We all know AWS stands for Amazon Web Services which is a subsidiary of Amazon and a profound name in the global market of Cloud Computing. AWS is regarded as the most trusted, well-grounded, and secure cloud services provider for every crucial domain such as computing, data storage, data analytics, robotics, and many more. According to a CSA (Cloud Security Alliance) report, AWS consists of 41.5% of the cloud computing market which is much more than all of its competitors -Microsoft Azure (29.4%), Google Cloud (3.0%), and IBM (2.6%). Also, AWS services cover a total of 25 domains through the IT infrastructure.
Now, coming to the discussion of AWS Big Data, it can be said that “Big Data” is an umbrella term describing huge amount of data that can be structured, unstructured, and semi-structured collected from a diverse range of sources. In fact, the data volume is messy and so massive that the traditional techniques as well as databased can’t manage. This is why, AWS provides certain solutions for managing the big data.
To dive deep into the discussion of the significant options that you should consider to handle huge amount of data, first we need to understand the concept of AWS Big Data properly.
What AWS Big Data mean?
The term AWS Big Data refers to the storage, collection, and utilization of large-scale data within AWS, supported by various services such as highly scalable storage, analytics and compliance regulation support. AWS Big Data solutions can be utilized for durability, provisioning, availability, recovery, and backup services.
AWS offers a range of solutions that facilitate the management of the entire big data cycle. The availability of exclusive tools and technologies within AWS make it affordable and easy to collect, store, and analyze data sets, as well as process, consume, and visualize them efficiently.
There are five significant options to consider when managing big data with AWS.
5 Big Data Analytics Options on AWS you should consider
AWS provides the most outstanding support for handling big data sets and its implementations in the form of analytics solutions. With AWS, you can make use of a variety of services which will enable you to automate the processes of data analysis, manipulate datasets, and derive valuable insights.
- Amazon EMR for distributed computing
- Amazon Machine Learning for easy machine learning model development
- Amazon Redshift for cloud data warehousing
- Amazon QuickSight for ad-hoc data analysis and visualization
- Amazon Kinesis for real-time streaming data processing and analysis.
Amazon EMR, also known as AWS EMR, is a distributed computing framework that easily scales and manages big data workloads using Apache Spark, Hive, Presto, and other tools. It is based on Apache Hadoop and clustered EC2 instances. Hadoop is a well-established framework for big data processing and analysis.
By implementing AWS EMR for big data management, the infrastructure for Hadoop is efficiently provisioned, managed, and maintained, allowing users to focus on analytics. This industry-leading cloud big data solution enables petabyte-scale data processing, interactive analytics, and machine learning using open-source frameworks like Apache Spark, Apache Hive, and Presto.
Recently, AWS EMR introduced Amazon EMR Serverless, a new option that simplifies and reduces the cost of operating open-source big data framework applications such as Apache Spark, Hive, or Presto, without requiring cluster tuning, operation, optimization, security, or management.
Amazon Redshift is a cloud data warehouse service that is fast, easy to use, and popular. It can be used for business intelligence analytics and is optimized for handling large structured and semi-structured data queries using SQL. Query results are stored in S3 data lake storage for later use with various analytics services like SageMaker, Athena, and EMR.
Redshift uses SQL to analyze data across data warehouses, operational databases, and data lakes. It utilizes AWS-designed hardware and machine learning to offer cost-effective performance at any scale. Additionally, Redshift’s Spectrum feature allows querying data in S3 without performing ETL processes. Spectrum assesses data storage and query requirements, minimizing S3 data to reduce costs and query times.
Amazon Machine Learning
Amazon Machine Learning, also known as Amazon ML, is a service designed to support the development of machine learning models without requiring expertise in the field. The service includes features like wizards, visualization tools, and pre-built models to help users get started. It supports data evaluation, model training, and optimization to meet specific business needs. Once the model is complete, it can be accessed through batch exports or API.
The AWS Machine Learning team is dedicated to helping customers leverage state-of-the-art machine learning (ML) and artificial intelligence (AI) technologies in the cloud to enhance operations, manage risk, engage customers, and gain valuable insights from their data. AWS ML provides extensive services to enable organizations worldwide to efficiently solve real-world problems.
