8/1/2023 0 Comments Redshift ntile![]() Previously, Amazon Redshift supported ASCII as a leader-node only function, which prevented its use with user-defined tables. The ASCII function takes as input a string and returns the ASCII code, or more precisely, the UNICODE code point, of the first character in the string. Download the latest version of AWS SCT and try it out. This example showed how to use merge automation for macros, but you can convert merge statements in any application context: stored procedures, BTEQ scripts, Java code, and more. SET name = "delta".name, manager = "delta".managerįROM testschema.employee_delta AS delta JOIN testschema.employee AS tgt We create two tables in Teradata: a target table, employee, and a delta table, employee_delta, where we stage the input rows:ĬREATE OR REPLACE PROCEDURE rge_employees() ![]() AWS SCT decomposes a merge statement into an update on existing records followed by an insert for new records. Now, we’re happy to share that AWS SCT automates this conversion for you. Until now, if you used merge statements in your workload, you were forced to manually rewrite the merge statement to run on Amazon Redshift. If there is no matching target row, the input row is inserted into the table. If an input row already exists in the target table (a row in the target table has the same primary key value), then the target row is updated. Like its name implies, the merge statement takes an input set and merges it into a target table. In this post, we introduce new automation for merge statements, a native function to support ASCII character conversion, enhanced error checking for string to date conversion, enhanced support for Teradata cursors and identity columns, automation for ANY and SOME predicates, automation for RESET WHEN clauses, automation for two proprietary Teradata functions (TD_NORMALIZE_OVERLAP and TD_UNPIVOT), and automation to support analytic functions (QUANTILE and QUALIFY). Today, we’re happy to share recent enhancements to Amazon Redshift and the AWS Schema Conversion Tool (AWS SCT) that make it easier to automate your Teradata to Amazon Redshift migrations. Until now, migrating a Teradata data warehouse to AWS was complex and involved a significant amount of manual effort. In these cases, you may have terabytes (or petabytes) of historical data, a heavy reliance on proprietary features, and thousands of extract, transform, and load (ETL) processes and reports built over years (or decades) of use. Many customers have asked for help migrating from self-managed data warehouse engines, like Teradata, to Amazon Redshift. Multi-tenancy Apache Kafka clusters in Amazon MSK with IAM access control and Kafka Quotas – Part 1 You can also integrate other services such as Amazon EMR, Amazon Athena, and Amazon SageMaker to use all the analytic capabilities in the AWS Cloud. With Amazon Redshift, you can query exabytes of data across your data warehouse, operational data stores, and data lake using standard SQL. No other data warehouse makes it as easy to gain new insights from your data. Accelerate your data warehouse migration to Amazon Redshift – Part 2 to learn about automatic conversion for proprietary data types.Īmazon Redshift is the leading cloud data warehouse.Accelerate your data warehouse migration to Amazon Redshift – Part 1 to learn more about automated conversion of database macros, case-insensitive string comparison, and case-sensitive identifiers,.Check out the previous posts in the series: ![]() ![]() We’re excited to share dozens of new features to automate your schema conversion preserve your investment in existing scripts, reports, and applications accelerate query performance and reduce your overall cost to migrate to Amazon Redshift. This is the third post in a multi-part series. Post Syndicated from Michael Soo original
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