AWS provides services for every domain such as computing, data storage, data analytics, robotics, and many more. In total, AWS services cover 25 domains across the IT infrastructure. AWS has been leading as the best cloud service provider for more than a decade now. Often, it is noticed that organizations utilize AWS to make data-related tasks easier and faster.
In this blog, let’s see how AWS helps companies in managing their Big Data. The following is a list of topics that will be discussed in this blog on AWS Big Data.
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What is Big Data?
You may have some idea of Big Data as it has become a very common term in the IT industry. To understand this term completely, let’s dive a little deep and look at its concepts.
Big Data cannot simply be defined as huge amounts of data stored. However, one can say that it is the large amounts of data that can be made useful for various purposes. To understand Big Data, we have to first know what data is. Data is information stored in three forms as follows:
- Structured data: Raw data or information is transformed into structured, extractable, and reliable data. Structured data is easily extractable for queries and analytical-related tasks. Data stored in SQL tables in the form of rows and columns is the best example of structured data. Relational databases make it simpler to manage and map the data.
- Semi-structured data: As the name suggests, it is the data that can be extracted to some extent for analysis and queries. It is difficult to store in the form of a table-like structure and map it. The best examples are JSON files and CSON files. They can be converted into SQL tables with the help of conversion algorithms.
- Unstructured data: Unstructured data is data we see on social media sites. They are mostly text-heavy, along with videos and images falling under the category. Because of this, it is difficult to read. It has no predefined syntax or data model. However, it may contain vital information such as dates, names, and facts. IT organizations utilize it with the help of AI and Machine Learning algorithms. Few examples you can relate to are PDF files, social media content, media files such as JPEG and MP3 when converted into text files, etc.
However, a dataset being huge does not imply that it falls under the category of Big Data. There is a fixed set of criteria for any data to be defined as Big Data. Known as 5Vs, it contains the following five conditions for Big Data:
- Volume: It is sort of obvious that the scale of data should be large, ranging from TBs (terabytes) to PBs (petabytes), for it to be called Big Data.
- Velocity: The speed of data accumulation from various resources must be rapid, irrespective of the amount of data.
- Variety: The data should be accumulated from various resources.
- Veracity: The data accumulated in huge amounts from various resources, practically, cannot be perfect in nature. It will contain various inconsistencies such as missing values, duplications, etc.
- Value: The data should have some value or contain useful information that can be utilized for analytical purposes.
Therefore, Big Data is not just ‘Big Data’! It has a broader concept, and most importantly, it must be useful to organizations for various business purposes. Luckily, cloud services can handle unlimited storage virtually and provide enough computing power for dealing with Big Data. In our case, the cloud provider is AWS, so let’s have a brief introduction to AWS.
A Brief Introduction to AWS
AWS is popular in the IT industry because of its reliability, scalability, and security. It has a pay-as-you-go pricing model and easy-to-deploy services, which are just a few of its best features. AWS is a subsidiary of Amazon, which was launched in 2006 with a mere number of three services. Now, it provides more than 200 services in 25 domains, covering all the IT services that an organization needs. Shifting to AWS can increase business revenue substantially as there is no upfront cost and it makes complex tasks simpler.
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As you know what Big Data and AWS are, now it will be much easier for you to comprehend ahead.
What is AWS Big Data?
Many services that AWS offers are utilized to manage Big Data. Without worrying about hardware, reliability, and security, organizations completely rely on AWS services for their Big Data needs. AWS’s integrable services make it easier to manage Big Data throughout the pipeline, i.e., from extraction to end-user consumption. Let’s understand the key reasons why AWS is chosen over other services for handling Big Data.
- Availability: AWS services are available throughout the data flow, irrespective of the scale of data.
- Ingestion: Organizations require high-speed data extraction from sources to storage. With the help of different AWS services data is extracted from sources in seconds.
- Computing: AWS services are powered with high computing capabilities to perform operations on Big Data.
