databases in the AWS Glue Data Catalog. must be registered in Athena. When AWS Glue creates a table, it registers enabled. Let us now move data between 2 tables with different columns in different databases. Step-by-Step. Specify the migration type as Migrate existing data and replicate ongoing changes, which is essentially a change data capture mode. Started. After the database is created, you can create a table based on SQL Server replicated data. discover data schema and extract, transform, and load (ETL) data. The following managed policy provides access to Athena and the appropriate Amazon S3 bucket permissions: The resource aws-athena-query-results in the preceding example is the bucket for Athena to store query results. so we can do more of it. we may want to rebuild the table for various reason like fragmentation removal ,moving to different tablespace etc. After the database is created, you can create a table … Now that we are ready with source data/table and destination table, let's create Azure Data Factory to copy the data. To only move a table: proutil dbname -C tablemove [owner-name] table table-area proutil sports2000 -C tablemove benefits misc To move indexes at the same time to the same area as the table or to a different area from the current one, add the name of the area where to store the indexes. For a step-by-step tutorial on creating a table and writing queries in the Not only can you easily replicate databases to a common data store such as Amazon S3, but you can also query data interactively and run ad hoc queries quickly using ANSI SQL—without needing to set up a target database, aggregate data, or load data into Athena. They are essentially metadata that describes your data in a way similar to a relation, and they don’t represent a true relational database. You can copy the data to a temporary table in a new database in server-A, then backup this database and restore it in the destination server, and finally move the data from the restored database into the real destination table. You can simply create an IAM user policy or bucket policy to provide access to the Amazon S3 bucket so that users can create tables and work with underlying data. Creating a replication instance To replicate databases using AWS DMS, you need to provision a replication instance to perform the tasks and replicate data from source to target. Right-click on the database name and choose Tasks -> Generate Scripts. Assign the following permission to the role that is used to create the migration task: The role should also have a trust relationship defined with AWS DMS as the principal entity, as in the following: Because the AWS Management Console does not have an explicit service role for AWS DMS yet, you create an Amazon EC2 service role and rename ec2.amazonaws.com to dms.amazonaws.com, as shown in the previous example. However, you can also set up a private VPC endpoint for Amazon S3 that lets you connect the replication instance to Amazon S3 without making it publicly accessible. In this post, I am sharing a T-SQL script for changing or moving tables between the schema of SQL Server. Delete Data from Table A . automatically or manually. Thanks for letting us know we're doing a good It’s important to understand databases and tables when it comes to Athena. * , which matches any character zero to … Prahlad Rao is a solutions architect at Amazon Web Services. There are two potential options I can think of: 1) Use Amazon Glue to process initial files and CDC files on S3 and consolidate onto a single table on glue. And in the folder, each of the tables being replicated has its own folder created. In addition, AWS Glue lets you automatically The metadata in the table tells Athena where the data is located in Amazon S3, and specifies the structure of the data, for example, column names, data types, and the name of the table. In the previous post, we discussed how to create Azure SQL Server and Azure SQL Database. When you move your data, Vertica removes the data from its original location. On the target cluster, to view both the schemas, use the \dt command. When creating the replication instance, ensure that you select the Publicly accessible check box because the instance needs to access the Amazon S3 bucket outside your virtual private cloud (VPC). Move an existing table to the new Filegroup: If the filegroup you want to move the table to; doesn’t already exist then please create the secondary filegroup and then move the table. This post demonstrates an easy way to replicate a SQL Server database that’s hosted on an Amazon EC2 instance to an Amazon S3 storage target. The query is as follows. You can also download query results and save queries for later execution from the Athena console. browser. This is especially useful if you have multiple database sources and you need to quickly aggregate and query data from a common data store without having to set up and manage underlying infrastructure. It provides powerful capabilities for granularly replicating datasets to the desired target. All rights reserved. Editor. 3. specify. Athena is Amazon's recipe to provide SQL queries (or any function availabe in Preso) over data stored in flat files - provided you store those files in their object storage service S3. 2) Second option is again using Glue catalog to discovery metadata information for initial and CDC files and Glue will create 2 metadata tables. 