AWS Data Engineer Roadmap: Kick Start Your Career as AWS Data Engineer
1. Introduction of AWS Data Engineer
AWS Data Engineering is like being the conductor of a big data orchestra on the Amazon Web Services (AWS) cloud stage. Imagine you have a treasure chest full of data, and you want to use it to make your business smarter and better. Data engineering is the magical process that helps you collect, store, and prepare all that data so you can analyze it and make wise decisions.
So, AWS Data Engineering is about using these AWS tools to build a smooth data highway, making sure your data goes where it needs to go, and turning it into insights that can boost your business. It’s a bit like being the captain of a data ship, navigating the vast sea of information to reach your destination safely and successfully.
So, think of this blog post as your treasure map to becoming an AWS Data Engineer. It’s going to guide you through the skills you need to master on your way to becoming a data engineering expert. Let’s get started on this exciting journey! However, before proceeding further make sure you know the prerequisites, for that you can visit our Blog post.
AWS offers a suite of powerful tools that data engineers can utilize to manage and process data effectively. These tools play a crucial role in building data pipelines, performing data analysis, and ensuring data security within the AWS cloud environment. Here’s an elaboration on some of the key AWS data engineering tools:
Start by exploring AWS’s official documentation and tutorials for AWS Glue. AWS provides comprehensive guides to help you understand the service’s features and capabilities.
2. Amazon Web Services​ Know AWS Inside Out
Amazon Web Services
Before you can rock as an AWS cloud engineer, you need to know your tools. AWS offers a bunch of services, and you can group them into Deployment & Management, Application Services, and Foundation Services. Get comfy with these categories.
A. AWS Glue - ETL Pipelines
- AWS Glue is a serverless data integration service in the AWS ecosystem.
- It simplifies data discovery, preparation, movement, and integration from various sources for analytics, machine learning, and application development.
- To learn AWS Glue, explore official AWS documentation, take online courses, and gain hands-on experience by creating your AWS account.
- Engage with the AWS community through forums, consider certifications, and explore books for comprehensive learning.
B. AWS Athena - Data Querying
- AWS Athena is a query service that simplifies data analysis by allowing you to use standard SQL queries.
- It’s a serverless service, which means you don’t have to worry about managing infrastructure.
- With Athena, you can swiftly examine data stored in Amazon S3 without complex setups.
- It’s an ideal tool for those who want to perform quick and straightforward data analysis using familiar SQL commands.
C. AWS Redshift - Data Warehousing
- AWS Redshift is a petabyte-scale data warehouse cloud service for powerful data analysis.
- It uses SQL queries to rapidly analyze structured and semi-structured data, like a superhero of data analytics.
- Redshift Serverless simplifies data import, querying, schema creation, and table building.
- Engineers can efficiently gain insights, import data visually, and explore databases with Query Editor v2.
D. AWS Kinesis - Real-time Data Analysis
- AWS Kinesis is a real-time data collection powerhouse for quick insights.
- Amazon Kinesis provides managed cloud services for collecting and analyzing streaming data in real-time.
- Data engineers use it to set up data streams, define requirements, and start streaming data immediately.
- With Kinesis, you can access and analyze data in real-time, avoiding the wait for delayed reports.
E. AWS IAM - Access Control
- AWS IAM is like the security gatekeeper of AWS, determining who can access its services and resources.
- It’s a vital service for controlling access to AWS resources, including services like Amazon SageMaker and Amazon S3.
- IAM is highly flexible, allowing data engineers to design roles that follow the principle of least privilege for each AWS service.
- This ensures that individuals and applications only have the permissions they need, enhancing overall security.
F. Amazon S3 - Data Storage
- Amazon S3 is a highly versatile data storage service known as a “data lake” because it can store vast amounts of data from anywhere on the internet.
- It’s renowned for its scalability, speed, and cost-effectiveness, making it a go-to choice for data engineers.
- Engineers can duplicate their S3 storage across different Availability Zones, enhancing data redundancy.
- Amazon S3 is a powerful tool for building web-based cloud solutions that automatically scale and offer flexible configurations.
G. Other Amazon Web Services​
AWS Lake Formation:
- Centralized permissions for precise data access.
- Handle permissions across different AWS analytics services.
- Use tags for controlling access to sensitive data.
- Share data easily across different AWS accounts or with external organizations.
ElastiCache:
- Use LazyLoading for scenarios with lots of reading and infrequent updates.
Amazon QuickSight:
- Utilize the SPICE engine for interactive dashboards with live connections to data sources like Amazon RDS.
AWS SCT (Schema Conversion Tool):
- Handle KMS and SSEKMS with customer managed keys.
AWS DMS (Database Migration Service):
- Use AWS SCT for schema conversion and copying schemas.
AWS CloudTrail:
- Store logs in S3.
- Create and manage a CloudTrail Lake for optimized storage and analysis of logs for up to seven years.
AWS CloudWatch:
- Understand logs and use Logs Insights.
Amazon Redshift Spectrum:
- Utilize Spectrum for additional analytics capabilities.
