The AWS Certified Big Data Specialty certification is designed to validate an individual’s expertise in designing, implementing, and managing big data solutions on the AWS platform. Preparing for the AWS Certified Big Data Specialty exam requires a comprehensive understanding of various AWS big data services, data processing frameworks, data storage options, and machine learning models. Amazon Web Services (AWS) provides a broad range of big data tools and services to handle and analyze large data sets, making it a popular choice for businesses.
AWS introduces new certifications to validate the skills of candidates in new AWS technologies. The AWS Certified Big Data Specialty exam has expired. It has been replaced by the AWS Certified Data Analytics Specialty exam. You must stay updated with current changes to reach new heights in your career.
However, preparing for the AWS Certified Big Data Specialty exam can be a challenging task, given the vast amount of information one needs to master. That’s where a cheat sheet comes in handy, providing a quick reference guide to the essential topics covered in the exam. In this blog, we will explore the AWS Certified Big Data Specialty Cheat Sheet and how it can help you prepare for the exam more efficiently.
How to prepare your own AWS Certified Big Data Specialty Cheat Sheet?
- Make sure you understand the exam format and the types of questions that will be asked. This will help you allocate your time more effectively during the exam.
- Start by reviewing the AWS Certified Big Data Specialty exam guide to understand the exam objectives and topics.
- Familiarize yourself with the AWS big data services, including their features, use cases, and limitations. Pay special attention to services like Amazon Kinesis, Amazon Redshift, AWS Glue, and AWS Athena.
- Review key big data concepts and best practices, such as data collection, storage, processing, analysis, visualization, security, data architecture, and data transformation.
- AWS provides comprehensive documentation for all its services. Use the AWS documentation to understand the services and their features in detail.
- Don’t rely on a single source of information. Use a combination of AWS documentation, whitepapers, online courses, and practice exams to get a well-rounded understanding of the topics.
- Try using memory aids like mnemonics or acronyms to remember key concepts and details.
- Joining a study group or forum can help you get additional support and insight from other learners who are preparing for the same exam.
Remember, the key to passing the AWS Certified Big Data Specialty exam is to understand the core concepts, learn how to apply them in real-world scenarios, and practice using AWS services and tools. With hard work, dedication, and thorough preparation, you can ace the exam and become a certified AWS Big Data Specialist.
1. Data Collection
To pass the AWS Certified Big Data Specialty exam, you’ll need to be familiar with a variety of big data concepts and technologies. One important area is data collection, which involves gathering data from various sources and preparing it for analysis. This includes understanding different data processing methods such as batch processing, stream processing, and real-time data processing.
- Batch processing: involves processing large volumes of data at once, often using tools like Apache Hadoop or Amazon EMR.
- Stream processing: involves processing data in real-time as it flows into the system, using technologies like Amazon Kinesis or Apache Kafka.
- Real-time data processing: involves processing and analyzing data as soon as it’s generated, often using tools like Apache Spark or Amazon Kinesis Analytics.
- Data collection also involves choosing the right data sources, identifying data quality issues, and ensuring data security and compliance.
2. Storage
One of the key components of any big data solution is the storage and management of data. In the AWS Certified Big Data Specialty exam, candidates must be familiar with various storage options available in AWS, including Amazon S3, Amazon EFS, Amazon EBS, and Amazon Glacier. Here are some key points to keep in mind about each of these storage options:
- Amazon S3: A scalable, highly durable, and secure object storage service that can store and retrieve any amount of data from anywhere on the web.
- Amazon EFS: A fully managed, elastic file system that provides scalable, shared access to files from multiple Amazon EC2 instances.
- Amazon EBS: A block-level storage service that provides persistent storage volumes for use with Amazon EC2 instances.
- Amazon Glacier: A secure, durable, and low-cost storage service designed for data archiving and backup.
Each storage option has its own unique features and uses cases, and candidates for the AWS Certified Big Data Specialty exam should be familiar with when and how to use each one.
3. Data Processing
Data processing is an important component of big data analytics, and the AWS Certified Big Data Specialty exam covers various data processing technologies such as Apache Hadoop, Apache Spark, and Amazon EMR. These technologies provide scalable and efficient ways to process large volumes of data. Here are some key points to keep in mind for this topic:
- Apache Hadoop is a distributed data processing framework that uses the Hadoop Distributed File System (HDFS) to store and process large datasets across clusters of computers.
- Apache Spark is a fast and flexible data processing engine that supports various data sources and processing models, including batch processing, stream processing, machine learning, and graph processing.
- Amazon EMR (Elastic MapReduce) is a fully managed service that provides a scalable and easy-to-use platform for running Hadoop, Spark, and other big data applications on AWS.
The AWS Certified Big Data Specialty exam may also cover other data processing technologies such as Apache Flink, Apache Beam, and Apache Apex, which provide alternative ways to process and analyze big data.
4. Analysis
The AWS Certified Big Data Specialty exam is designed to test the skills and knowledge of individuals working with big data technologies on the AWS platform. One important area of focus on this exam is analysis, which involves understanding the different types of analysis that can be performed on large datasets. Here are some key points to keep in mind when studying for this section of the exam:
- Descriptive analysis: involves summarizing and describing data, and is often used to gain a basic understanding of a dataset.
- Diagnostic analysis: involves identifying the root causes of problems or issues, and is often used in troubleshooting or root cause analysis.
- Predictive analysis: involves using data to make predictions about future events or trends, and is often used in forecasting or predictive modeling.
- Prescriptive analysis: involves providing recommendations or actions based on data insights, and is often used in decision-making or optimization scenarios.
- To perform effective analysis, it’s important to have a solid understanding of statistical concepts, data modeling techniques, and machine learning algorithms.
AWS offers a range of services and tools that can be used to support different types of analysis, including Amazon Redshift for data warehousing, Amazon EMR for big data processing, and Amazon Machine Learning for predictive modeling.
5. Visualization
Visualization is an essential part of big data analysis and understanding. The AWS Certified Big Data Specialty exam tests your knowledge of different visualization techniques and tools, including Amazon QuickSight, Tableau, and Apache Zeppelin. Here are some key points to keep in mind:
- Visualization techniques help to present complex data in a more understandable and actionable form.
- Amazon QuickSight is a business intelligence (BI) tool that allows you to create visualizations, and dashboards, and perform ad-hoc analysis.
- Tableau is a powerful data visualization tool that helps to create interactive and informative dashboards and reports.
- Apache Zeppelin is an open-source web-based notebook that supports data visualization and collaboration features.
- Knowledge of data visualization best practices, including using the right chart types, colors, and labels, is also essential for the exam.
6. Security
The AWS Certified Big Data Specialty Security certification provides individuals with the knowledge and skills required to implement and maintain security best practices for Big Data solutions. This includes understanding encryption, access control, and network isolation. In this certification, candidates learn how to secure data throughout its lifecycle, from ingestion to analysis and storage. In this task, I will elaborate on the security best practices covered in this certification in one-liner points.
Here are some of the key security best practices covered in the AWS Certified Big Data Specialty Security certification:
- Implementing encryption for data at rest and in transit.
- Implementing secure access control to protect data from unauthorized access.
- Implementing network isolation to protect data from unauthorized network access.
- Implementing secure data storage practices.
- Implementing security monitoring and logging to detect and respond to security incidents.
- Implementing security best practices for data processing, including data validation and cleansing.
- Understanding security requirements for compliance frameworks such as HIPAA, GDPR, and PCI DSS.
- Implementing security for Big Data architectures such as Hadoop, Spark, and EMR.
7. AWS Big Data Services
AWS offers a wide range of big data services to help organizations process and analyze large datasets efficiently. As an AWS Certified Big Data Specialty candidate, you must be familiar with these services to effectively design and implement big data solutions. Here are some key AWS big data services to focus on for the exam:
- Amazon Kinesis: A real-time data streaming service that enables you to ingest, process, and analyze streaming data.
- Amazon Redshift: A fully-managed data warehousing service that enables you to store and analyze petabyte-scale data.
- AWS Glue: A fully-managed extract, transform, and load (ETL) service that makes it easy to move data between data stores.
- AWS Athena: An interactive query service that enables you to analyze data in Amazon S3 using SQL.
- Amazon EMR: A managed Hadoop and Spark service that makes it easy to process large amounts of data.
- Amazon DynamoDB: A fully-managed NoSQL database service that can handle high volumes of data and supports low-latency queries.
- Amazon SageMaker: A fully-managed machine learning service that enables you to build, train, and deploy machine learning models at scale.
- Amazon QuickSight: A business intelligence service that enables you to create interactive visualizations and dashboards.
Remember, the AWS Certified Big Data Specialty exam covers a wide range of topics related to big data, and having a good understanding of these AWS services is crucial to passing the exam.
8. Data Architecture
As an AWS Certified Big Data Specialty professional, understanding data architecture is crucial. This involves designing data models and structures that efficiently handle big data using different techniques and tools. Here are some important points to keep in mind as a cheat sheet for the data architecture aspect of this certification.
- Use services like Amazon S3 and Glacier for scalable, durable, and cost-effective storage solutions
- Implement distributed computing frameworks like Apache Hadoop and Apache Spark for processing big data
- Leverage data warehousing solutions like Amazon Redshift for faster querying and analysis of large datasets
- Implement streaming data solutions using services like Amazon Kinesis
- Use database services like Amazon RDS and Amazon DynamoDB for managing structured data
- Implement data pipelines using services like AWS Glue and Amazon EMR
- Use data encryption and access controls for data security and compliance
- Implement data lakes using AWS Lake Formation for storing, cataloging, and analyzing large amounts of data
- Design data models that meet business requirements and optimize for query performance
- Use AWS CloudFormation and AWS CloudTrail for automating infrastructure deployment and logging and auditing of API calls, respectively.
9. Data Transformation
Data transformation is a crucial step in the big data pipeline, where data is extracted from various sources, transformed, and loaded into a target data store. The AWS Certified Big Data Specialty exam covers different ETL (extract, transform, load) tools and techniques used to process and transform data. Here are some key points to keep in mind:
- Extract: Collecting data from different sources such as databases, files, and streams.
- Transform: Converting the data into a desired format, cleaning it up, and applying business rules.
- Load: Storing the transformed data into a target data store such as a data warehouse or a data lake.
- AWS ETL Services: Familiarity with various AWS ETL services such as AWS Glue, AWS Data Pipeline, and AWS DMS.
- ETL Tools: Understanding popular ETL tools such as Apache NiFi, Talend, and Informatica.
By understanding these concepts and techniques, you’ll be better equipped to transform and process big data in the AWS ecosystem.
10. Machine Learning
As an AWS Certified Big Data Specialty, you need to have a comprehensive understanding of various big data concepts, technologies, and tools. One crucial aspect is machine learning, which helps to make data-driven decisions and predictions. In this cheat sheet, we will highlight some key points related to machine learning that you should be familiar with as an AWS Certified Big Data Specialty. These points will serve as quick reference notes that you can use to refresh your knowledge and prepare for the certification exam:
- Machine learning involves using algorithms to analyze data, learn from it, and make predictions or decisions without being explicitly programmed.
- Some common machine-learning techniques include supervised learning, unsupervised learning, and reinforcement learning.
- AWS offers various machine learning services, including Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend, among others.
- Amazon SageMaker is a fully managed machine learning service that provides developers and data scientists with the ability to build, train, and deploy machine learning models at scale.
- Amazon Rekognition is a service that provides image and video analysis, object recognition, and facial analysis capabilities using deep learning algorithms.
- Amazon Comprehend is a natural language processing service that helps to extract insights and relationships from text data.
- AWS offers various APIs for machine learning, including Amazon Polly for text-to-speech conversion and Amazon Translate for language translation.
- To work with machine learning on AWS, you need to have knowledge of programming languages such as Python and R, as well as familiarity with data processing and analysis tools such as Apache Spark and Hadoop.
Exam preparation resources for the AWS Certified Big Data Specialty exam
Preparing for the AWS Certified Big Data Specialty exam requires a deep understanding of big data concepts, AWS services and tools, and best practices for big data architectures. Here are some exam preparation resources that can help you achieve success:
AWS Big Data Specialty Certification Exam Guide: The official guide from AWS provides an overview of the exam, including its structure, content, and suggested study resources. It also includes information on the exam domains and objectives, as well as sample questions and answers.
AWS Big Data Specialty Training: AWS offers training courses that cover the key concepts and skills needed to pass the exam, including AWS Big Data Specialty Certification Exam Readiness Workshop and AWS Big Data Specialty Certification Exam Readiness: Data Architecture. These courses are designed to provide hands-on experience with AWS services and tools used in big data architectures.
AWS Big Data Specialty Practice Exam: AWS offers a practice exam that simulates the actual exam experience and helps you assess your readiness for the real exam. This practice exam includes a variety of question types and provides detailed explanations for the correct answers.
AWS Big Data Specialty Sample Questions: AWS provides sample questions that are similar in format and difficulty to the actual exam questions. These questions cover the exam domains and objectives and help you understand the types of questions you can expect on the exam.
AWS Big Data Specialty Whitepapers and Documentation: AWS provides a wide range of whitepapers and documentation that cover various aspects of big data, including data lakes, data warehousing, and data streaming. These resources provide detailed information on AWS services and tools, best practices for big data architectures, and real-world use cases.
AWS Big Data Specialty Community and Forums: The AWS community and forums are a great place to ask questions, get answers, and connect with other individuals who are also preparing for the exam. You can learn from the experiences of others and get advice on exam preparation strategies.
Online courses and tutorials: There are various online courses and tutorials available on platforms like Coursera, Udemy, and LinkedIn Learning that cover the topics related to the AWS Big Data Specialty exam. These courses provide in-depth coverage of AWS services and tools, big data concepts, and best practices for big data architectures.
Remember to allocate enough time for studying, practice using hands-on exercises and projects, and test your knowledge with the practice exam and sample questions. By leveraging these exam preparation resources, you’ll be well on your way to passing the AWS Certified Big Data Specialty exam. Good luck!
Expert’s Corner
Passing the AWS Certified Big Data Specialty exam can be a challenging task, but with the right study materials and preparation, it is achievable. One helpful resource for exam preparation is a cheat sheet that condenses and organizes the key concepts, formulas, and best practices that you need to know for the exam. Overall, the AWS Certified Big Data Specialty certification is a valuable credential that can open up many career opportunities in the growing field of big data. With the right study materials and dedication to learning, you can achieve your goal of becoming a certified big data specialist on AWS.
In this blog, we have provided a comprehensive cheat sheet for the AWS Certified Big Data Specialty exam, covering a wide range of topics such as data collection, storage, processing, analysis, and visualization. By studying and practicing with this cheat sheet, you can improve your chances of passing the exam and earning valuable certification.
However, it’s important to remember that the cheat sheet alone is not enough to guarantee success on the exam. You should also have hands-on experience working with big data technologies and platforms such as Amazon S3, Amazon EMR, Amazon Redshift, and Amazon Kinesis. Additionally, you should review the AWS documentation, whitepapers, and practice exams to further enhance your knowledge and skills.