Amazon QuickSight is a valuable AWS service for business analytics that allows ad-hoc data analysis and visualization creation. The service allows natural language queries, exploration through interactive dashboards, and automatic pattern and outlier identification through machine learning. QuickSight supports various data sources, including on-premises databases, exported Excel or CSV files, and AWS services like S3, RDS, and Redshift.
QuickSight supports millions of weekly dashboard views, allowing end-users to make better data-driven decisions. The service uses a “super-fast, parallel, in-memory calculation engine” (SPICE), which relies on columnar storage and machine code generation to deliver interactive queries. The engine persists data until manually deleted to ensure speedy subsequent queries.
Amazon Kinesis is a real-time streaming data service that collects, processes, and analyzes data for timely insights and immediate action. It can handle various types of data, including video, audio, logs, clickstreams, and IoT telemetry data. The Kinesis Client Library (KCL) supports creating custom applications for streaming data, dynamic content, alert generation, and real-time dashboards. Kinesis has scalable and cost-effective capabilities that allow for streaming data at any scale and selecting ideal tools for your application.
Architecting big data workloads is a crucial process in the data management pipeline, and the above-mentioned AWS big data analytics options provide efficient and effective solutions for handling big data sets.
A Note on Architecting Big Data Workloads
Architecting Big Data is nothing less than a strenuous activity that requires appropriate tools and best practices offered by AWS to help you overcome the challenges involved in the process of architecting. AWS Big Data Architecture indeed poses some major data management challenges which you cannot resolve using traditional methods. The process of AWS Big Data Architecture not only involves challenges but also exhibits major issues such as scaling problems, bottlenecks, and spiraling costs. These challenges and issues can be answered with modern data architecture solutions which address all the approaches that should be applied while architecting Big Data Workloads.
The modern solutions involved in AWS Big Data Architecture do not only mean integrating a data lake with a data warehouse, but also about integrating a data lake, a data warehouse, and purpose-built stores paving the way for centralized governance and seamless data movement. By using a modern data architecture on AWS, customers can quickly and promptly establish scalable data lakes, use a broad and deep collection of purposefully built data services, provide compliance through unified data access, security, and governance, scale their systems at an affordable rate without affecting the quality of performance, and effortlessly share data across organizational boundaries. This will let the customers take decisions with speed and agility at scale.
With time, Data volumes have been expanding at an unprecedented rate, evolving from terabytes to petabytes and sometimes exabytes. Traditional on-premises data analytics approaches are not enough to tackle these data volumes as they can’t scale well enough and are extremely costly. This is why many organizations are using the modern methods of AWS Big Data Architecture to take all their data from various silos and aggregate those data in one place, also called a data lake, to do analytics and ML directly on top of that data. During other times, the same companies are storing other data in purpose-built data stores to analyze and gather faster insights from both structured and unstructured data. This data movement can happen “inside-out”, “outside-in”, “around the perimeter” or “sharing across” because data consists of gravity.
Some significant Architectural principals involved in the process of big data management can be listed as follows,
- Build decoupled systems
- Data – Store – Process – Store – Analyze – Answers
- Use Appropriate tools for the job
- Data Structure, Latency, Throughput, Access Patterns
- Leveraged managed and serverless services
- Scalable/elastic, available, reliable, secure, no/low admin
- Use event-journal design patterns
- Immutable datasets (data lake), materialized views
- Try to be careful with costs
- Big Data = Big Cost
AWS provides all the services and tools required to collect, store, process, analyze, and visualize big data on the cloud. Its well-structured data analytics and superior scalable storage as well as compliance regulations make it possible for you to efficiently manage the entire big data cycle without any hassle. There are a number of ways the AWS Big Data solutions work and the big data analytics options also help to transform the data technically as well as economically feasible for you and your organization. In a nutshell, there is no hardware to gather and no infrastructure to be maintained and scaled with AWS. This lets you concentrate carefully on your resources on exploring and revealing new insights. Moreover, AWS is constantly updated with new capabilities and features and enables you to leverage the most recent technologies without committing for long-term investments.
Are you ready explore more about the services of AWS? Do you want to leverage the potential of AWS cloud in your business? Then hurry up and get in touch with us at Webuters.
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