AWS SAM (Serverless Application Model):
- Use API Gateway, Data Pipeline, AWS Lambda, and AWS EFS for various functions.
- Employ AWS Step Functions for workflow automation.
AWS Sagemaker:
- Use Sagemaker for machine learning tasks, data tracking, and data wrangling.
EMR (Elastic MapReduce):
- Process large datasets efficiently with EMR and Apache Spark.
AWS Code Services:
- Utilize CodeCommit, CodeBuild, CodeDeploy, and CodePipeline for managing code.
AWS Neptune:
- Use Neptune for graphical data structures.
RDS (Relational Database Service):
- Work with RDS Read Replicas, Multi AZ deployments, and supported database engines.
AWS Aurora:
- Leverage Aurora Read Replicas for better performance.
AWS EKS (Elastic Kubernetes Service):
- Manage deployments, versions, concurrency, and integration with other services.
Other Technologies:
- Understand and use Apache Spark, Apache Flink, Hive, and Parquet for various use cases.
G. AWS CloudWatch - Log Metrics Analysis
- AWS CloudWatch is your command center for managing system and application logs, making it a valuable tool for debugging.
- It consolidates system, application, and AWS service logs into a single, scalable service for centralized monitoring.
- Data engineers use CloudWatch to access logs for the services they operate, aiding in troubleshooting and debugging.
- It also supports scheduling services to run at specific times through CloudWatch Events, enhancing automation and resource management.
H. AWS Lambda - Serverless Computing
- AWS Lambda is your automated helper, running code when events occur, ideal for data collection and processing.
- It’s a serverless service, meaning you don’t have to manage the infrastructure.
- Data engineers can use Lambda to create functions that fetch data from API endpoints, process it, and save it to places like S3 or DynamoDB.
- Lambda simplifies tasks that involve gathering and processing raw data, making it a valuable asset for data engineers.
I. Amazon EMR - Large-Scale Data Processing
- AWS Elastic Map Reduce (EMR) is your solution for large-scale data processing using big data technologies like Hadoop and Spark.
- It allows data engineers to launch temporary clusters for tasks like Spark, Hive, or Flink, simplifying complex processing.
- Engineers can define dependencies, set up cluster configurations, and identify the underlying EC2 instances for efficient data handling.
J. Amazon DynamoDB - RDBMS System
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- Amazon DynamoDB is an alternative to traditional databases, supporting various data types including documents, graphs, key-value, memory, and search.
- It’s perfect for data engineers storing semi-structured data with a unique key.
- DynamoDB is also handy for maintaining data consistency and tracking the state of other services, such as Step Functions.
AWS’s official documentation is an indispensable resource for learning AWS data engineering services, offering the latest, reliable, and comprehensive information on the entire spectrum of AWS offerings. With clear introductions, practical tutorials, and detailed user guides, it provides valuable insights into service features, best practices, and configuration options. Whether you’re just starting or have extensive experience, the documentation serves as a foundational reference, supported by practical examples and troubleshooting guidance. It’s a go-to source for staying up-to-date with AWS’s ever-evolving ecosystem and a gateway to successful data engineering on the AWS platform.
3. AWS Data Engineering Project Example!
Project title: Enhancing Customer Insights for 'StreamWise' Streaming Service
‘StreamWise’ is a popular streaming service, offering a wide range of content, including movies and TV shows to subscribers. The company is keen to improve its understanding of customer preferences and behavior to enhance customer satisfaction, content recommendations, and subscription retention. To address this, StreamWise has initiated a data engineering project that encompasses data ingestion, transformation, and analysis.
A. AWS Technologies and Services Used
- Amazon S3 (Simple Storage Service)
- AWS Glue
- Amazon Redshift
- Amazon Kinesis
- AWS IAM (Identity and Access Management)
- AWS Lambda
B. Project Flow
Data Ingestion:
- Data is ingested from multiple sources, including user activity logs, streaming history, and subscription data.
- Amazon Kinesis is employed for real-time data collection to capture user interactions with the streaming service.
- AWS Glue is used to transform and organize incoming data streams efficiently.
Data Transformation with AWS Glue:
- AWS Glue performs ETL (Extract, Transform, Load) operations to process and cleanse the raw data.
- Data engineers create a comprehensive data catalog that maps out the schema and structure of the ingested data.
- Advanced transformations are applied to enrich data, such as user profiling, content categorization, and sentiment analysis.
Data Storage and Querying:
- Transformed data is stored in Amazon S3 buckets, creating a centralized data repository.
- Amazon Redshift, a petabyte-scale data warehousing service, is used for querying structured data.
- Amazon Athena, a serverless query service, is leveraged for ad-hoc querying using standard SQL.
Real-time User Insights:
- AWS Lambda functions process real-time user data from Kinesis streams to provide immediate insights.
- User interactions trigger Lambda functions, which analyze preferences and recommend content in real time.
Access Control and Security:
- AWS IAM is employed to control access to data and services, ensuring that only authorized users can access and manipulate the data.
- Amazon S3 and Redshift security is enhanced through IAM, and data encryption is implemented to protect sensitive customer data.
