EMR File System (EMRFS), Amazon EMR extends Hadoop to add the ability to directly access data stored in Amazon S3 as if it were a file system like HDFS. AWS Glue is one of the best ETL tools around, and it is often compared with the Data Pipeline. For more information, see the AWS Glue documentation. SolarWinds Task Factory using this comparison chart. Infrastructure and Scale. It provides the capability to develop complex programmatic workflows with many Learn about AWS Data Pipeline. Oct 26, 2020 · For our final step of the pipeline we can read the parquet data from our newly created table back into a Pandas dataframe. Aug 24, 2021 · In our performance tests using TPC-DS benchmark queries at 3 TB scale, we found EMR runtime for Apache Spark 3. EMR clusters spawn on-demand. On the EMR cluster, the following default values are defined in the Spark configuration file (spark-defaults. Orchestration for parallel ETL processing requires the use of multiple tools to perform a variety of operations. In AWS Data Pipeline, a data node defines the location and type of data that a pipeline activity uses as input or output. Copy CSV Data Between Amazon S3 Buckets Using AWS Data Pipeline. A DynamoDB table that contains data for HiveActivity or EmrActivity to use. In summary, while both tools provide robust orchestration capabilities, Airflow's open-source nature and customization For migrating activities on AWS Data Pipeline managed resources, you can use AWS SDK service integration on Step Functions to automate resource provisioning and cleaning up. This table lists the Amazon EC2 instances that AWS Data Pipeline supports and can create for Amazon EMR clusters, if specified. AWS Glue for ETL - AWS Data Pipeline vs. SqlDataNode. The latter involves managing the EMR clusters, etc. Athena by default uses the Data Catalog as its metastore. Don’t worry to scale-out in no hurry. A data pipeline is a series of processing steps to prepare enterprise data for analysis. 0 provides a 1. It is suitable for a wide range of data processing, including log Nov 4, 2018 · However ,if your aim is only making Spark transformations on your data in AWS environment , to run a Spark job on EMR with “Data Pipeline” ,you have to wait 15 minutes during the spin up of AWS Glue vs Data Pipeline. It enables you to develop fault-tolerant, repeatable, and highly available complex data processing workloads. Mar 30, 2023 · This post showed how to build an event-driven data pipeline purely with native Kubernetes API and tooling. Apache Livy is a service that enables easy interaction with a Spark cluster over a REST interface. Oct 31, 2023 · Apache Airflow and Snowflake have emerged as powerful technologies for data management and analysis. To scale up the storage of core and task nodes on your cluster, use this bootstrap action script. With EMR Serverless, we could run applications built using open-source frameworks such as Apache Spark (as in our case) without having to configure, manage, optimize, or secure clusters. An AWS Glue environment, which contains the following: An AWS Glue crawler, which crawls the data from the S3 source bucket sample-inp-bucket-etl-<username> in Account A. AWS Data Pipeline helps you easily create complex data processing workloads that are fault tolerant, repeatable In this video, I gave an overview of what EMR is and its benefits in the big data and machine learning world. For migrating activities on on-premises servers, user-managed EC2 instances, or a user-managed EMR cluster, you can install an SSM agent to the instance. Amazon EMR simplifies building and operating big data environments and applications. It offers automatic code generation for ETL transformations and creates a metadata catalog automatically. If required i can create jar out of that and submit in data pipe line . The Run Job on an Elastic MapReduce Cluster template launches an Amazon EMR cluster based on the parameters provided and starts running steps based on the specified schedule. ETL; AWS間のデータ以降とかであれば、簡単なマウスとキーボードの操作だけで処理を作り、実行できるようなGUIがある; 複雑な変換処理などは自前のプログラムを噛ませる Nov 22, 2021 · A) Databricks vs EMR: Deployment. Sep 27, 2019 · A key difference between AWS Glue vs. AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. AWS Data Pipeline is more concentrated on working with data workflows. AWS Data Pipeline manages the lifecycle of these EC2 instances, launching and terminating them when a job operation is complete. Process Data Using Amazon EMR with Hadoop Streaming. AWS Data Pipeline is a web service for scheduling regular data movement and data processing activities in the AWS cloud. SolarWinds Task Factory in 2024 by cost, reviews, features, integrations, deployment, target market, support options, trial offers, training options, years in business, region, and more using the chart below. To check the script logs, ssh into the node of interest, and check the file /tmp/resize_storage. g. 0 and later. AWS Documentation AWS Data Pipeline Developer Guide. The flow of the pipeline is as follows: Create a database if it doesn’t already exist in the Data Catalog. Currently, there are five primary AWS products for cloud-based analytics: Elastic MapReduce (EMR), Kinesis, Redshift, Data Pipeline and Machine Learning. All the workloads can be deployed to AWS EMR using Amazon EC2 instances and Amazon Elastic Kubernetes Service (EKS). There are four processes that run in the outer state machine pipeline: Step 1 launches an EMR cluster using the CloudFormation template. You don’t need to worry about infrastructure May 17, 2023 · AWS EMR (Elastic MapReduce): AWS EMR is a managed big data processing service that uses Apache Hadoop and Spark to process large volumes of data. Data engineer: Define the AWS Glue Data Catalog for the source. ETLやデータ以降をマネージドでできる. This is usually followed up by data processing jobs using permanently spawned distributed computing clusters like EMR. The pipeline uses EMR on EKS as compute and uses serverless AWS resources Amazon S3, EventBridge, and Step Functions as storage and orchestration in an event-driven architecture. Amazon EMR is the industry-leading cloud big data solution for petabyte-scale data processing, interactive analytics, and machine […] Jan 22, 2024 · This lambda function is in charge of processing this file with some spark jobs submitted in a transient AWS EMR. Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. xlarge EC2 instances (one core and two workers). processing. The Data Pipeline then spawns an EMR Cluster and runs several EmrActivities. Once the job completes, the EMR cluster is terminated. Lessons. AWS Glue lesson from Cloud Academy. scripts. After one of the runs is complete, open the Amazon S3 console and check that the time-stamped output folder exists and contains the expected results of the cluster. AWS provides a fully managed service for Apache Flink through Amazon Kinesis Data Analytics, which enables you to build and run sophisticated streaming applications quickly, easily, and with low operational overhead. If your ETL data-flow changes at a known times and needs still some Feb 16, 2021 · Source: AWS EMR Instances Guidelines Scenarios where it did not make sense to leverage spot instances. EMR simplifies the deployment and management of big data frameworks and enables high-performance processing of large-scale datasets. However, AWS Data Pipeline is a tad bit more rigid when it comes to working with specific types of data. ‘Workload‘ is an application that has a collection of resources and code to derive business values. Zero Time Tolerance/Fault Tolerance: If your data pipeline is time-critical (cannot even Open the Amazon EMR console. However, raw data is useless; it must be moved, sorted, filtered, reformatted, and analyzed for business Nov 16, 2023 · In this era of big data, organizations worldwide are constantly searching for innovative ways to extract value and insights from their vast datasets. We created an Airflow DAG to demonstrate how to run data processing jobs concurrently and extended the DAG to start a Step Functions state machine to build a complex ETL pipeline. AWS Glue is a serverless ETL service that simplifies the ETL (Extract, Transform, Load) process. Paytm is now able to utilize a Spot to On-Demand ratio of May 28, 2020 · AWS Data Workflow Options. Apr 17, 2023 · EMR Serverless is an option in Amazon EMR that makes it easy and cost-effective for data engineers and analysts to run petabyte-scale data analytics in the cloud. If the data format is not supported, then to use that data, you will need to tinker Scalable data lakes. Also Read: AWS Glue Vs. Oct 25, 2019 · This post presented how to use Apache Airflow, Genie, and Amazon EMR to manage big data workflows. After all the operations are performed in Athena, let’s go back to Amazon EMR and confirm that Amazon EMR Spark can consume the updated data. To see the Explorer, choose the EMR icon in the Activity bar. Open the AWS Data Pipeline console. Glue’s Apache Spark environment fully manages scaling, provisioning, and configuration. The post described the architecture components, the use cases the architecture supports, and when to use it. You must create a table in DynamoDB before consuming data in an Amazon Kinesis stream with an Amazon EMR cluster in checkpointed intervals. Jun 17, 2022 · ML Pipeline using State Machine 3. Amazon EMR managed scaling helped reduce the scale-in and scale-out time and increase the usage of Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances for running the Spark jobs. The default role is DataPipelineDefaultRole. Third-party tools also exist to diversify and expand on the AWS analytics portfolio. Will AWS Data pipeline will be helpful in my scenario ? Also i have spark-scala script that i have been running zeppelin . AWS. AWS Data