As server technologies become more affordable, big tech giants are investing a great deal in cloud services. Hardware costs have become incredibly cheaper over the years when compared to the early 2000s. As a result, the subscription cost of cloud services has also gone drastically down, making them an immensely viable option for medium-to-low tier companies. Of course, despite the best Internet connectivity, nothing can beat the latency advantages of locally-housed servers, but these modern cloud enterprises, such as AWS, Google Cloud, and Microsoft Azure, are providing their customers with fully managed, highly scalable and secure, computational and storage resources, with pay-as-you-go pricing models.
A lot of times, dedicating an entire admin team and office space to maintaining a server may prove to be more expensive than renting it out using a cloud service. Organizations that do not require the availability of Big Data clusters and server resources around the clock may also opt for these online solutions. Hence, it is extremely relevant for people aspiring to get ahead in the fields of Big Data and Data Science to get accustomed to cloud platforms.
The skills required to work on data using cloud platforms are highly sought after today. Here, the AWS Certified Big Data – Specialty accolade proves that a candidate is not only proficient in operating individual Big Data services on AWS but is also capable of easily integrating and running multiple Big Data services in conjunction.
In this blog, we are going to discuss the following key aspects of the AWS Big Data certification exam:
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Introduction to Big Data
Data comes in various shapes and forms. In technical terms, every fundamental unit of information stored on a computer system is called data. This data is categorized into three types. The types are mentioned and explained as follows:
- Structured data: It is the data that is present in the form of properly structured, SQL-like tables, with well-defined columns and rows. This data is the most organized category of data that exists and can easily be queried or converted into Data Analytics and Data Science models. For example, tables created within databases in MySQL can be termed as structured data.
- Semi-structured data: As the name suggests, semi-structured datasets have somewhat a structure but are present in the form of files, unlike SQL tables. In most cases, these datasets can be converted into structured datasets by running them through a conversion algorithm. For example, CSV (comma separated) and JSON files are semi-structured because they have a fixed syntax associated with their contents and can be converted into SQL tables pretty easily.
- Unstructured data: Unstructured data is present in a file format and is completely random in appearance. These datasets do not have any fixed structure or syntax associated with them, and it is up to the data expert to derive any meaning out of them. For example, media files such as JPEG, MP3, MP4, etc. are usually random in their content when converted to a text file.
When we take a step further and immensely increase the scale of this data, we get Big Data. However, the criteria aren’t that simple, and for data to be accurately termed as Big Data, there are five conditions that need to be met, called the 5 Vs of Big Data:
The data should be accumulated at a really rapid rate for it to be termed as Big Data. This means that the data should be continuously coming in and should range from terabytes to petabytes per day.
The data should obviously be incredibly high in size or volume for it to be termed as ‘Big’ Data. This requirement should also satisfy the terabyte-to-petabyte scale in most cases.
The data should not come from homogeneous sources but from multiple locations, and it should be of different formats for it to have variety in it.
In a practical environment, the data will seldom be perfect in nature. It will always have inconsistencies, errors, noise, and missing values in it.
The data should always have some meaning, utility, or information that can be used for any sort of analytical or statistical modeling for further Data Analytics or Data Science-related activities.
Now that we understand what Big Data is, let’s move further to understand the tools that are traditionally used to manage Big Data. Obviously, since the data is so large in nature, we cannot use traditional hardware to store it.
To fix this particular issue, we link together a bunch of computers together through networking and install the Hadoop framework on top of these systems. Once the framework is installed on the systems present in the aforementioned network, we can store and process extremely large files by using the combined computing and storage power of these systems.
Traditionally, this network of systems is called a Big Data cluster or a Hadoop cluster. We use the Hadoop distributed file system (HDFS) to store data across these systems and make use of processing/computing frameworks, such as Apache Hadoop MapReduce and Apache Spark, to process the data with the combined power of the cluster of systems.
Now, instead of installing Hadoop and Spark on all these locally present systems or servers, we can simply use AWS tools, such as EMR, to accomplish the same result if the data load is not that high and frequent and requires extremely low latency. There are various tools that help with this, covered under the AWS Big Data Certification. Let’s dive into some of the major ones in the following section.
Big Data Domains and Tools on AWS
In the AWS Big Data certification exam, you will be tested on the central elements associated with the Big Data workflow in the AWS cloud environment.
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Domain 1: Data Collection
Data collection is usually the first step involved in the AWS Big Data workflow. This step occurs when the data producer is generating and streaming the data, which is to be queried or processed. The data cannot be processed until it actually arrives in the Big Data workspace.
This data streaming can be accomplished with various tools. The most popular tool used for this, for extremely large and heavy loads of data, is the Amazon Kinesis service. With Kinesis, you can easily scale up or scale down the capacity of your data streams by using Kinesis shards. Amazon Kinesis consists of four sub-categories of streams, which can be selected as per your particular use case.
Apart from the practical skills of using data collection tools with the help of AWS CLI, Console, and SDKs, a conceptual understanding of using the right tools, security settings, integration, calculations about shard configurations, and the comparison points between various data collection tools should also be known by the candidate when appearing for the certification exam.
