It is important to consider many different aspects of AWS Data Analytics Services such as analytics, data movement, data lake, and predictive analysis.
Here are the most significant benefits offered to AWS customers:
Amazon Redshift replaces many other tools providing the capability to query all types of data. In addition, tools like AWS SageMaker allow customers to set up predictive analysis platforms with ease.
AWS provides the most comprehensive portfolio of products for all aspects of Data Science like analytics, data movement, data lakes, and predictive analysis. Many options exist for each process, enabling customers to choose the one that is most suitable for their needs.
Open file formats like Apache Parquet allow data to be stored centrally but be consumed by all AWS services.
AWS provides virtually unlimited compute and storage resources, allowing for convenient scaling of your Data Warehouse. Once a Data Analytics platform has been set up with vast amounts of data, it is not possible to migrate it without disrupting the business and incurring considerable costs. As such, it is vital to select a service provider like AWS.
With businesses seeing exponential growth in the data loads they accumulate, it is essential to acquire services with cost-effective pricing. AWS offers variable pricing and pay-as-you-go options that ensure that you only pay for the amount of resources that are being consumed.
AWS is one of the safest cloud providers, and it offers many security benefits and compliance certifications to customers. Data is stored and transmitted in an encrypted form to ensure data security.
In addition, AWS offers many other tools like Amazon Macie that identifies any sensitive data which is stored carelessly and Amazon Inspector that identifies configuration errors that may lead to data breaches.
AWS consists of many services that are important for different aspects of data analytics such as Processing big data, Data warehousing, real-time analytics, interactive analysis, operational analytics, and Dashboards and visualizations.
Amazon Redshift is one of the most highly recommended tools for querying a Data warehouse, which is a centralized collection of data from many areas of a business. Complex SQL queries are used to fetch structured and semi-structured data from petabytes of data across your Data warehouse to facilitate decision making. You can then save these results back to your AWS Data Lake through Amazon Redshift in order to use them across other big data analytics tools like Amazon EMR and Amazon Athena.
AWS Redshift powers the data analytics efforts of organizations ranging from startups to Fortune 500 companies. Yelp, McDonald’s, Fox Networks, and Lyft are just a few of its biggest customers.
Amazon EMR and Kinesis are some of the other tools offered in this category for data processing and analytics respectively.
Importing data in large capacities is one of the most challenging aspects of a data lake. AWS offers many services to facilitate capturing, transforming, and load streaming data into products such as Amazon S3 and Amazon Redshift.
Amazon Kinesis Data Firehose, Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics are some of the main options for streaming data from various external sources. Amazon Kinesis Video Streams is a specialized tool for importing video content, which becomes useful for many industries like healthcare, recruitment, and retail.
AWS offers three main categories of tools for data lakes. The first of these is Data cataloging, in which AWS Lake Formation plays a significant role in automating the process of building a data lake.
Object Storage is another vital category given the unimaginably large datasets that are associated with big data analytics. Amazon S3 is the leading storage option for AWS Data Lakes.
Last but not least, data backup and archive tools like Amazon S3 Glacier and AWS Backup take care of securing your data in case of emergencies and other disasters.
Amazon SageMaker Autopilot is another useful part of the platform, and it aids customers in automating most of the routine tasks entailed in the process of training Machine Learning algorithms. SageMaker Studio is the official Integrated Development Environment (IDE) for the AWS Deep Learning platform.
In addition, other tools like SageMaker Notebooks to manage elastic computer instances, Marketplace, Augmented AI, and Debugger provide further capabilities to smoothen the process of setting up an AWS Data Lake.
Contact Cloud Computing Technologies today for a no-obligation proof-of-concept and proposal! We specialize in building and optimizing AWS Data Lakes and many other big data analytics platforms.
Data analytics refers to a comprehensive set of techniques and methods that examine datasets to draw conclusions from massive amounts of datasets. In this way, you take raw data and display patterns to show valuable business insights.
Data was limited before the 21st century. Back then, manual activities were used to extract insights from a customer’s information. However, as the world moved to the Internet, global data storage underwent an explosive growth. This led companies to rely on data analytics services. They combined artificial intelligence (AI), machine learning, statistics, algorithms, and automation to build specialized systems for displaying key business metrics on user-friendly dashboards.
For instance, manufacturers record runtime, downtime, and work queue for multiple machines and perform analysis to adjust the workloads. This ensures that the machines are optimized and operate closely to peak capacity.
Data-driven companies are 23 times more likely to get successful at customer acquisition.
Modern customers expect businesses to understand them. Data analytics help you to understand what your customers need. They use the newly generated insights to optimize the user experience and foster long-lasting relationships.
Effective data analytics ensures that the organization can stay competitive as new technologies emerge and industry demand fluctuates. It is an excellent opportunity to create tailored products and services. Analytics reviews factors, such as real-time customer location, age, income, to deliver high-quality personalization. Your marketing department can rely on these insights and display customized ads.
Security and fraud analytics can protect your financial, physical, and intellectual assets from misuse by external and internal threats. Powerful data analytics features boost organizational security and increase fraud prevention. Transparent reporting and data management produces improved fraud risk management processes. For instance, financial institutions can look for unusual patterns to save their customers from identity theft.
This is the biggest advantage of data analytics. It empowers the c-suite to make quick and timely decisions. As a result, the entire organization gets to benefit from data-driven insights.
90% of business professionals believe that data analytics play a pivotal role in their company’s digital transformation.