Table of Contents
Over a decade, the cloud has been a major driver of Big Data management services. Cloud computing power allows organizations to manage and analyze data that is growing in volume, velocity, and variety. Businesses used to struggle to scale their databases or applications in-house before the advent of cloud technology. Inadequate data management can lead to opportunities loss and cost increases for organizations. AWS is a top cloud service provider that has helped organizations achieve operational efficiency and cost optimization. It also empowered them to invest in new opportunities using its data analytics services. AWS Data Analytics services that are highly regarded include Amazon Athena and Amazon Redshift, Amazon EMR and AWS Glue. This article will discuss the AWS data analytics and Big Data services. AWS certification and training courses are also available that can help your team achieve new goals in data analytics and management.
AWS Big Data Analytics Solutions – ProcessModern companies look for solutions to help them manage and analyze their data to gain valuable insights. Analytics solutions can help organizations deliver better products or services to customers. Big Data is often described in terms of the “three Vs” Volume, Variety, And Velocity. The volume of data produced or gathered by institutions and organizations can be from terabytes up to petabytes. Variety is an important factor in data management. The third factor is Velocity. With the help of thousands upon thousands of servers and software like MapReduce and Hadoop, organizations can store, process, and analyze Big Data in real-time through cloud computing platforms. These are the top Big Data and data analytics services of AWS.
Data collection: Users must be able to access data from multiple sources in real time using the AWS Big Data analytics solution. Data can be both structured and unstructured in any format. Data collection sources include operational processes that generate transactional data, IoT device, third-party sources and social media.
Data Storage: AWS provides data storage that is both durable and reliable for data at rest and in-transit.
Data Processing and Analytics: This phase is where the cloud team uses a variety of tools and techniques in order to access large-scale data and to process it to prepare it to be used in the next stage or extract valuable insights.
Data Consumption: The data is visualised to gain intelligent insights during the consumption phase. This phase uses data visualization and BI tools to explore data sets and gain insights.
Learn how AWS services can be used to modernize your data strategy, and to innovate through AI/Machine Learning. Participate in the AWS Discovery Day.
AWS Big Data and Data Analytics Tools and ServicesAmazon Amazon AthenaAmazon athena allows customers to query and analyse data. Customers can use standard SQL to create queries. It takes just a few seconds for large datasets to be returned results.
Amazon EMRAmazon EMR can run workloads from frameworks like Apache Spark, Apache Hive and Presto. Amazon EMR supports clusters – a group of EC2 instances that can process and analyze large amounts of cloud data. Amazon EMR offers greater flexibility, scalability, and cost savings. Customers can also enjoy the benefits of other AWS capabilities and services.
Amazon RedshiftAmazon Redshiftis a data warehouse facility. It uses SQL to analyze both structured and unstructured data. Amazon Redshift allows customers the ability to analyze data from operational databases as well as data lakes, data warehouses, data warehouses, or third-party data sets. It is also one of the most popular cloud data warehouses in the world.
Amazon KinesisIf your goal is to collect and analyze data in real time, then Amazon Kinesis may be the right service for you. Amazon Kinesis is a solution that allows customers to simultaneously collect, analyze, and react to data in real-time. It can also be used for machine learning.
AWS Lake FormationAccording Aberdeen research, organizations who implement Data Lake solutions can outperform competitors by a 9% growth in organic revenue. Data lakes are
Table of Contents