We will help you to design and build AWS-powered data lakes that can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot.
The first step to building data lakes on AWS is to move data to the cloud. The physical limitations of bandwidth and transfer speeds restrict the ability to move data without major disruption, high costs, and time. To make data transfer easy and flexible, AWS provides the widest range of options to transfer data to the cloud.
On-premises data movement
AWS provides multiple ways to move data from your datacenter to AWS. To establish a dedicated network connection between your network and AWS, you can use AWS Direct Connect. To move petabytes to exabytes of data to AWS using physical appliances, you can use AWS Snowball and AWS Snowmobile. To have your on-premises applications store data directly into AWS, you can use AWS Storage Gateway.
Real-time data movement
AWS provides multiple ways to ingest real-time data generated from new sources such as websites, mobile apps, and internet-connected devices. To make it simple to capture and load streaming data or IoT device data, you can use Amazon Kinesis Data Firehose, Amazon Kinesis Video Streams, and AWS IoT Core.
Once data is ready for the cloud, AWS makes it easy to store data in any format, securely, and at massive scale with Amazon S3 and Amazon Glacier. To make it easy for end users to discover the relevant data to use in their analysis, AWS Glue automatically creates a single catalog that is searchable, and queryable by users.
Fully functioning data lake on AWS with the following properties: