Aws Data Pipeline Java Example

What is AWS Data Pipeline?

Last Updated: 22 Jun 2022

An AWS data pipeline helps businesses move and unify their data to support several data-driven initiatives. Generally, it consists of three key elements: a source, processing step(s), and destination to streamline movement across digital platforms. It enables flow from a data lake to an analytics database or an application to a data warehouse. Amazon Web Services (AWS) offers an AWS Data Pipeline solution that helps businesses automate the transformation and movement of data. With AWS Data Pipeline, companies can quickly get started with Amazon Web Services and define data-driven workflows and parameters of data transformations. They also use the pipeline to transfer it to various Amazon Web Services for analysis, such as Amazon DynamoDB, Amazon RDS, etc. AWS CLI is an excellent tool for managing Amazon Web Services.

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This blog will teach you about AWS Data Pipeline, its architecture, components, and benefits. You'll also learn the steps to build an AWS Data Pipeline.

Table of Contents

• What is an AWS Data Pipeline?
• AWS Data Pipeline Architecture - Core Components
• AWS Data Pipeline Examples
• Need for AWS Data Pipeline
  • AWS Data Pipeline For Data Consolidation
  • AWS Data Pipeline For Easier Access to Information and Insights
  • AWS Data Pipeline For Faster Decision-Making
  • AWS Data Pipeline For Agility to Meet Peaks in Demand
• Advantages of AWS Data Pipeline
• Components of a Typical AWS Data Pipeline
• How to Create an AWS Cloud Data Pipeline?
• Steps to Building an AWS Data Pipeline with Console Templates
• FAQ's on AWS Data Pipeline
  • 1) Is AWS Data Pipeline Serverless?
  • 2) What is AWS data pipeline vs. AWS glue?

What is an AWS Data Pipeline?

Amazon Web Services Inc offers Data Pipeline, a web service that helps process and moves data between various AWS compute, on-premises sources, and storage services at specified intervals. Businesses don't have to create an ELT infrastructure to extract and process their data, and instead, the AWS pipeline web service will help them access, transform, and process data from the location where it is stored. With Amazon EMR, developers can simplify running big data frameworks on AWS to process and analyze vast amounts of data.

AWS Data Pipeline Architecture - Core Components

Source: A place (RDBMS, CRMs, ERPs, social media management tools, or IoT sensors) where a pipeline extracts information from.

Destination: The endpoint where the pipeline dumps all the extracted information. The destination can be a data lake or data warehouse. For example, data can be fed directly into data visualization tools for analysis.

Data Flow: Data changes as it travels from source to destination, and this data movement is called data flow. One of the most common data flow approaches is ETL, extract, transform, load.

Processing: The steps involved in extracting data from sources, transforming it, and moving it to a destination are called processing. The processing component decides how data flow is implemented. For example, it determines which extraction process you should use for ingesting data — batch or stream processing.

Workflow: Workflow involves sequencing jobs and their dependence on each other in a pipeline. Dependencies and sequencing will decide when it runs. Furthermore, upstream jobs are completed before downstream jobs can begin in pipelines.

Monitoring: Consistent monitoring is vital to check data accuracy, speed, data loss, and efficiency. These checks and monitoring become increasingly important as data size grows more prominently.

AWS Data Pipeline Examples

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Let's look at the AWS data pipeline example, where the data from the DynamoDB table is copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. The following is the AWS data pipeline architecture for the above example:

To Learn More About Other AWS Data Pipeline Example Implementation, Check Out AWS Data Pipeline Examples documentation.

Need for AWS Data Pipeline

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Here are four benefits of an AWS data pipeline for your business:

• AWS Data Pipeline For Data Consolidation

The expansion of the cloud has meant that a modern enterprise uses a suite of apps to serve different functions. Different teams might employ various tools to manage leads and store customer insights, leading to data silos and fragmentation across other tools. Silos make it challenging to fetch simple business insights. Even if you manually fetch data from different sources, you can run into errors such as data redundancy. You can consolidate data from your sources into one common destination with the pipeline to get business insights.

• AWS Data Pipeline For Easier Access to Information and Insights

Analyzing data and turning it into informative insights is important since it has no value while residing in its raw format. Businesses need an automated process to extract data from the database and move it to an analytics software or tool. For example, companies can create an AWS data pipeline to consolidate their sales data from various platforms and get insights into customer behavior and buying journeys.

• AWS Data Pipeline For Faster Decision-Making

Even a couple of hours of lag can mean lost business in today's fast-paced world. For example, if a sales team doesn't know about a recent trend or global event that can trigger a change in customer behavior, they can't offer the right products to customers. Suppose a customer service team isn't aware of the problems with their logistics partner or can't pull customer data in time to answer queries. In that case, they won't be providing adequate service to customers. AWS pipelines help companies easily extract and process their data. They can get up-to-date insights, leverage more opportunities, and make informed decisions.

• AWS Data Pipeline For Agility to Meet Peaks in Demand

Several organizations need access to data, so the business should be able to add storage and processing capacity within minutes. Traditional pipelines are rigid, hard to debug, slow, inaccurate, and unable to scale. AWS Data Pipeline is scalable and ensures processing a million files is easy.

Advantages of AWS Data Pipeline

Following are some of the advantages of AWS Data Pipeline:

1. Low Cost

AWS Pipeline pricing is inexpensive and billed at a low monthly rate. AWS free tier includes free trials and $100,000 in AWS credits. Upon sign-up, new AWS customers receive the following each month for one year:

• 3 Low-Frequency preconditions running on AWS

• 5 Low-Frequency activities running on AWS

2. Easy-to-use

AWS offers a drag-and-drop option for users to design a pipeline easily. Businesses don't have to write code to use common preconditions, such as checking for an Amazon S3 file. You only have to provide the name and path of the Amazon S3 bucket, and the pipeline will give you the information. AWS also offers a wide range of template libraries for quickly designing pipelines. These templates simplify creating pipelines for several uses, such as archiving data to Amazon S3, regularly processing log files, and running periodic SQL queries.

3. Reliable

AWS Cloud Pipeline is built on a highly available, distributed infrastructure designed for fault-tolerant execution of your events. With Amazon EC2, users can rent virtual computers to run their computer applications and pipelines. AWS Pipeline can automatically retry the activity if there is a failure in your activity logic or sources. AWS Cloud Pipeline will send failure notifications via Amazon Simple Notification Service whenever the failure isn't fixed. Users can also configure notifications for delays in planned activities, successful runs, or failures.

4. Flexible

AWS pipeline is flexible, and it can run SQL queries directly on the databases or configure and run tasks like Amazon EMR. AWS cloud pipelines can also assist in executing custom applications at the organizations' data centers or on Amazon EC2 instances, helping in data analysis and processing.

5. Scalable

The flexibility of the AWS pipeline makes them highly scalable. It makes processing a million files as easy as a single file, in serial or parallel.

6. Transparency

Users have complete control over the resources required to execute or rectify the logic. All the logs and a record of the pipeline are saved in the Amazon S3 storage service, and users can use these records to check the status of tasks and events in the pipeline.

Components of a Typical AWS Data Pipeline

1. Pipeline definition  This is how users communicate their business logic to the AWS cloud Data Pipeline. It contains the following information:

• • Names, formats, and locations of your data sources

  • Activities that transform it

  • The schedule for the above activities

  • Resources that run the activities and preconditions

  • Preconditions that must be met before the activities can be scheduled

  • Ways to alert the status update to the user as the pipeline executes

2. Pipeline: It schedules and runs tasks. You can upload the pipeline definition and activate it. Users will have to deactivate a pipeline before changing the source and then start it. A pipeline doesn't have to be deactivated if you only need to edit it.

3. Data nodes: In AWS Pipeline, a data node defines the location and type of data that a pipeline activity uses as input or output. It supports the following four types of data nodes:

• • SqlDataNode: An SQL database and table query that represents data for a pipeline activity to use.

  • DynamoDBDataNode: A table that contains data for EmrActivity or HiveActivity to use.

  • RedshiftDataNode: A Redshift table that contains data for RedshiftCopyActivity to use.

  • S3DataNode: An Amazon S3 location that contains files for a pipeline activity to use.

4. Activity: AWS pipeline activities define the work that pipeline has to perform. You can have pre-defined activities for various common scenarios like movement, hive queries, SQL transformation, etc. Activities are extensible, and you can run custom scripts.

5. Task runner: It is installed and runs automatically on the resources created by pipeline definitions. It will poll for tasks and then perform the task mentioned in the pipeline definition. Pipeline provides a Task Runner application, or you can write a custom application.

6. Pipeline Log files: You can configure pipelines to create log files in a persistent location. You can create log files in locations where you use the pipelineLogUri field, causing all components to use an Amazon S3 log location by default.

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How to Create an AWS Cloud Data Pipeline?

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With AWS, you can create pipelines in a variety of ways:

• Creating Pipelines Using AWS data pipeline Templates

• Building Pipelines Using the Console Manually

• Use the AWS Command Line Interface in JSON format

• Use an AWS SDK with a language-specific API

This blog will cover how to get started with AWS Data Pipeline using console templates.

Prerequisites:

• Amazon S3 location, where the file that you copy is located

• A destination Amazon S3 location to copy the file to

Steps to Building an AWS Data Pipeline with Console Templates

1. Open the Data Pipeline console.

2. The next screen depends on whether you have previously created a pipeline in the current region. You might see one of the two screens:

1. 1. The console displays an introductory screen with the message if you haven't created a pipeline.

   2. The console will display your previous pipelines for the region if you've already created a pipeline.

3. Under the Name section, enter a name for your pipeline.

4. You can also enter a description under the Description section for your pipeline.

5. Next, select Build using a template, and then you can select any of the templates. For example, you can choose this template: Import DynamoDB backup data from S3.

6. You can add bootstrapAction to the Amazon Emr Cluster object if you use a resource managed by AWS Data Pipeline. Also, Amazon Web Service Pipeline only supports Amazon Emr cluster release version 6.1.0

7. Under Parameters, set the Input S3 folder to s3://elasticmapreduce/samples/Store/ProductCatalog, a directory containing ProductCatalog.txt, and the sample data source. You should set the DynamoDB table name to the name of your table.

8. Under Schedule, select pipeline activation.

9. Under Pipeline Configuration, leave logging enabled. Next, select the folder icon under the S3 location for logs, select one of your folders, and choose Select.

10. Under Security/Access, leave IAM roles set to default.

11. Click Edit in Architect.

Businesses have been generating vast amounts of data through their websites, social media platforms, and other channels. An Amazon Web Services Inc's data pipeline is an excellent solution for managing, processing, and storing the data. It also helps developers on AWS to transfer it to various Amazon Web Services for analysis, such as Amazon RDS, Amazon DynamoDB, etc. However, you will have to provide access to these web services through AWS IAM. Companies can create an AWS Pipeline in one of the four ways mentioned. But for this article, we have created a pipeline in AWS using the DynamoDB template. Companies can use tools like AWS CLI to manage Amazon services. You can check AWS documentation if you want to create an AWS pipeline from scratch.

FAQ's on AWS Data Pipeline

1) Is AWS Data Pipeline Serverless?

AWS Step Functions and AWS Glue provide various serverless components. You can build, orchestrate, and run serverless pipelines that can quickly scale to process large data volumes.

2) What is AWS data pipeline vs. AWS glue?

AWS data pipeline provides automation in data movement and ensures that the following process begins automatically only after the first process is completed successfully. It comes under the 'Data Transfer' category in big data. In contrast, AWS Glue provides easier creation, transformation, and subsequent loading of the datasets. It is primarily an ETL (Extract, Transform, Load) tool and comes under the "Data Catalog" category.

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Source: https://www.projectpro.io/article/what-is-aws-data-pipeline/599

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