‘StreamWise’ can now harness valuable insights from its data, enabling a deeper understanding of customer preferences and behaviors. The data engineering pipeline efficiently collects, transforms, and stores data from various sources, allowing for real-time and batch analysis. Customer interactions are used to improve content recommendations, enhance user experiences, and increase subscription retention rates. The project empowers ‘StreamWise’ to stay competitive in the streaming market by offering tailored content and personalized services to its subscribers.
4. Data Engineering Certification: AWS Certified Data Engineer - Associate
The AWS Certified Data Engineer – Associate exam is at the associate level and has a duration of 170 minutes. It consists of 85 questions, which can be either multiple choice or multiple response. The exam can be taken either in-person at a Pearson VUE testing center or online through a proctored exam. It is offered in English, and the cost is $75 USD. Additional cost information can be found on the Exam pricing page.
- The AWS Certified Data Engineer – Associate (DEA-C01) exam checks if you can handle data pipelines, troubleshoot issues, and optimize cost and performance.
- It evaluates your skills in ingesting and transforming data, orchestrating pipelines, and applying programming concepts.
- Tasks include choosing optimal data stores, designing data models, cataloging data schemas, and managing data lifecycles.
- You should be adept at operationalizing, maintaining, and monitoring data pipelines, as well as analyzing data and ensuring data quality.
- Implementing authentication, authorization, data encryption, privacy, and governance, along with enabling logging, is part of the exam.
- The ideal candidate should have 2-3 years of data engineering experience and 1-2 years of hands-on experience with AWS services.
- General IT knowledge should cover setting up and maintaining ETL pipelines, using programming concepts, employing Git commands, and understanding data lakes, networking, storage, and compute.
- AWS-specific knowledge involves using AWS services for the mentioned tasks, understanding encryption, governance, protection, and logging, and comparing AWS services for cost, performance, and functionality.
- Competence in structuring and running SQL queries on AWS services and analyzing data, verifying data quality, and ensuring consistency is expected.
To Know More about Exam content, Please check this out
https://d1.awsstatic.com/training-and-certification/docs-data-engineer-associate/AWS-Certified-Data-Engineer-Associate_Exam-Guide.pdf
- The AWS Certified Data Engineer – Associate certification is a prestigious accreditation offered by Amazon Web Services (AWS) that validates the skills and expertise of professionals in the field of data engineering.
- This certification is designed for individuals who specialize in the design and implementation of data solutions on the AWS platform. It requires a strong foundation in data analytics, data processing, data storage, and data security on AWS services.
- Achieving the AWS Certified Data Engineer – Associate certification demonstrates one’s proficiency in key areas such as AWS data services, ETL (Extract, Transform, Load) processes, data lakes, and data warehousing.
For More Information , you can check out this Link
https://aws.amazon.com/certification/certified-data-engineer-associate/
In the ever-evolving world of data engineering, embarking on the AWS journey necessitates a well-structured roadmap. This comprehensive guide has illuminated the critical milestones on this path, from mastering AWS services like Glue, Athena, Redshift, and Kinesis to understanding the importance of security through IAM and utilizing data storage solutions like S3. Furthermore, the power of serverless computing with Lambda, the scalability of EMR for big data processing, and the versatility of DynamoDB for different data types are essential facets. While the journey is undoubtedly challenging, the destination promises a world of opportunities in data management, analytics, and data-driven decision-making, ultimately making a significant impact in the realm of data engineering on AWS.
AWS Data Engineer Job Description
An AWS engineer’s role varies across companies, but certain fundamentals define AWS data engineering tasks:
1. Utilizing AWS Tools: Employing AWS data and analytics tools like Spark, DynamoDB, RedShift, and others in conjunction with third-party tools to design, develop, and operationalize extensive enterprise data solutions and applications.
2. Migration to AWS Cloud: Analyzing, re-architecting, and re-platforming on-premise data warehouses to data platforms on AWS cloud, using AWS or third-party tools.
3. Data Pipeline Development: Designing and constructing production data pipelines from data intake to consumption within a comprehensive data architecture, utilizing Java, Python, and Scala.
4. Implementation of Data Functions: Utilizing AWS native or custom programming to design and implement data engineering, ingestion, and curation functions on the AWS cloud.
5. Platform Analysis and Migration Planning: Conducting thorough analyses of existing data platforms and devising suitable strategies for migrating them to the AWS cloud.
AWS Data Engineer Roles and Responsibilities
AWS data engineers have many tasks, including:
1. Building data models to gather information from different sources and organize it well.
2. Keeping data safe by creating backup and recovery systems.
3. Improving database design to make things run faster.
4. Finding new technologies and data sources for ongoing projects.
5. Spotting trends in data to help make business decisions or plans.
6. Making new applications using existing data for new products or better services.
7. Updating old code or adding features to keep apps up-to-date.
8. Setting up security measures to protect data from misuse.
9. Enhancing infrastructure for more storage or better performance.
10. Creating and managing data pipelines.
11. Using AWS tools for data integration.
12. Retrieving data using Amazon Simple Storage Service.
13. Implementing firewall security using AWS security groups.
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