![]() ![]() Given all other conditions being equal, which applications and analytics should you start with for the first wave? The best practice is to start somewhere in the middle (such as Analytic 8 or 9 in the preceding table). The bottom row displays the number of subject areas that appear in an application (from the most dense application to the least dense, from left to right). The right-most column shows the total number of times a subject area appears in applications (from the most common subject area to the least common subject area, top to bottom). The sorting method in the preceding table is important. You can similarly derive another potential second step, which results in a more detailed mapping between data sources and subject areas (to the level of individual tables) and helps with detailed project planning. This mapping is the basis for the creation of each wave - a single-step migration of an application’s objects and associated subject areas. The basis for this mapping is the query execution metadata often stored in system tables of legacy data warehouses. The following diagram illustrates the workflow of data subject areas and information consumption.įigure 2: The affinity mapping between applications (analytics) and subject areas. Application – An analytic that consumes one or more subject areas to deliver value to customers.It is typically associated with a business function, such as sales or payment. Subject area – A data source and data domain combination.For more information, see How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime.Ī data warehouse has the following two logical components: You should run both the source MPP data warehouse and Amazon Redshift production environments in parallel for a certain amount of time before you can fully retire the source MPP data warehouse. Consumption-based migration methodologyĪn effective and efficient method to migrate an MPP data warehouse is the consumption-based migration model, which moves workloads from the source MPP data warehouse to Amazon Redshift in a series of waves. It breaks a complex data warehouse migration project into multiple logical and systematic waves based on multiple dimensions: business priority, data dependency, workload profiles and existing service level agreements (SLAs). This approach is derived from lessons learned from analyzing and dissecting your ETL and reporting workloads, which often have intricate dependencies. You can significantly reduce the complexity of migrating your legacy data warehouse and workloads with a subject- and object-level consumption-based data warehouse migration roadmap.ĪWS Professional Services has designed and developed this tool based on many large-scale MPP data warehouse migration projects we have performed in the last few years. Large-scale MPP data warehouse migration presents a challenge in terms of project complexity and poses a risk to execution in terms of resources, time, and cost. SCT can also help migrate data from a range of data warehouses to Amazon Redshift by using built-in data migration agents. The AWS Schema Conversion Tool (SCT) makes this type of MPP migration predictable by automatically converting the source database schema and a majority of the database code objects, including views, stored procedures, and functions, to equivalent features in Amazon Redshift. With tens of thousands of current global deployments (and rapid growth), Amazon Redshift has experienced tremendous demand from customers seeking to migrate away from their legacy MPP data warehouses. You can set up a cloud data warehouse in minutes, start small for just $0.25 per hour, and scale to over a petabyte of compressed data for under $1,000 per TB per year – less than one-tenth the cost of competing solutions. You can run queries across petabytes of data in your Amazon Redshift cluster, and exabytes of data in-place on your data lake. They cannot support modern use cases such as real-time or predictive analytics and applications that need advanced machine learning and personalized experiences.Īmazon Redshift is a fast, fully managed, cloud-native and cost-effective data warehouse that liberates your analytics pipeline from these limitations. ![]() These traditional data warehouses are expensive to set up and operate, and require large upfront investments in both software and hardware. Traditional on-premises MPP data warehouses such as Teradata, IBM Netezza, Greenplum, and Vertica have rigid architectures that do not scale for modern big data analytics use cases. However, only a fraction of this invaluable asset is available for analysis. Data in every organization is growing in volume and complexity faster than ever.
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