Pipeline is a native AWS service that provides the capability to transform and move data within the AWS ecosystem. Apache Spark offers the scalability and speed needed to process large amounts of data efficiently. Code Generation: AWS Glue includes automatic code Project Description. AWS customers can do everything from process data in real time to implement machine learning for applications. The assumption here, is that the EMR cluster is always on, in an idle state, waiting for a step to be added. You can take advantage of the managed streaming data services offered by Amazon Kinesis, Amazon MSK, Amazon EMR Spark streaming, or deploy and manage your own streaming data solution in the cloud on Amazon Elastic Compute Cloud (Amazon EC2). The first parameter for comparing Databricks vs EMR is the method of deployment. Jul 17, 2018 · Such pipelines often require Spark jobs to be run in parallel on Amazon EMR. Apr 3, 2017 · I understand that I can choose Spark as a bootstrap action as said here, but when I do I get this message: Name: xxx. Jan 11, 2021 · In this post, we used Amazon MWAA to orchestrate an ETL pipeline on Amazon EMR and AWS Glue with Step Functions. AWS Glue is a fully-managed ETL service that provides a serverless Apache Spark environment to run your In this AWS Data Pipeline Cheat Sheet, we will learn the concepts of AWS Data Pipeline. You can use either HDFS or Amazon S3 as the file system in your cluster. Aug 13, 2020 · Typically, we run data pipelines as follows: Data collection at regular time intervals (daily, hourly or by the minute) saved to storage like S3. Supported Amazon EC2 Instances for Amazon EMR Clusters. but provides a lot more orchestration built in. Often, these services have overlapping functionalities, making it challenging to determine the best fit for your specific needs. Otherwise, AWS Data Pipeline attempts to queue and schedule all runs of your pipeline for that interval. 3 helped efficiently scale the system at a lower cost. Period: role: The IAM role passed to Amazon EMR to create EC2 nodes. EMR: Which One is Better? Aug 23, 2023 · In this tutorial we learnt how automate PySpark pipelines using Apache Airflow and AWS EMR in combination. Amazon EMR using this comparison chart. Most often, Amazon S3 is used to store input and output data and intermediate results Data ingestion methods. Note: If you do not have default AWS credentials or AWS_PROFILE environment variable, use the EMR: Select AWS Profile command to select your profile. Jul 21, 2022 · With the use of AWS Data Pipeline, you can access your data where it’s stored, transform and process it at scale, and move the results efficiently to other AWS services – like Amazon RDS, Amazon DynamoDB, Amazon S3, or Amazon EMR. Mar 8, 2019 · AWS Step Functions is a generic way of implementing workflows, while Data Pipelines is a specialized workflow for working with Data. Once created, the SageMaker Pipelines EMR Compare AWS Data Pipeline vs. 7 times performance improvement on average, and up to 8 times improved performance for individual queries over open-source Apache Spark 3. Her career has included teaching about AWS all over the world, creating AWS content that is fun, and working in consulting. This is not uncommon in a professional environment or easily achievable by setting a personal AWS account. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. However, to get the most from this lesson, you should already have an understanding of Amazon EMR and Amazon EC2. What I'm trying to figure out is this. Apache Airflow is an open-source data workflow solution developed by Airbnb and now owned by the Apache Foundation. Organizations have a large volume of data from various sources like applications, Internet of Things (IoT) devices, and other digital channels. I read about AWS data pipeline . Parameters: EC2 key pair (optional): xxx_xxxxxxx_emr_key EMR step (s): spark-submit --deploy-mode cluster s3://xxx. The table must be in the same region as your Amazon EMR cluster. There are a couple of ways we do this. Jan 21, 2022 · The SageMaker Pipeline EMR step requires customers to provide cluster-id of EMR cluster and execution property for EMR job which need to be executed on the cluster. It launches and manages the lifecycle of EMR clusters and EC2 instances to execute your jobs. Copy Data to Amazon Redshift Using AWS Data Pipeline. This step involves creating a database and required tables in the AWS Glue Data Catalog. This article will give you a comprehensive guide to AWS Data In this lesson, I will provide introductory information on AWS Glue. log. Nov 15, 2021 · Extract, transform, and load (ETL) orchestration is a common mechanism for building big data pipelines. For more information on these services, please see our existing content titled: Amazon EMR vs. Amazon EMR makes it easy to set up, operate, and scale your big data environments by automating time-consuming tasks like provisioning The Amazon EMR connector for Amazon Kinesis uses the DynamoDB database as its backing for checkpointing metadata. You will be using the sales dataset. String: runsOn: This field is not allowed on this object. 