- Storage: To store data without worrying about leakage or exposure is a hectic task for companies. AWS storage services, like Amazon S3, can reliably and securely store PBs of data and perform operations on it.
- Analysis and visualization: Every organization wants to utilize data for business growth and profits by performing analyses on it and getting key insights as output.
- Security: In the data pipeline, any error or security outage can lead to major issues for companies. AWS’s integrable security services provide high security for the data, with the help of security policies and compliances.
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These are the reasons AWS is the most trusted cloud service provider, especially when it comes to Big Data. Now let’s see, AWS Big Data tools and services provided by Amazon Web Services.
AWS Services for Big Data
AWS offers numerous services across various domains. To handle Big Data efficiently in the domains discussed above, the following are AWS services used:
Ingestion
- Kinesis Firehose: Kinesis Firehose is utilized for the extraction of real-time streaming data into S3 buckets. It does not require any administration, and you can configure it for the compression and encryption of data.
- Snowball: Amazon Snowball is used to extract Big Data from on-premises hardware and Hadoop clusters to S3 buckets. It is the most secure and efficient tool for data extraction.
- Storage Gateway: AWS Storage Gateway can be used to provide access to on-premises hardware storage and extract data from it to the S3 Data Lake.
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Analysis and Visualization
- Amazon Redshift: Redshift is the most trusted cloud data warehousing tool for Big Data. It is popular due to its features such as pricing and the speed of data flow. Also, you can easily integrate it with other AWS services, such as S3, RDS, EMR, on-premises storage, and even third-party applications. With its integrable features, organizations utilize it for quick analysis and real-time query extraction.
- Amazon Athena: Amazon Athena is used for analyzing data stored in AWS S3 with the help of standard SQL. It is easy to manage, and you have to pay only for the queries that you run.
- Amazon SageMaker: AWS SageMaker is used by Data Scientists to create Machine Learning models. It provides an IDE for ML, where they can develop, train, and deploy ML models. ML models are developed for performing predictive analyses on Big Data.
- Amazon Elasticsearch: AWS Elasticsearch service offers a fully managed application-monitoring platform and efficient search engines. It also helps you manage it cost-effectively, according to your requirements.
- Amazon QuickSight: Amazon QuickSight offers a strong ML-based BI (Business Intelligence) tool that lets you create a BI dashboard. ML-based insights are easily accessed with the help of this dashboard. Below is an example diagram of AWS Big Data architecture.
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Storage:
- S3 Glacier: S3 Glacier is the most used AWS service for data storage. It provides high scalability and reliability features to store data in large amounts. It can store data in petabytes with almost 100% durability.
- Amazon DynamoDB: DynamoDB is a NoSQL database service from AWS, which can handle more than 10 trillion requests per day and more than 15 million requests per second.
- Amazon RDS: It is called the Relational Database Service and is used to create, manage, and operate relational databases completely. It also provides popular data engines such as MySQL, MariaDB, and Oracle.
- AWS Lake Formation: With the help of AWS Lake Formation, organizations can set up data lakes in a few days. A data lake is the central data storage service that contains data in the raw form or also in a structured form, ready for analysis.
Computing:
- Amazon EMR (Elastic MapReduce): AWS EMR is one of the industry-leading Big Data tools used to process high-scale data without performing any administration tasks, such as tuning the clusters and their capacity.
- AWS Glue: AWS Glue is used for the extraction, transformation, and loading of Big Data for Machine Learning, analysis, and application development. It offers a fully managed and easy-to-use data integration service, which you can set up and get the output in a matter of minutes.
Security:
- AWS IAM (Identity and Access Management): AWS IAM lets you control access to all your AWS resources. Also, you can easily set up access authorization for desired users, depending on the service.
- AWS KMS (Key Management Service): AWS KMS offers data encryption for the complete AWS data pipeline and also security keys for providing access to AWS resources.