2. If you needed to clear out the print queue after moving the data, you could run a Delete query after that. Today in this post, I will explain how to rebuild the oracle tables or move the different type of oracle tables using alter table move command. Step-by-Step, Getting Create Temporary Database and Copy Table to Temporary Database more information about AWS Glue and crawlers, see Integration with AWS Glue. Move the data from cluster1_table1 to another table in the target database. If you want to move a temporal table from one database to another, you cannot easily export data to the new temporal table due to its versioning history table. Once I resolved issues with the schema I intended to simply rename the table to its final name. the Therefore, before querying data, a When you replicate a relational database to the cloud, one of the common use cases is to enable additional insights on the replicated data. That would work, however I am not SQL smart. In the Export dialog box, change the name of the new object if you do not want to overwrite an existing object with the same name in the destination database. an Athena-managed internal data catalog that exists side-by-side with the AWS Glue Be sure to test the connection for both the endpoints. We can see the IndexName in the above screenshot. Because the data on the underlying Amazon S3 bucket is encrypted using Amazon S3 server-side encryption (SSE), you need to specify has_encrypted_data = ‘true’ for TBLPROPERTIES. You can see the DML delete statement captured on the AWS DMS dashboard: When you query data using Amazon Athena (later in this post), due to the way AWS DMS adds a column indicating inserts, deletes and updates to the new file created as part of CDC replication, we will not be able to run the Athena query by combining data from both files (initial load and CDC files). want to copy a table from database A to database B. Athena Query Editor, see Getting On the Scripting Options screen set Script Data to False. Regardless of how the tables are created, the tables creation process registers the will On the Athena console, using the Query Editor, type CREATE DATABASE sqlserver, with sqlserver being the name of the database. source data, the query result location that you The query is as follows. No data loading or transformation is required, and you can delete table definitions and schema without impacting underlying data in Amazon S3. The metadata in the table tells Athena where the data is located in Amazon S3, and If you wish to move tables also to some different tablespace, we can create a new tablespace for tables & write a script to move the tables. in to https://console.aws.amazon.com/athena/ for the first time. You need to define columns that map to source SQL Server data, specify how the data is delimited, and specify the location in Amazon S3 for the source data. This post breaks down the process into two high-level steps: Step 1: Replicate data from a SQL Server database to Amazon S3 When you replicate data to Amazon S3 using AWS DMS from supported database sources, both full load and change data capture (CDC) data is written as comma-separated values (CSV) format in the target Amazon S3 bucket. 4 TestDB_3 C:\Database\Data\TestDB_3.ndf 6.937500 (4 row(s) affected) To reclaim the disk space you can shrink the data file [TestDB] using DBCC SHRINKFILE, if that’s why you moved table to another file group. Click here to return to Amazon Web Services homepage, Replication Instances for AWS Database Migration Service, Using Table Mapping with a Task to Select and Filter Data. The replication instance should be able to connect to both the source database and the target Amazon S3 bucket. The source is SQL Server on an Amazon EC2 instance. You can also choose Find the table from the “Tables” list Click the three dots to the right of the table If you don’t have permissions to create an IAM role to access Amazon S3try to talk with the infrastructure or DevOps team of your organization so they can create it for you. Other AWS services AWS DMS names CDC files using time stamps. Started. Use the Athena API or CLI to run a SQL query string with DDL To read about some best practices and tuning tips for Amazon Athena, see this post on the AWS Big Data blog. Simply log in to the AWS Management Console, navigate to the Amazon Athena console, and in the Query Editor you will see the databases and tables that you created previously. table. If you've got a moment, please tell us what we did right In this example, you can use a dms.t2.medium instance for the replication. In order to make sure that the tables will be created in the destination database, click on the Edit Mappings button and make sure that the Create destination table option is ticked, and if any of your tables contain Identity column, make sure to tick the Enable identity insert option, then click the OK button. Athena applies schemas on-read, which means that your table definitions are applied to your data in Amazon S3 when queries are being executed. Please refer to your browser's Help pages for instructions. The AWS account that you use for migration should have write and delete access to the Amazon S3 bucket that is used as a target. This left me with creating the table with the new schema with a new name, generally just appending a number to it. On the AWS DMS console, choose Replication instances, and then Create replication instance, which takes you to the screen shown in the following image. source data. The table definition stored in the InnoDB shared tablespace includes the database name. On the task tab, choose Table statistics to verify the tables and rows that were replicated to the target database with additional details—in this case, HumanResources data: Next, verify your target Amazon S3 bucket: You can see the HumanResources folder that was created under the dms-replication-mrp bucket. a schema for underlying source data. DATABASE, and DROP TABLE under the hood to create tables and To move this table to the second file group name “MoveFile2” all I have to do is run the following command: -- Move table to filegroup MoveTable2 CREATE CLUSTERED INDEX IX_ID ON MoveTable.dbo.ToMove(ID) WITH(DROP_EXISTING=ON,Online=ON) ON [MoveFile2] GO. Note: To ensure that the tables you have selected will be created in the destination database, click the Edit Mappings button and check the Create Destination Table option. Select Source Tables and Views wizard will appear on the screen; choose the Tables you wish to transfer from source database to destination database and click Next. 2) Use the CREATE TABLE ... CLONE command and parameter to clone the table in the target schema. This registration occurs in the AWS Glue Data Catalog and enables Using sqlserver as the database, create a table using a CREATE TABLE statement, and then choose Run Query: You will create three tables from the HumanResources schema that was replicated by AWS DMS: Employee, Department, and Employee Dept History. With respect to how to combine data from both files to run Athena queries to reflect existing and new data inserts is beyond the scope of this blog post. For each source table, AWS DMS creates a folder under the specified target folder. To move the tables in the schema; To use the AWS Documentation, Javascript must be The purpose of table A is to record current payroll, and the purpose of table B is to store historical data (for running reports later) Anyway, I need a simple macro that when executed will do the following: 1. Dropping the old table and creating a new one fails as it appears the table’s schema is cached for some time. it in its own AWS Glue Data Catalog. The MySQL InnoDB engine has two types of tablespaces, one is a shared table space and second is an individual tablespace for each table. Summary Using AWS DMS and Amazon Athena provides a powerful combination. Note that AWS DMS does not edit or delete existing data from the CSV files on the Amazon S3 bucket but instead creates new files under the Amazon S3 folder. sorry we let you down. To open index properties, expand the DemoDatabase database >> expand Tables >> expand Indexes. Athena uses the AWS Glue Data Catalog to store Wizard. Filters the list of databases to those that match the regular_expression that you specify. Let’s look at two of the scenarios—inserts and deletes. For share the AWS Glue Data Catalog, so you can see databases and tables created throughout USE TestDB GO ALTER TABLE UserLog DROP CONSTRAINT PK__UserLog__7F8B815172CE9EAE WITH (MOVE … For each dataset, a table needs to exist in Athena. After the task runs, the initial load is completed followed by any changes as they happen on the source database. Databases are a logical grouping of tables, and also hold only metadata and Then use Athena with Glue catalog to query the table. Creating a database and tables in Amazon Athena. This can take additional time and configuration, especially if you’re looking to query the data interactively and aggregate data from multiple database sources to a common data store like Amazon S3 for ad hoc queries. your Copy table between servers, databases, schemas is a common requirement for all DBs. Although you can use Athena for many different use cases, it’s important to understand that Athena is not a relational database engine and is not meant as a replacement for relational databases. Tables are definitions of how your data is stored. Now that the task is created, verify it on the console. © 2021, Amazon Web Services, Inc. or its affiliates. If you want to move your existing table into some other schema, you can try below script. To create a table automatically, use an AWS Glue crawler from within Athena. DBO is a default schema of a SQL Server. From the “database” dropdown, select the “default” database or whatever database you saved your table in. Azure Data Factory: Click on Create a resource --> Analytics --> Data Factory Fill the mandatory fields and… 1) Use the ALTER TABLE ... RENAME command and parameter to move the table to the target schema. table and then issue a query against it using Athena. You can use AWS Database Migration Service (AWS DMS) and then interactively query data stored on Amazon S3 using Amazon Athena without having to set up a target database instance. Javascript is disabled or is unavailable in your Before you can create tables and databases, you need to set up the appropriate IAM permissions for Athena actions and access to Amazon S3 locations where data is stored. If you've got a moment, please tell us how we can make To move a table to a different filegroup involves moving the table’s clustered index to the new filegroup. this metadata, using it when you run queries to analyze the underlying Source Sample Temporal Table Let’s first create a source test database TestDB1. This launches automatically if you log In the Table Mappings section, choose the HumanResources schema. For example: ALTER TABLE db1.schema1.tablename RENAME TO db2.schema2.tablename; OR. Use the Athena console to run the Create Table There's no need to load files into a database - just create a simple data definition and away you go. Use the Athena console to write Hive DDL statements in the Query a To move all objects in a schema to a different schema, the following steps must be followed. Change the schema of a table: Be sure to choose the Settings button on the top right to note the staging directory. When you create tables and databases manually, Athena uses HiveQL data definition Right-click on the database name and choose Tasks -> Generate Scripts. Create tables for Department and Employee Dept History, and specify the appropriate Amazon S3 bucket locations. In Athena, tables and databases are containers for the metadata definitions that define If you have tables in Athena created before August 14, 2017, they were created in The required size of the instance varies depending on the amount of data needed to replicate or migrate. Athena can analyze structured, unstructured and semi-structured data stored in an S3 bucket. To be able to use the UNLOAD and COPY commands effectively we need to make use of the Amazon S3 service, create a S3 folder and have an IAMrole with permissions to access Amazon S3. Use Amazon Athena to run interactive queries for data that is stored on Amazon S3. table Also notice the Amazon S3 file location for Employee data. the The AWS Glue Data Catalog is accessible throughout your AWS account. Move data from Table A to Table B. Note: if your table contains LOB data, this method will not move LOB pages. After you create a table, you can use SQL SELECT statements to query it, including getting specific file locations for your Do one of the following: In the Append dialog box, click Current Database, select the destination table from the Table Name combo box, and then click OK.