4. To simplify the orchestration, you can use AWS Glue workflows. Source the Sqoop code to EMR and execute it to move the data to S3. Optional bootstrap actions can be specified to install additional software or to change application configuration on The IAM role that AWS Data Pipeline uses to create the Amazon EMR cluster. Build using a template: Run job on an Elastic MapReduce cluster. Oct 13, 2020 · Diving into the pipelines details. You can use AWS Data Pipeline to manage your Amazon EMR clusters. I then provided a step by step instruction on h Feb 26, 2016 · To process the data using Spark Streaming, create an Amazon EMR cluster in the same AWS region using three m3. Apr 18, 2019 · Data Pipeline. If you haven’t created a pipeline in this region, the console displays an introductory screen. Did this page help you? Explore more of AWS. AWS Glue. executor. In this blog, we will be comparing AWS Data Pipeline and AWS Glue. Dec 29, 2019 · AWS Glue . Kinesis is a platform for streaming data on Feb 21, 2020 · Apache Flink is a framework and distributed processing engine for processing data streams. AWS provides several options to work with streaming data. Jan 24, 2022 · AWS Glue is serverless, so developers have no infrastructure to manage. A core capability of a data lake architecture is the ability to quickly and easily ingest multiple types of data: Real-time streaming data and bulk data assets, from on-premises storage platforms. Analyse sales data using highly competitive technology big data stack such as Amazon S3, EMR , Tableau to derive metrics out of the existing data . Source the Spark code and model into EMR from a repo (e. Export MySQL Data to Amazon S3 Using AWS Data Pipeline. Build and store your data lakes on AWS to gain deeper insights than with traditional data silos and data warehouses allow. With AWS Data Pipeline you can specify preconditions that must be met before the cluster is launched (for example, ensuring that today's data been uploaded to Amazon S3), a schedule for repeatedly running the cluster, and What’s the difference between AWS Data Pipeline, Amazon EMR, and SolarWinds Task Factory? Compare AWS Data Pipeline vs. xxxxxxx. Start learning today with our digital training solutions. One of the most important features of a data lake is for different systems to seamlessly work together through the Iceberg open-source protocol. Execute the code, which transform the data and create output according to the pre-developed Compare AWS Data Pipeline vs. Amazon Web Services are dominating the cloud computing and big data fields alike. 0. Compare price, features, and reviews of the software side-by-side to make the best choice for your business. Data Pipeline is that developers must rely on EC2 instances to execute tasks in a Data Pipeline job, which is not a requirement with Glue. May 25, 2024 · AWS Data Pipeline Task Runner provides logic for common data management scenarios, such as performing database queries and running data analysis using Amazon Elastic MapReduce (Amazon EMR). conf): … spark. . AWS Data Pipeline is a managed service, simplifying deployment but offering less flexibility compared to Airflow. Intermediate. 9. In this lesson, we will compare Amazon EMR and AWS Glue and cover ways to make ETL processes more automated and repeatable. Compute Fundamentals for AWS. AWS Documentation AWS Data Supported Amazon EC2 Instances for Amazon EMR Clusters Pipeline Definition Learn about AWS Data Pipeline Airflow can be deployed on various environments, including Kubernetes, and offers dynamic pipeline generation. For more information, see Supported Instance Types in the Amazon EMR Management Guide. Apr 25, 2024 · data – Any datasets used in the DAG. Essentially, AWS Step Functions is a generic way of designing and executing workflows. Choose Get started now. The name of the workgroup and namespace are prefixed with sample. PDF RSS. 