Domain 2: Data Storage
Naturally, after you have initiated the collection process, a properly optimized storage space is required to hold the data. Choosing the correct storage space depends on the specific use case. Various tools used for data storage include:
- S3 Glacier: When the data does not need to be accessed frequently and can be stored away for a long time
- Amazon RDS: For SQL data cataloging
Domain 3: Data Processing
The most important step in any Big Data task is to process the data that has been stored or accumulated. This is done mainly through processing frameworks optimized for extremely large amounts of data. The tools that are generally used for this in the AWS environment include:
- Amazon EMR (Elastic MapReduce): Amazon EMR is a combination of EC2 instances mimicking a physical Hadoop cluster. All framework and software installations can be set on the startup to run automatically, alongside selecting the option of scaling up or scaling down the computational resources involved in the cluster according to the dynamically changing workload.
- AWS Lambda: It is a serverless implementation or provision to create reusable functions that can transform data as the data input is being received, before it is sent to the target storage or the output location, with appropriate security permissions in place.
- AWS Glue: It is an ETL (extract-load-transform) tool to process extremely large amounts of data. This is used to prepare the data for the Data Analytics phase, after processing.
Domain 4: Data Analytics
Once the data has been processed and filtered, the filtered information is put through analytical tools to derive insights and answers. There are multiple ways to go about this step, depending on the type and size of data and on the type of analytical routines that need to be executed. These tools are mentioned below:
- Amazon Redshift: Amazon Redshift is the cheapest and fastest cloud data warehousing tool for Big Data use cases. You can easily integrate it with other AWS and third-party applications, such as S3 Data Lake, RDS, EMR, SageMaker, and Quicksight.
- Amazon Athena: Amazon Athena is used to analyzing the Big Data present in Amazon S3 using standard SQL.
- Amazon SageMaker: Amazon SageMaker is a cloud service used to analyze and conveniently create Machine Learning models out of Big Data.
- Amazon Elasticsearch: It is a cloud service used to implement efficient search engines for Big Data files.
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Domain 5: Data Visualization and Security
An AWS Certified Big Data Specialty expert should be able to securely perform all his jobs because data security is key in modern business practices, where everything is online. Amazon provides security features with each of its services, and certified professionals should be well aware of the security practices that need to be followed while they work.
The final key aspect that comes in Big Data Analytics is being able to visualize your findings and display them in a pictorial form to the upper management or collaborating teams with non-technical backgrounds. The findings should be in the form of neatly labeled flow charts and graphs, among other representations, to be comprehended by individuals and teams who require those insights in the most concise way possible. The biggest instance of this would be when the Data Analytics team of an e-commerce organization needs to convey the findings to the marketing and sales teams during quarterly presentations when new strategies are decided.
The go-to data visualization tool that Amazon provides is Amazon QuickSight, which is extremely easy to use. It is highly flexible with large datasets and can generate visualizations quickly with just a few mouse clicks.
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AWS Big Data Certification Exam Details
Let’s now discuss the AWS Certified Big Data Specialty exam details:
- Format: Multiple-choice and multiple-answer questions
- Type: AWS specialty certification
- Delivery method: Through the testing center
- Time allotted: 170 minutes
- AWS Big Data Speciality certification cost: US$300
- Languages available: English, Japanese, Korean, and Simplified Chinese
The AWS Big Data Certification passing score is 750 out of 1000.
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AWS Big Data Certification Tips
The AWS Big Data certification difficulty is moderate to high, so do not take it lightly. Although all topics mentioned above should be thoroughly studied before attempting the AWS Certified Big Data Specialty examination, there are some key subjects that you can focus on:
- Amazon Kinesis
- Amazon DynamoDB
- Amazon EMR
- Amazon Redshift
- AWS Lambda
Going through the entire syllabus with a special emphasis on the above topics is certain to give you the edge required to qualify for the AWS Big Data Certification exam on the first attempt.
AWS Big Data Certification Practice Exam
Amazon also provides you with multiple resources to test if you currently have what it takes to qualify for the certification exam and to know how much more you need to learn to make yourself a true contender.
You can check out AWS Big Data certification exam questions for AWS Certified Big Data Speciality to have a self-assessment:
AWS Big Data Certification Preparation
There are various ways you can go about preparing for the AWS Big Data certification exam. But at a hefty exam fee of US$300, it is in your best interest to make your first attempt successful. So, how should you go about this?
Your first option would be to search for study material and videos for each of the topics online, which could be completely disorganized without any proper course curriculum or structure. Often, this methodology jumbles up certain concepts and can prove to be extremely inefficient with regard to time. Another issue with such learning is when the technology gets upgraded. Then, the course material and syllabus may get tremendously confusing. Moreover, in the case of AWS Certified Big Data Speciality, there isn’t a lot of comprehensive material available online as well.
The alternative to completely relying on self-study would be to opt for a well-organized course on the subject matter. At Intellipaat, we have realized the lack of study material online, alongside the absence of hands-on exercises, and have come up with the AWS Big Data Training. The program covers the entire exam syllabus in detail with qualified instructors taking live classes, alongside comprehensive self-paced videos demonstrating various topics through diagrammatic and hands-on content.
Be sure to check out our course and contact our course advisors, who are available 24/7 to resolve all your course doubts if you have any.
Intellipaat wishes you the best of luck with your AWS Big Data certification journey and career!