Want to Become a AWS Data Engineer — Technical Skills😎😎

💭Cloud platforms are becoming the new standard for managing an organization’s data. Data engineering is the key to holistic business process management, Data engineering makes it possible for businesses to extract important information from large datasets and make key decisions after careful data analysis.

What is Data Engineering?

Data engineers build systems for collecting, validating, and preparing high-quality data. Data engineers gather and prepare the data and data scientists use the data to promote better business decisions.

A Data Engineer’s primary job is to prepare data for Analytical and operational uses ,they can store process and analiyse the data of any organization which includes large volume of data.
They are also responsible for building data pipelines to bring information together from different source systems into Data warehouse or Data lake. They integrate, consolidate and cleanse the data and structures them to use in analytics applications

Data Engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret.

AWS Data Engineering Skills

1. AWS Service to know as a Data Engineer

✍️Amazon Simple Storage Service — Amazon S3 is a data lake that can store any volume of data from any part of the internet, Since it is an incredibly scalable, quick, and affordable option, Data engineers have the flexibility to duplicate their S3 storage across various Availability Zones with Amazon S3.

✍️Amazon Relational Database Service (RDS) — It’s a managed SQL database service provided by Amazon Web Services (AWS). Amazon RDS supports an array of database engines to store and organize data. It also helps in relational database management tasks like data migration, backup, recovery and patching.
Amazon RDS facilitates the deployment and maintenance of relational databases in the cloud. Cloud administrators use Amazon RDS to set up, operate, manage, and scale relational instances of cloud databases.

✍️AWS Redshift — Amazon Redshift is a petabyte-scale data warehouse cloud service that enables you to leverage your data to discover new insights about your clients and organization. Data engineers can gain insights from data with Redshift Serverless by easily importing and querying data in the data warehouse. Additionally, engineers can build schemas and tables, import data visually.

✍️AWS EMR — AWS Elastic Map Reduce (EMR) is one of the primary AWS Services for developing large-scale data processing that leverages Big Data Technologies like Apache Hadoop, Apache Spark, Hive, etc. Data engineers can use EMR to launch a temporary cluster to run any Spark, Hive, or Flink task. It allows engineers to define dependencies, establish cluster setup, and identify the underlying EC2 instances.

✍️AWS Glue — AWS Glue is a fully managed ETL(extract, transform, and load) service for easily and affordably processing, improving, and migrating data between different data stores and data streams. Data engineers may interactively analyze and process the data using AWS Glue Interactive Sessions. Data engineers can visually develop, run, and monitor ETL workflows in AWS Glue Studio with a few clicks.

✍️AWS Kinesis — Amazon Kinesis offers several managed cloud-based services to collect and analyze streaming data in real time. Data engineers leverage Amazon Kinesis to build new streams, easily specify requirements, and begin streaming data. Additionally, Kinesis enables engineers to get data instantly and analyze it rather than waiting for a data-out report.

✍️AWS Lambda — Its a serverless computing AWS service that executes your code in response to events and manages the underlying computing resources effortlessly. Lambda comes in handy when collecting the raw data is essential. Data engineers can develop a Lambda function to access an API endpoint, obtain the result, process the data, and save it to S3 or DynamoDB.

2. Programming Language

Proficiency in coding is essential in any one Programing Language like Python, R, Scala, Java. As a AWS Data Engineer you need to manage the data and automate the script.

3. Understand AWS Data Engineer Role

AWS Data Engineer is responsible for Building, Managing and optimizing large scale data processing system under AWS Platform.
They work with Big Data Tools and Technologies like-
AWS Redshift , AWS Glue, Amazon EMR, Amazon kinesis, these are essential to understand the Databases, Data warehousing , ETL processes and Data Modeling.

4. The Data Engineering Concepts

Have a strong understanding of Relational Data Base, SQL, No SQL databases, Data warehousing concepts, ETL processes, data Modeling and Normalization ,Big data Processing like Hadoop and spark, data streaming and real time processing.

5. Acquire AWS Certification

Certification is self-promotion of knowledge, Certificates are the credentials that recognize and validate one’s knowledge and expertise.
Obtaining professional certification displays your dedication to your profession and provides verification that you’re well-trained to use the services effectively.

Below mentioned are some of the Data related Certification-

📍AWS Certified Data Analytics — Specialty
📍AWS Certified Database — Specialty
📍AWS Certified Machine Learning — Specialty
📍AWS Certified Big Data — Specialty

5. Build a portfolio on Data Engineering Projects

Building data engineering projects and creating a good Data Engineering portfolio helps to showcase your skill and knowledge, Set up you own labs with help of AWS Console and start converting your learnings into POCs.
Come-up with good number of use cases and pre plan to work on use-cases. Once you start working on use-cases, develop ideas to automate the pipelines wherever necessary.
Document your project thoroughly this must contain a detailed description of the project, the tools and technologies used, the data sources, the data transformations, and any challenges or obstacles you encountered during the project. This helps to streamline in upcoming projects you do.

Data pipelines?

A data pipeline defines the mechanism that determines the flow of data from its origin to its destination, including the various processes or transformations that the data might undergo along the way. A standard pipeline is quite like one of the most basic computing processes of Input → Processing → Output.