以下のような機能・特徴を持つ. AWS Data Pipeline helps you to process and move data (ETL) between S3, RDS, DynamoDB, EMR, On-premise data sources. AWS Data Pipeline supports the following types of data nodes: DynamoDBDataNode. The AWS Lambda function downloads the template and parameter file from the specified Amazon S3 location and initiates the stack build. Etleap vs. Amazon EMR is a cloud big data platform for running large-scale distributed data processing jobs, interactive SQL queries, and machine learning applications using open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto. String: retryDelay: The timeout duration between two retry attempts. AWS Glue supports various data sources like Redshift, SQL, Amazon RDS, Amazon S3, and DynamoDB. Jan 4, 2018 · Within the Data Pipeline, you can create a job to do below: Launch a ERM cluster with Sqoop and Spark. The spark jobs are the following : step A : run preprocessing pipeline Oct 29, 2015 · Step 1: Create a simple pipeline. Alana Layton is an experienced technical trainer, technical content developer, and cloud engineer living out of Seattle, Washington. In this Big Data project, a senior Big Data Architect will demonstrate how to implement a Big Data pipeline on AWS at scale. AWS Data Pipeline uses AWS Identity and Access Management roles. In the last blog, we discussed the key differences between AWS Glue Vs. ”. Yet its serverless option is not available yet so you need to be mindful about your ETL load, despite AWS having auto-scaling available. For more information on these services, please see our existing content titled: Introduction to Amazon Elastic Map Reduce. “Amazon EMR (previously called Amazon Elastic MapReduce) is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS to process and analyze vast amounts of data. AWS-powered data lakes, supported by the unmatched availability of Amazon S3, can handle the scale, agility, and flexibility required to combine different data and analytics approaches. The Snowflake Data Cloud provides a Jun 16, 2022 · Consume Iceberg data across Amazon EMR and Athena. Say theoretically I have five distinct EMR Activities I need to perform. So the process is step-by-step in the pipeline model and real-time in the Kinesis model. Pros: Processing Jobs run on a schedule. 6/5. Orchestrating ETL Workflows - AWS Data Pipeline vs. This post demonstrates how to accomplish parallel ETL orchestration using AWS Glue workflows […] Jul 15, 2020 · Database Migration Service VS Schema Conversion Tool; AWS Data Pipeline. The Amazon EMR Explorer allows you to browse job runs and steps across EMR on EC2, EMR on EKS, and EMR Serverless. Jan 19, 2018 · Invoking AWS lambda function on S3 event and lambda will create EMR cluster and will do spark-submit . You can use AWS Data Pipeline Task Runner as your task runner, or you can write your own task runner to provide custom data management. AWS Data Pipeline is not serverless like Glue. Claim Amazon EMR and update features and information. Amazon EMR vs. The clusters that were spawned by AWS Data Pipeline have a name formatted as follows: <pipeline-identifier> _@ <emr-cluster-name> _ <launch-time>. The permissions policies attached to IAM roles determine what actions AWS Data Pipeline and your applications can perform, and what AWS resources they can access. You can either use a crawler to catalog the tables in the AWS Glue database, or define them as Amazon Athena external tables. Glue and Data Pipeline are both ETL services, but the former is highly managed and you still need to do a bit of orchestration for a pipeline, but is better at discovery and bringing in new datasets for analysis. instances 2 spark. A good example of this is the comparison between AWS Glue Jobs and EMR Serverless. You can visually build the workflow by wiring individual data pipeline tasks and configuring payloads, retries, […] Dec 21, 2023 · AWS Glue vs Amazon EMR vs AWS Lambda 2)AWS batched ETL pipeline for on-demand analytics. This post focuses on how to submit multiple Spark jobs in parallel on an EMR cluster using Apache Livy, which is available in EMR version 5. This video covers a hands-on example in setting up Batch Processing example using Amazon Data Pipeline which leverages S3 and DynamoDB along with Amazon EMR. Its installation and managing infrastructure overhead are taken care of by AWS. 3m 6s. Airflow is an open-source product. For testing/development, use a relatively short interval. Sagemaker pipelines takes care of establishing a secure connection, submitting the EMR workloads and actively tracking them to completion. AWS Glue lesson from Cloud Tutorials. Provides a managed ETL service that runs on a serverless Apache Spark environment. Dec 18, 2023 · AWS Step Functions is a fully managed visual workflow service that enables you to build complex data processing pipelines involving a diverse set of extract, transform, and load (ETL) technologies such as AWS Glue, Amazon EMR, and Amazon Redshift. Big data Oct 30, 2018 · Dynamically resize the storage space on core and task nodes. Compare AWS Data Pipeline vs. The EMR File System (EMRFS) is an implementation of HDFS that allows Amazon Elastic MapReduce (Amazon EMR) clusters to store data on Amazon Simple Storage Service (Amazon S3). dp. Additionally, the EC2 instance profile of your cluster must have the ec2:ModifyVolume In this lesson, I will provide introductory information on AWS Glue. 305. That means that Data Pipeline will be better integrated when it comes to deal with data sources and outputs, and to work directly with tools like S3, EMR, DynamoDB, Redshift, or RDS. yr fh ib nu tk nm vq mu zu kr