No high-level AWS overview would be complete without a focus on analytics. Alongside the rise of big data, there’s been a substantial increase in cloud computing usage. As a direct result of this growth, it’s become increasingly possible, and even commonplace, to analyze mountains of data. While this efficiency was not always possible, due to the limitations of traditional systems, recent years have seen significant forward movement in the way Amazon and other service providers approach reliable, insightful analytics
Today’s big data analysis allows organizations and businesses to gain better insight regarding the data they obtain to build a competitive advantage. A focus on big data analysis offers a number of advantages, including:
Real-time insights. When organizations use big data analysis, they’re capable of assessing a diverse selection of data from hundreds of unique sources, done in real time. This allows organizations to engage in more productive interactions with stakeholders, thus increasing opportunities for commitment.
Elimination of data niches. Organizations can gain a fuller understanding of clients and other stakeholders including a variety of calculated, descriptive metrics; this allows analysts to build detailed records of client behavior.
Opportunities for risk management. Big data analysis provides a quicker, more comprehensive means to evaluate risk portfolios.
When organizations utilize AWS tools, in particular, they’ll find that the process of storing, processing, visualizing, and analyzing data is easier than ever before. AWS tools include technologies such as scalable data lakes, purpose-built analytics services, seamless data movement, and unified governance. Together, these tools are capable of making big data analysis much more efficient and accessible than it was in the not-so-distant past.
Within the available AWS components, organizations often choose to take advantage of smart applications or data warehousing. Smart applications make use of actionable, data-driven insights into the experience of users. Insights can come in the form of employee-facing or customer-facing insights, depending on the needs of the organizations. With smart applications, the end user may receive recommendations, estimates, or suggestions for how to proceed. For example, a retail application may generate and display product recommendations based on previous buying habits.
Many modern organizations are also making use of data warehousing tools. Data warehousing allows organizations to electronically store vast quantities of information. From this compilation, organizations can consolidate data from various heterogeneous sources, begin analysis, and build improved insights regarding performance.
Any organization considering investing in Amazon AWS for data analysis should consider the following services:
- Amazon Athena
- Amazon EMR
- Amazon CloudSearch
- Amazon Elasticsearch Service
- Amazon Kinesis
- Amazon Kinesis Data Firehose
- Amazon Kinesis Data Analytics
- Amazon Kinesis Data Streams
- Amazon Kinesis Video Streams
- Amazon Redshift
- Amazon QuickSight
- AWS Data Pipeline
- AWS Glue
- AWS Lake Formation
- Amazon Managed Streaming for Apache Kafka (Amazon MSK)
Amazon Athena provides organizations with an interactive query service. Built to simplify analysis of the collected data in Amazon S3, Athena utilizes a standard SQL. Amazon Athena is a serverless service, negating the need for infrastructure management.
With Amazon EMR, organizations gain access to a managed Hadoop framework. This makes big data analysis across scalable Amazon EC2 instances easier, faster, and more cost-effective than ever before. EMR can handle financial analysis, web indexing, log analysis, and more.
Amazon CloudSearch is a simple, cost-effective way to set up and manage a search solution. It supports search features such as autocomplete, highlighting, and geospatial search, as well as over 34 global languages.
Amazon Elasticsearch Service
Use Amazon Elasticsearch Service to deploy and operate Elasticsearch with simple APIs and boundless integration with other AWS components like AWS Key Management Service Amazon CloudWatch, AWS Lambda, and many more. This allows organizations to search, analyze, and visualize large quantities of data, all in real-time.
Amazon Kinesis is a simple way to collect and analyze streaming data in real time, enabling timely insights and generating new information. Organizations can react quickly and efficiently as data comes in, instead of enduring lengthy processing times.
Amazon Kinesis Data Firehose
A simple means of loading streaming data into analytics tools and data stores, Amazon Kinesis Data Firehose can load the resulting data into Amazon Redshift, Amazon S3, Splunk, and Amazon Elasticsearch Service. This allows for analytics that are near real-time, configurable with a few clicks, and ready for storage.
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics is a simple way to analyze streaming data, allowing an organization to gain insights and respond to business and client needs, all in real-time. This AWS service also helps simplify the process of building, managing, and integrating streaming apps.
Amazon Kinesis Data Streams
Amazon Kinesis Data Streams provides real-time data streaming in a way that is highly scalable and durable. This service is able to capture gigabytes of data per second from an enormous collection of sources, allowing real-time analysis on the spot.
Amazon Kinesis Video Streams
With Amazon Kinesis Video Streams, companies can securely stream video from a connected device to aid in machine learning, analytics, playback, and more. Organizations rely on Kinesis Video Streams to store, encrypt, and index streaming video data and provision and scale the necessary infrastructure to maintain live video.
Amazon Redshift is a simple, cost-effective data warehouse. This AWS service allows organizations to analyze data across data warehouses and data lakes, all up to ten times faster than other services.
Powered by cloud computing, Amazon QuickSight is a fast business intelligence service, helping companies deliver insights throughout their organization. The service also allows users to create interactive dashboards, which are accessible through both browsers and mobile devices.
AWS Data Pipeline
Using AWS Data Pipeline, organizations are able to process and move data among various AWS compute and storage services. Data can also be reliably processed and moved to on-premises data sources, at any intervals specified.
With AWS Glue, organizations will find it easier to prepare and load data, prior to performing analytics. Glue is a fully-managed extract, transform, and load service, also known as an ETL.
AWS Lake Formation
Using AWS Lake Formation, it’s simple to quickly set up a secure data lake. With a data lake, organizations can both centralize and secure mountains of data, whether in its original form or after preparation for analysis. The resulting aggregates allow improved analytics and deeper insights.
Amazon Managed Streaming for Apache Kafka (Amazon MSK)
With Amazon MSK, organizations can construct and run applications that process streaming data using Apache Kafka. Apache Kafka is a platform that facilitates real-time streaming data applications and pipelines. Together, Kafka and Amazon MSK can enable data lake population, facilitate database changes, and drive analytics or machine learning.