What are the things to learn when AWS cloud services are used for a Data Engineering Project?

Hi Everyone,

As i was trying to figure out how does a data pipeline get created on Cloud environment I was able to get some very good videos which showed exactly what all things a Data Engineer can do with Data that is taken for Business Analysis across various business organizations.

Let's Go!!!

First there are a few things to understand here before we talk about technical aspects of Data Engineering with cloud. Infact when i explored this area further i understood that the role of Data Analyst and Data Scientist is much less required when it comes to ETL operations on Data. However Data Analyst's job and Data Scientists inputs may prove handy for the Product Managers or Business Analysts so that Developers can build more relevant features to the application than building things that do not add value to the application.

Why is Cloud Important for Data Science?

Most of them are quite aware that certain Companies/Businesses have their business data stored on Data centers that are Physically located on their premise or their client premises. This is one approach that certain businesses take. However with increasing demand for huge volumes of data processing capability there is a need to upgrade their physical infrastructure at all their client premises apart from doing it in their own premises. In such cases cost involved in investing on hardware, os and other resources leads businesses to think instead of doing this why not use cloud based services which will cost much less in the long term.

Hence a lot of businesses like Ecommerce, CRM businesses, Manufacturing businesses look for moving their physical infrastructure into cloud platform like AWS/GCP/Azure.

Every individual Cloud service provider offers a host of services which when combined together becomes a data pipeline to be able to process and transform huge volume of data and derive meaningful output to take proper and market driven business decisions.

Lets now understand on the top if you are using AWS what kind of Services would be helpful for a Data Engineer to be able to build a proper ETL Data Pipeline and offer end to end Data Engineering Services.

In a general aspect the tools and technical details one has to learn in order to be a Data Engineer are many however with time and the type of solution offered the tools and services vary. Because the data that you are processing depends on the type of business and the business requirement specification. Please note that not all businesses do the same kind of business analysis. Different companies do different kind of business analysis to plan out different marketing strategies and take data driven business decisions.

What a Data Engineer should know for offering End to End Business Solution on Cloud using AWS?

In order to provide end to end Data Engineering services to clients/Businesses a Data Engineer should know the following things:

• Strong programming expertise in Python/ Java/ Scala
• Strong experience in Query tools such as SQL, Postgres, MySQl, Spark SQL.
• Experience in working on AWS services such as Amazon Cloud Watch, Glue, Lambda, S3 Buckets, AWS Direct Connect, Data Lake, EC2 instances, PySpark, Athena, Scrawler, Amazon Redshift, IAM (Identity Access Management). These services can be learnt inside AWS and a bit of IAM configuration.

What is the scope of Data Engineering in Future?

Well there is a lot of scope as far as Business Analysis is concerned because every multinational business is going to need its business data analyzed in order to understand whether they are doing the right thing or to study the market demand, how are the trends and so on.

There can be a lot of demand for Migration of platform from Physical infrastructure to cloud platform for many small upcoming businesses and hence you can become a independent consultant or a free lancer working at your own pace in future.

There could be businesses that need to upgrade their Data processing infrastructure once in a while depending on the need to process huge volumes of data. So migrating from older databases into new once and more powerful ones plays an important role here.

What kind of experience will Data Engineer get here?

When offering end to end solutions Data Engineer will be exposed to building data pipelines on AWS using its available services such as Lambda for transformation, Glue for Scrawling through the new data set inside S3 bucket and also exposure to use BI tools like Power BI, or Data Warehouses such as Snowflake, Amazon QuickSight, Power BI, NoteBook and so forth.

The type of Business requirement to be solved also matters and hence the experience is wide and very large in terms of tools and cloud services.

Data Engineer can work without cloud and with cloud services. There are open source tools if you are taking Python Stack but for higher security AWS/GCP/Azure is preferred.

Data Engineer can also use advanced formulas and options in Microsoft Excel to transform data locally or if you are insisting on working on cloud then cloud certification along with working experience in cloud is necessary along with programming skills.

Higher pay comes for Cloud based engineers and again these things can be replicated on Azure and GCP also. However market share of AWS is much higher at the present time.

Will figure out how Data Scientists differ from Data Engineers in future articles. Machine learning and AI is completely another kind of role but interrelated and driven by information given by the Data Engineers Pipeline and Data Analysis.

Hope this helps for now. More later.

Thanks,

Swaroop