![]() ![]() ![]() The following diagram illustrates this architecture. The migration strategy uses the AWS SCT to accelerate schema object conversion and migrate the data from the Netezza database to the Amazon Redshift cluster. Make sure it’s clear which data warehouses are in scope for the migration. Some Netezza source systems contain two Netezza data warehouses, for example one for ETL loading throughout the day and one for end-user reporting users. For each table identified, record the number of rows and size in GB. This information forms a migration runbook that is updated during the migration to document the progress of data migration from Netezza to Amazon Redshift. To plan and keep track of the migration tasks, you should produce a tracker of all the Netezza databases, tables, and views in scope. Business validation (including optional dual-running).Migrate to other pre-production environments.Configure AWS SCT for Netezza source environments.Record objects to be migrated into a migration runbook.It details the different environments migrated to and the tasks, tools, and scripts used to complete the work: ![]() The following plan is a real-world use case from a large European Enterprise customer. It’s important to build a migration plan unique to your organization’s processes and non-functional requirements. We also walk you through validating that the schema and data content were migrated as expected and followed Amazon Redshift best practices. In this post, we explain how a large European Enterprise customer implemented a Netezza migration strategy spanning multiple environments, using the AWS Schema Conversion Tool (AWS SCT) to accelerate schema and data migration. Common columnsĭistribute the fact table and its largest dimension table on their common columns.The post How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime described a high-level strategy to move from an on-premises Netezza data warehouse to Amazon Redshift. If a table is largely denormalized and does not participate in joins as a rule of thumb always use the Even distribution. All distribution - A copy of the entire table is distributed to every node.If two tables distributed on the joining key, data is co-located on the slices according to the values in the joining columns. Key distribution - data is distributed according to the values in one column.This is ideally used when a table does not participate in the join. Even distribution - data is distributed across the slices in a round-robin fashion.If you are using a star schema, a variant of star schema or a totally denormalised schema - you have to factor these in your table distribution style decision. Please note these distribution styles are applied at table level but the choice of distribution style often depends on the type of schema used in your database design. Even distribution is the default distribution style for Redshift. In particular, moving data from one node will have a major impact on network traffic.Īmazon Redshift supports three different types of table distribution styles: Even, Key and All. The cost of data redistribution can be substantial, and often it will slow down query performance. This can happen for two reasons - first when performing joins or aggregates and second when trying to distribute the workload uniformly among the nodes in the cluster. This means Redshift query execution engine may need to move or redistribute data from one node or slice to another physically during the runtime. Redshift's query optimizer determines where the block of data need to reside to execute the most optimized query. Cost of data redistributionĪmazon Redshift query execution engine ships with an MPP-aware query optimizer. For instance, if a query is performing join over two tables, to avoid the redistribution of data, data from two tables can be co-located by planning an appropriate distribution style. This is accomplished by locating or co-locating the data where it needs to be before the query is executed. A key objective is to avoid the data redistribution during query execution or runtime. In a nutshell, table's distribution style dictates how the data is distributed across Redshift node and slices. When using Amazon Redshift, distribution style plays an important role in optimising the table design for best performance. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |