Customers want to leverage more modern data architectures to deliver analytics on a broader range of data and toolsets. Customers also want to reduce the cost and complexity of operating a traditional data warehouse to provide all the facilities. Amazon brings Redshift for the enterprise data warehouse.
Amazon Redshift enterprise solution is a fully managed data warehouse service from AWS that is easy to use and very cost-effective; it allows to run complex queries against petabytes of data, and most results come back in seconds.
Amazon Redshift Enterprise – 10x Faster at 1/10th the cost
Amazon Redshift enterprise solution is fast as it delivers ten times better performance than on-premises data warehouses. It is also fast for all types of workloads, from short-running queries to complex, long-running queries on a trillion data rows. Amazon Redshift enterprise solution leverages a massively parallel processing architecture to deliver high throughput. Redshift enterprise solution allows the creation and start of a data warehouse quickly.
Amazon Redshift enterprise also automates most common administrative tasks to manage, monitor, and scale a data warehouse, including backups, updates, and more.
It also allows building quick integrated data lake and analytics with Amazon Redshift enterprise. Many enterprises in the financial services, healthcare, and retail government trust amazon Redshift enterprise solution to run mission-critical workloads and keep their data secure.
AWS Database Migration Service (DMS)
AWS database migration service providing the following facilities:
- Simple to Use
- Minimal Downtime
- Support Most Widely Used Database
- Low Cost
- Fast and Easy to Set Up
When to use AWS DMS
- Migrate databases to AWS with minimum downtime
- Large-scale data migrations (TB-PB)
- Database consolidation on AWS
- Continuous data replication with high available
- Offload data warehouse data to run analytics in the cloud using Amazon Redshift
- Migrate data warehouse to Amazon Redshift
- Good network connectivity between the user data center and AWS
- Source database engine or data warehouse appliance is supported
- When clients need the option of using their network to transfer the data or using the AWS snowball service
AWS SCT Data Extraction Agents
- Extracts through local migration agents
- Data is optimized for amazon Redshift enterprise and saved in local files
- Files are loaded to an Amazon S3 bucket (through network or AWS snowball) and then to Amazon Redshift
Extraction agents can be installed on:
- Red Hat Enterprise Linux (RHEL) 6.0
- Ubuntu Linux (Version 14.04 and later)
Agents support the following source data warehouses:
- Greenplum Database (version 4.3 and later)
- Netezza (version 7.0.3)
- Teradata (version 13 and later)
- Vertica (version 7.2.2 and later)
- Oracle (version 10 and later)
- Microsoft SQL Server (version 2008 and later)
Data Warehouse Offload Tasks
AWS Snowball is a petabyte-scale data transport solution that uses secure appliances to transfer a large amount of data into and out of the AWS cloud. It’s device can hold up to 80 terabytes of data, and an AWS snowball edge device can hold up to 100 terabytes of data. It also provides data encryption. Schema conversion tool works both with snowball and snowball edge devices.
- Simple, fast, and secure data transfer
- 1/5 the cost of high-speed internet
- Can transfer up to 90 PB of data
Moreover, with AWS SCT and a snowball device, user can migrate their data in two stages. In the first stage, the user will use the AWS SCT tool to process the data locally and then move it to the snowball device. The user can send that device using the AWS snowball process. AWS automatically loads the data into an Amazon S3 bucket. When data is available on S3, users can migrate their data to Redshift using the schema conversion tool.
Amazon Redshift Enterprise Data Warehouse Migration Tasks
These steps enable the deployment of a pattern for a full data warehouse migration. Migration data warehouse to Amazon Redshift will help leverage more modern data architecture to deliver analytics on a broader range of data and toolsets. As reducing the cost and complexity associated with operating a traditional warehouse.
Author: SVCIT Editorial
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