Learn how to become an AWS Certified Data Engineer -Associate with hands-on learning. In addition, learn how will the AWS Certified Data Engineer – Associate help my career?

Demand for data engineer roles increased by 42% year over year per a Dice tech jobs report. This is an in-demand role with a low supply of skilled professionals. AWS Certified Data Engineer – Associate and accompanying prep resources offer you a means to earn this certification and build your confidence and credibility in data engineer or related roles. 

The AWS Certified Data Engineer – Associate (DEA-C01) exam validates a candidate’s ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices.

The AWS certified data engineer associate exam validates a candidate’s ability to complete the following tasks:

• First, ingest and transform data, and orchestrate data pipelines while applying programming concepts.

• Choose an optimal data store, design data models, catalog data schemas, and manage data lifecycles.

• Operationalize, maintain, and monitor data pipelines. Analyze data and ensure data quality.

Implement appropriate authentication, authorization, data encryption, privacy, and governance. Enable logging.

In addition, you should have the equivalent of 2–3 years of experience in data engineering. Thus, the target candidate should understand the effects of volume, variety, and velocity on data ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design. Additionally, the target candidate should have at least 1–2 years of hands-on experience with AWS services.

You should have the equivalent of 2–3 years of experience in data engineering. The target candidate should understand the effects of volume, variety, and velocity on data ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design. Additionally, the target candidate should have at least 1–2 years of hands-on experience with AWS services.

Course Description

This course covers all aspects of the following domain areas:

1. Domain 1: Data Ingestion and Transformation (34% of scored content)
2. Domain 2: Data Store Management (26% of scored content)
3. Domain 3: Data Operations and Support (22% of scored content)
4. Domain 4: Data Security and Governance (18% of scored content)

Why should you get AWS Certified?

Getting AWS Certified can help you propel your career, whether you’re looking to find a new role, showcase your skills to take on a new project, or become your team’s go-to expert. And because AWS Certification exams are created by experts in the relevant role or technical area, preparing for one of these exams helps you build the required skills identified by skilled practitioners in the field.

Demand for data engineer roles increased by 42% year over year per a Dice tech jobs report

Your certification will be valid for three years from the day scores are released

Above all, When you’re working with AWS platform you will gain valuable experience and hands-on skills with this course.

Data analytics is vital to businesses large and small. Hence, Data analytic processes are combined to create data analysis solutions, which help businesses decide where and when to launch new products, when to offer discounts, and when to market in new areas. Thus, without the data provided by data analytics, many decision makers would base their decisions on intuition and pure luck.

As businesses begin to implement data analysis solutions, challenges arise. Therefore, these challenges are based on the characteristics of the data and analytics required for their use case. In the past, these challenges have been defined as “big data” challenges. However, in today’s cloud-based environment, these challenges can apply to small or slow data sets nearly as often as very large, fast data sets

As we have become a digital society, the amount of data we create and collect has grown significantly, and the pace of growth is accelerating. Thus, we have built systems to handle data collection, and those systems store all that data very efficiently.

Who is an AWS Certified Data Engineer?

Data engineers, in general, need to have a working knowledge of software engineering and database management. Data engineers design, build and maintain massive databases that support web applications or other digital services.

Furthermore, AWS data engineers perform the same duties as regular data engineers but exclusive to Amazon Web Services cloud platform. In other words, an AWS engineer creates, maintains, and upgrades the AWS infrastructure to run applications. To succeed in this field, one should have a solid understanding of AWS and data engineering principles.

5 key challenges: volume, velocity, variety, veracity, and value

Components of a data analysis solution

Thus, A data analysis solution has many components. The analytics performed in each of these components may require different services and different approaches. 

Now, 5 Challenges of Data Analytics

Additionally, how do you know if you have a need for a comprehensive data analysis solution or even a basic analysis solution? Well, ask yourself: are you struggling to support sudden increases in the volume of data you’re dealing with? Or the speed at which new data arrives? The variety of data sources? The accuracy of your data? Whether you’re drawing value from your data?

Above all, I am referring to 5 Vs: volume, velocity, variety, veracity, and value.

Let’s take each one in turn, starting with the first one. Therefore, when I say volume, I mean the amount of data that a solution must handle. Thus, the solution must do it efficiently and be able to distribute the load across enough servers to handle the next V: velocity.

The majority of analytical data comes from existing on-premises databases and file stores

Furthermore, you need to know the options for processing your data. The term processing includes collecting, cleaning, transforming, and loading data into an analytic data store. That’s a lot of work. This process can be handled manually or by using applications to assist in automating this process. 

Above all, AWS Data Engineer Roles and Responsibilities

AWS data engineers have a variety of responsibilities, some of which are:

• Constructing data models that may be utilized to gather information from numerous sources and store it in a helpful fashion
• Data integrity maintenance through the development of backup and recovery mechanisms
• Finding ways to boost performance through enhancing database design
• Researching new technologies and data sources that can be used in projects currently being worked on
• Finding patterns or insights in data that may be utilized to inform business choices or build strategies
• Creating new applications employing existing datasets to provide new goods or enhance current services
• Upgrading old code or adding new features to existing apps to keep them current with changing needs
• Designing and putting in place security measures to shield data from abuse or illegal access
• Making improvements to the infrastructure to increase storage capacity or performance 
• Building data pipelines and handling large datasets
• Using AWS tools to integrate data
• Using Amazon Simple Storage Service for retrieving any or all the data
• Implementing firewall security of AWS using AWS security groups

Streaming data is a source of business data that is gaining popularity

Know the options for processing your data

Additionally, It is fairly easy to install Ansible Tower. The following instructions use Red Hat Enterprise Linux 8 as the base operating system.

Thus, there are many different solutions available for processing your data. Hence, there is no one-size-fits-all approach. You must carefully evaluate your business needs and match them to the services that will combine to provide you with the required results. 

It is vital to spot trends, make correlations, and run more efficient and profitable businesses. It’s time to put your data to work.

Conclusion

We discussed the challenges that can come from working with large data sets that must rapidly produce meaningful insights. In addition, we also introduced you to the five Vs of data analysis and outlined some questions to explore when you start planning your data analysis solution.

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