Businesses understand that their data contains valuable insights that provide a competitive edge. However, not all have been equally successful in unlocking this value. The key to gaining an upper hand lies in the rapid and cost-effective conversion of data into actionable knowledge. Traditional on-premise data architectures, while involving substantial investment, often fall short in delivering consistent reliability and comprehensive capabilities. Colossal troves of data also increase risk and become a liability without effective governance tools. Migrating your data warehouses and applications to AWS can alleviate these issues, offering cost reductions, heightened agility, and comprehensive security.
Data lakes built on Amazon S3 enjoy unparalleled scale and durability. AWS Lake Formation combines S3 with AWS Glue and other AWS services into a single user-friendly platform, enabling the construction of a complete data lake within days, inclusive of all necessary components ranging from data ingestion to intelligent cleaning and cataloging. Comprehensive governance policies ensure data is preserved, access controlled, and auditable.
With your data lake established on AWS, analysis is straightforward with tools and infrastructure purpose-built for any job. Amazon Redshift enables SQL querying on exabytes of data, boasting up to five times better price performance than any other cloud data warehouse. Amazon Athena offers easy and versatile SQL or Spark analytics on petabytes of data stored in your S3 data lake and 30 other data sources in or out of the AWS cloud. Amazon EMR runs big data applications with frameworks like Spark or Hive nearly twice as fast and at half the cost of traditional on-prem solutions. Amazon QuickSight makes it easy to present insights in fast, interactive reports and dashboards that can be used directly in AWS or even embedded directly in your applications.
AWS makes it easy to apply machine learning models to extract maximum value from your data. Amazon SageMaker provides a powerful suite of machine learning tools for Data Scientists, Machine Learning Engineers, and Business Analysts. With no prior data science experience, anyone can use SageMaker Canvas's simple point-and-click interface to train ML models on your data. Analysts can then invoke those models as if they were simple SQL functions in queries to analytics services like Redshift, Athena, and QuickSight. With end-to-end integrated solutions for data management, analytics, and machine learning, AWS empowers businesses to transform their data into valuable insights rapidly and cost-effectively, driving innovation and bolstering their competitive advantage in today's data-driven marketplace.