How to Prepare for the New AWS Big Data Certification
How can you prepare for the new Amazon Web Services (AWS) Big Data certification exam? Effectively harnessing and managing big data has become a competitive advantage for companies and a key component of an organization’s digital transformation. However, as companies collect all kinds of data, IT teams are often left to unravel the impact of this data on IT infrastructure. When it comes to scaling existing databases and applications, IT teams are currently wrestling with many big data challenges. Recognizing this challenge, the new AWS Big Data Certification covers innovative technologies that take the headache out of big data and allow organizations to quickly collect, process, analyze and act on their data while also reducing costs and gaining efficiencies. In order to successfully achieve your organization’s digital transformation, you and your IT team will need to upskill on these key AWS big data technologies.
What is big data?
First what kind of data is “big data?” Big data is often described in terms of the “three Vs:”
Volume: Big data involves large quantities of data measured in terms of terabytes or petabytes.
Velocity: Big data is usually created at a rapid rate (e.g. social media feeds) and needs to be processed quickly.
Variety: Big data is made up of a wide variety of different sources like text, images, video, audio, email, databases, spreadsheets, and presentations.
When existing databases can no longer scale to support sudden increases in the volume, velocity, and variety of data, your IT infrastructure will likely need to upgrade to AWS big data technologies.
What are big data examples?
What are the use cases of how organizations are leveraging big data to gain competitive advantage? While I (Frank) worked at Amazon for many years, I used big data, algorithms, and machine learning to build their powerful recommendations engine. Other use cases at other industries include building dashboards that capture a 360 view of the customer, so that customer service, sales, and marketing can be smarter and more targeted with their customer interactions. Fraud prevention is another good example. Credit card companies use big data analytics and machine learning to detect criminal activity based on historical patterns and predictive analytics. For example, fraud often happens in certain zip codes or isolated purchases (e.g. plane tickets to the Caribbean) without related purchases (e.g. swimsuit, sunblock) can look suspicious.
How to prepare for the AWS big data certification exam
In our AWS Certified Big Data Speciality course on Udemy, we help IT pros prepare for this new and difficult exam. There are not that many courses or materials out there since this is a new exam, and our course is one of the first comprehensive prep courses. Find out how a Udemy for Business subscription can prepare your IT team for the AWS Big Data Certification Exam.
First, you should understand the menu of AWS big data technologies and how they fit together in the larger IT ecosystem. In particular, IT pros need to master big data technologies along the entire data lifecycle–from data collection and processing to analysis and visualization. These cool new tools make it possible to collect and store large datasets while enabling you to analyze data in real-time and uncover actionable insights for your business.
Top 4 AWS big data tools to master
These are the 4 hot big data technologies that IT pros need to become well-versed in for the certification exam:
- Kinesis: Real-time collection of data
- Dynamo DB: Storage of a large amount of data in a semi-structured cloud and a hosted NoSQL database.
- EMR: A Hadoop cluster in the cloud for big data processing jobs using Spark.
- Redshift: A data warehouse that lets you process a lot of data quickly and in a scalable way.
These 4 big data technologies are among the 25 tools we cover in our course. It is also important to understand how these technologies work together as part of a larger system architecture and data lifecycle.
Master the 6 domains along the data lifecycle
IT pros will need to master a wide range of AWS big data tools along the 6 domains of the data lifecycle for the AWS big data certification exam.
Collection: This is about building the right platform to collect your raw data, whether it’s from transactions, logs, mobile devices or whether it’s structured data (spreadsheets) or unstructured data (images, videos). You’ll need to master AWS tools like Amazon Kinesis and Snowmobile. Amazon Kinesis lets you ingest real-time data like video, audio, and website clicks and then analyze this data as it arrives to gain actionable insights and respond on the spot. You don’t have to wait until all the data is processed before analyzing it. AWS Snowmobile can move extremely large amounts of data to AWS servers like video libraries, image repositories or even complete data centers.
Storage: Data lakes are a popular way to store and analyze structured (data spreadsheet) and unstructured data (videos, images). Dynamo DB allows for storage of a large amount of data in a semi-structured cloud and a hosted NoSQL database.
AWS Glue is an extract, transform and load (ETL) service that can make all your data immediately available for analytics without having to move the data. Moreover, AWS Glue is serverless, so there is no infrastructure to provision or manage. You only pay for applications that are running.
Processing: In this part of the data lifecycle, your raw data is transformed into a more usable format. This usually involves transforming unstructured data into structured data or aggregating data in interesting ways that enable you to extract it. AWS EMR provides a Hadoop framework to quickly and cost-effectively process large amounts of data. You can run other frameworks such as Apache Spark in EMR for high-velocity and real-time processing. For the certification exam, you’ll need to know how to set these tools up, how to use them, how to configure them, how they fit into the larger ecosystem, and their limitations. The exam is difficult and IT pros will need to master this information in-depth.
Analysis: Redshift is Amazon’s massive scale data warehouse where you can run SQL queries. AWS also offers a serverless data warehouse called Athena. You only pay for the data use when you run a query.
Consumption and visualization: At the end of the day, big data is all about gaining actionable insights for the business. Amazon Quicksight service provides self-service business intelligence tools and data visualization tools that allow stakeholders to create cool graphs and visualizations that can be shared with a wider audience. Amazon Quicksight is offered on a pay-as-you-go model. AWS Quicksight works with a variety of other tools like Excel. Stakeholders may also want to consume data insights using predictive analytics to predict, for example, the likelihood of fraud, security breaches, or purchases and recommend specific actions.
Separately, the security of the data across these technologies is also an important topic on the certification exam. IT pros will need to know how to secure the data and protect it against hackers while ensuring data scientists get access to the data when they need it. In our course, we prepare IT teams through a simulated hands-on exercise. We build an alarm system, simulate orders coming in via e-commerce, funnel the data through Kinesis, and then analyze the data. If you see a surge in traffic in the data, then this will trigger an alarm or fire up your cell phone alert to let you know.
New trends in the AWS big data space
Finally, new big data technologies like Tensorflow, Hadoop, Spark, and AWS Snowmobile allow you to process and move tremendous amounts of data–unlocking the power of your data science team. For example, you can essentially copy your data into a “snowmobile truck” and move them around. At a recent AWS show, they actually rolled out semi-trailer truck on the stage to make this point. As a previous Amazon employer, I (Frank) was part of Amazon’s early investment in machine learning to power the recommendations engine as well as increase operational efficiencies and reduce retail delivery time. Amazon has always been fascinated by the intersection between machine learning, deep learning, and data. In fact, the next certification exam they’ve released focuses on AWS and machine learning.
Who should take this course?
Finally, who should take this course? The exam is really hard (as we mentioned earlier), so you’ll need to dedicate time to prepare for the exam and our AWS Big Data Certified Speciality course on Udemy is a good place to start. A line manager who is supervising engineers should take this certification exam, but a VP doesn’t need to. If you’re a hands-on technical IT manager, development manager, big data, or solutions architect, this course will provide you with the latest tools to manage and harness big data insights to drive your business forward.
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