Amazon S3 is secure, highly scalable, durable object storage with millisecond latency for data access. S3 is built to store any type of data from anywhere – web sites and mobile apps, corporate applications, and data from IoT sensors or devices. It is built to store and retrieve any amount of data, with unmatched availability, and built from the ground up to deliver 99.999999999% (11 nines) of durability. S3 provides comprehensive security and compliance capabilities that meet even the most stringent regulatory requirements.  

Amazon Glacier is secure, durable, and extremely low cost storage for long-term backup and archive that can access data in minutes.  It is designed to deliver 99.999999999% durability (11 nines), and provides comprehensive security and compliance capabilities that can help meet even the most stringent regulatory requirements. Customers can store data for as little as $0.004 per gigabyte per month, a significant savings compared to on-premises solutions.

AWS Glue is a fully managed service that provides a data catalog to make data in the data lake discoverable, and has the ability to do extract, transform, and load (ETL) to prepare data for analysis. The data catalog is automatically created as a persistent metadata store for all data assets, making all of the data searchable, and queryable in a single view.

For expert machine learning practitioners and data scientists, AWS provides AWS Deep Learning AMIs  that make it easy to build deep learning models, and build clusters with ML and DL optimized GPU instances. AWS supports all the major machine learning frameworks, including TensorFlow, Caffe2, and Apache MXNet, so that you can bring or develop any model you choose. These capabilities provide unmatched power, speed, and efficiency that deep learning and machine learning workloads require.

For developers who want to get deep with ML, Amazon SageMaker is a platform service that makes the entire process of building, training, and deploying ML models easy by providing everything you need to connect to your training data, select, and optimize the best algorithm and framework, and deploy your model on auto-scaling clusters of Amazon EC2. SageMaker also includes hosted Jupyter notebooks that make it is easy to explore, and visualize your training data stored in Amazon S3.

AWS offers the broadest set of analytic tools and engines that analyzes data using open formats and open standards. You get to store your data in the standards-based data format of your choice such as CSV, ORC, Grok, Avro, and Parquet, and the flexibility to analyze the day in a variety of ways such as data warehousing, interactive SQL queries, real-time analytics, and big data processing. The breadth of analytics services that you can use with your data in AWS, ensures that your needs will be met for your existing and future analytics use cases.

Amazon S3 is built to store and retrieve any amount of data, with unmatched availability, and built from the ground up to deliver 99.999999999% (11 nines) of durability. It is the only storage offering that can store your data in multiple data centers across three availability zones within a single AWS Region for unmatched resilience to single data center issues, and the only storage offering that seamlessly replicates data between any regions.

S3 is the only cloud storage platform that allows you to apply access, log, and audit policies at the account and object level. S3 provides automatic server-side encryption, encryption with keys managed by the AWS Key Management Service (KMS), and encryption with keys that you manage. S3 encrypts data in transit when replicating across regions, and lets you use separate accounts for source and destination regions to protect against malicious insider deletions. To proactively detect early stages of an attack, Amazon Macie, an ML powered security service monitors data access activity for anomalies, and generates detailed alerts when it detects risk of unauthorized access or inadvertent data leaks.

Data lakes built on AWS are the most cost-effective. Data that is infrequently used can be moved to Amazon Glacier which provides long-term backup and archive at very low costs. Amazon S3 management capabilities can analyze object access patterns to move infrequently used data to Glacier on-demand or automatically with lifecycle policies. You can begin querying the data with Amazon Athena for as little as $0.005/GB queried. Other analytics and machine learning services are priced with a pay-as-you-go approach for the resources you consume.

AWS analytic services like Amazon Redshift and Amazon Athena were built for fast interactive query performance to support large numbers of concurrent interactive queries. When running AWS' broad portfolio of analytics and machine learning services using Amazon S3 Select, only the subsets of data that are needed within objects are returned, leading to much faster queries up to 400% faster, and at a dramatically lower cost. Glacier Select provides a similar capability allowing you to retrieve archived data faster, and allowing you to extend your analytical capability over your data lake to include archival storage.  

The AWS Partner Network (APN) has twice as many partner integrations than anyone else, with tens of thousands of partners, including consulting and independent software vendors, from all across the globe. This makes it easy to work and integrate with many of the same tools you use and love today. Data Lake Quick Starts, developed by AWS solution architects and partners, help you build, test, and deploy data lake solutions based on AWS best practices for security and high availability, in a few simple steps.