Migration Of Data Warehouse To AWS Cloud

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Migration of data warehouse to AWS Cloud
Time to move your data warehouse to the cloud

Organizations face multiple challenges in data migration and access when embarking on a cloud journey. Moving data warehouses (DW) to the cloud without interrupting business operations, ensuring timely and quality data flow for business users, and integrating multiple applications are some of the critical issues that need to be addressed during the journey. Migration of Data Warehouse to Cloud with Dynamic Scaling to Achieve Better Availability and Cost Management. Companies also looking to dynamically enhance the ability of adding new data sources to the DW and optimize maintenance of ETL jobs, while maintaining strict adherence to SLAs.

Why it’s time to move your data warehouse to the cloud?

For many large organizations, Enterprise Data Warehouses (EDWs) are their lifeblood. EDWs can support a variety of workloads, including financial reporting, customer satisfaction analysis, manufacturing quality, shipping & logistics, as well as ad hoc workloads from individual business units. This ability to support so many departments means EDWs are the go-to tool for any organization looking to utilize their data effectively. Although many organization still have their EDWs based on site, there is a growing trend for moving this data to the cloud, and with good reason. As operational data volumes continue to grow at exponential rates, service-level expectations are raised, and the need to integrate structured warehouse data with unstructured data in a data lake becomes greater, it’s not a matter of if you go to the cloud to manage your enterprise data.


The benefits of a cloud-based data warehouse:

Scalability
Start-up costs are a fraction of on-premises solutions
Reduce ongoing costs
Easily change user numbers
Allows for new capabilities
No disruption to internal users
Access to a virtual team of experts
Increased security
One of the biggest barriers to effective digital transformation faced by today's organizations is connecting, synchronizing, and relating structured and unstructured data from cloud and on-premises applications and processes across multiple internal and external sources including public and private clouds. It is difficult to balance the complexity of data distributed so broadly with the necessity to access it when and where it's needed.
Before the debut of Amazon Redshift, data warehousing was essentially an on-premises initiative, with data migration and security issues playing big roles in keeping warehoused stores of corporate information inside the walls of organizations. Redshift made the idea of deploying a data warehouse in the cloud viable, with at least the promise of substantial cost savings compared with installing and running traditional data warehouse systems. Cloud services can also be easily scaled up or down as data and business needs change. But fundamental data management processes -data integration, data quality, data governance, master data management -- still need to be applied to information that's warehoused in the cloud.

Getting your cloud migration started with Amazon Redshift

Like any project, migrating your EDW will need to spend time planning and researching. W Moving to the cloud offers cost savings, efficiency, scalability and security; but, you’ll need to decide which solutions are best suited for your business.


Migrating from Oracle to AWS cloud

After careful evaluation of requirements and various options, we selected Amazon Web Services (AWS) as the cloud platform. Amazon Redshift, a fast and fully managed petabyte-scale data warehouse with its massively parallel processing (MPP) architecture, was deployed to analyze data using existing business intelligence tools.


The solution implementation involved the following steps:

Data extraction from multiple sources to AWS S3 buckets - using an Open Source ETL Tool - Pentaho.
Data cleansing, ETL steps and application of business logic – using AWS Data Pipeline Service to trigger AWS EMR jobs. For sources that do not support change data capture, a full extract with de-duplication logic was applied in EMR jobs.
Data loading to Amazon Redshift – after transforming data from all source systems into an intermediary generic data structure.
Migrating Data Warehouse on AWS Cloud training explains....

The challenges with traditional data warehouse deployments and demonstrates how we can create and migrate Data Warehouse on Cloud and how overcomes these obstacles. Learn how to create, connect to, and load data into an Autonomous Data Warehouse Cloud instance. In our Migration and creation of data warehouse Essential training and expert guidance of our consultant, you'll find more information about Amazon Redshift and Migration and creation of data warehouse service platform for same, plus expert insight and real-world advice on managing cloud data warehouses.
Want to find out more about using hands-on and live experience, Endeavor to Remember or on the off chance that you found this post important, Make without question, you can take a gander at our Web based Learning AWS Cloud courses for more tips, traps and strategies for effectively to uncover stand-out bits of information from your data.

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