With the help of the above-mentioned AWS services, organizations can easily operate and manage data pipelines. Managing Big Data is a hectic task, but with the right integration of these AWS services, organizations can get maximum efficiency.
To understand how companies utilize these AWS Big Data technologies, we will look into a use case.
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AWS Big Data Use Case
In this section, we will discuss the case study of Big Basket and how they utilized AWS for Big Data.
Big Basket is India’s largest online grocery shopping platform. According to Business Standard, it is currently recording around 20 million orders per month, and its subscriber base has grown by 84 percent in 2021. This boom occurred during the COVID-19 lockdown.
All these years, Big Basket has been using numerous AWS services. Let’s discuss some key services the company is using, and how they helped it to face its surging business growth.
- AWS Redshift and AWS S3: These are used to manage the data warehouse and the data lake, respectively. To attain maximum customer retention during the surge, Big Basket uses these services extensively for recording customer behavior. It then uses the key insights to make the customer experience better on the platform.
- AWS Elasticsearch: To cater to the local demand in the best way possible, Big Basket uses Elasticsearch for the geo-analysis of various cities. The company can thereby stack the inventory with the right products based on the demand from the local area. To make all the right products readily available, inventories are used for storing the right products in the defined service area.
- AWS RDS: Big Basket has been using RDS for the past 5 years. During this COVID era, the database started to grow substantially. This increased the usage of RDS and hence increased the usage cost. However, since RDS can be optimized for scaling, it decreases the cost drastically.
Hence, with the help of AWS services, Big Basket has been able to handle the data surge for months. With the right scaling and application of services, the company could seamlessly manage the data flow.
Many other organizations are also able to operate effortlessly in the COVID era. Due to this online user base surge, organizations are in dire need of Certified Data Engineers who can manage AWS Big Data services just like how Big Basket did it. Let’s move ahead and discuss the certification you should go for if you are looking for a career in Data Analytics.
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AWS Big Data Certification
To get better opportunities, salary, and your dream AWS Big Data jobs, IT professionals go for certifications that fit best for their career path. AWS Certified Big Data Specialty certification is the best certification for the AWS Big Data career path.
AWS Big Data Specialty Certification is an industry-leading certification for Big Data Analysts. Professionals working on AWS infrastructure with at least 1–2 years of experience can go for the certification. The following key skills will be validated if you clear this certification:
- Deploy and manage AWS Big Data infrastructure
- Plan and manage Big Data infrastructure
- Use tools and services for the automation of data analysis
Experienced Data Analysts or experienced AWS Cloud Architects are suggested to go for the certification because the exam tests:
- Your knowledge of Big Data infrastructure
- Your knowledge in deploying AWS services for Big Data
- Your skills in Data Analysis
Now if you want to go for the certification, let’s briefly discuss the exam details:
- Format: MCQ-based exam
- Duration: 170 minutes
- Cost: US$300
- Languages: English, Japanese, Korean, and Chinese
The level of the exam directly depends on your knowledge and experience. With proper knowledge and experience, you will easily be able to attain the certification. After giving the exam successfully, you will receive your certificate through email. Also, you can add it as a digital badge to your LinkedIn profile. Certifications can help you land better jobs. In the next section, we will discuss the jobs and salaries of AWS Data Engineers.
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Career in AWS Big Data
According to PayScale, the average salary of an AWS Data Engineer is US$97,786 p.a., and the maximum salary goes up to US$134,000 p.a. The average salary of an AWS Data Engineer in India is around ₹9 LPA, and the maximum pay goes up to ₹28 LPA.
Organizations are increasingly utilizing cloud-based solutions. According to Amazon, AWS had a revenue of US$12.7 billion in Q4, 2020. Consequently, there is an increased demand for AWS Data Engineers, which is going to remain the same for the years to come. So, start today by signing up for Intellipaat’s AWS Big Data Certification and make a lucrative career!