-or-In the Append dialog box, click Another Database. specify, Upgrading to the AWS Glue Data Catalog First, you create an Amazon S3 bucket named dms-replication-mrp in the same AWS Region as your database instance. To replicate the data and all tables under that schema, type % for Table name is like, as shown in the following screenshot: Table mapping specifies tables from a particular source schema that you want to replicate to the target endpoint. We have examples with sample data within Athena to show you how to create A detailed walkthrough of using Athena is beyond the scope of this post, but you can find more information in the Amazon Athena documentation. The S3 folder is going to be used as a bridge between the two Amazon Redshift databases. Now that source and target databases are being replicated, you can proceed to the second step, which is to configure Amazon Athena to interactively query data on this Amazon S3 bucket. I have created a new tablespace PROD_INDX for moving the indexes, so we need to only move indexes as tables will be in originally created tablespace. For each dataset, a table needs to exist in Athena. Insert queries are used to move data from one table to another. mysql> insert into sample.receive(UserId,UserName) -> select Id,Name from test.send; Query OK, 2 rows affected (0.21 sec) Records: 2 Duplicates: 0 Warnings: 0. You can use Athena to process logs and unstructured, semi-structured, and structured datasets. For wildcard character matching, you can use the combination . It can read Apache Web Logs and data formatted in JSON, ORC, Parquet, TSV, CSV and text files with custom delimiters. Create an Athena "database" I haven't had the opportunity to learn SQL yet. Similarly, the target endpoint is your Amazon S3 bucket. This article will demonstrate how to move a temporal table from one database to another in SQL server. This is the location AWS DMS used to replicate SQL Server data in step 1. Amazon S3. Display all records from the table sample.receive. Run a simple SELECT statement of employees using the sqldata_employee10 table: Because you’re using standard SQL to query data, you can use JOIN and other SQL syntax to combine data from multiple tables as in the following example: The following is another example that queries unique job titles from the Employee database: As you can see, the query time from Amazon Athena is pretty fast. In the File name box on the Export - Access Database dialog box, specify the name of the destination database and then click OK. This capability is especially useful if you’re building a data lake architecture using Amazon S3 as a central data store, and you want to extract specific datasets from multiple database sources and use them for downstream applications and analytics. DATA_STAGE in queries is the tablespace to be moved and ADURUOZ is the user to which we will move all of its objects. Athena, specific file locations for your As much as I would love too, time hasn't really allowed me to learn SQL. the structure of the data, for example, column names, data types, and the name of For more information, see Using Table Mapping with a Task to Select and Filter Data. For every insert to the source table, AWS DMS replicates the insert and creates a new file with a time stamp under the same target folder. The actual CSV file that contains data for each table is in respective folders. Creating the source and target endpoints After creating the replication instance, create the source and target endpoints. Athena to run queries on the data. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. When you perform a delete on the source table, AWS DMS replicates the delete and creates a new file for the delete row with similar time stamp details. Your query results are stored in Amazon S3 in the query result location that you AWS Service Integrations with The transaction IDs and log sequence numbers stored in the tablespace files also differ between databases. Creating a replication task Finally, create a replication task to replicate data from source to target. You can apply the analytics and query-processing capabilities that are available in the AWS Cloud on the replicated data. console that helps you get started creating a table based on data that is stored in You then use Athena with Glue catalog, have a SQL join syntax to query across the 2 tables. This seems to be slightly complicated by the fact that the table I want to copy is in fact really a link to table in database C I thought I could copy the table structure from db A to db B and the append the contents, using the command below. Data Catalog until you choose to update. To move LOB data you need to re-create the table. This solution works for both the types of tablespaces. Replicate data from a SQL Server database that is stored on an Amazon EC2 instance to an Amazon S3 target using AWS DMS. Here we can move the indexes using online option. What is my requirement is i need to move database tables and store procedures from different database so what i think for one time i have created SSIS package so that i can reuse it to move table and SP's by just only changing database server name when i have to move tables and SP's from different database server. Another possible approach to move very large table across different schemas ('A' to 'B'), mentioned above is very efficient but this leaves you with partitoned table with a 'dummy' partition in schema 'B' as compared to non partitioned table in schema 'A'.Now what is the most efficient way to convert it to regular table just like in the source. language (DDL) statements such as CREATE TABLE, CREATE Right-click PK_CIDX_Records_ID , as shown in the following image: As I mentioned, once clustered index moves to a secondary filegroup, the table will be moved to the secondary filegroup. In Athena, tables and databases are containers for the metadata definitions that define a schema for underlying source data. To replicate the database, you need to set up and configure a target database on Amazon EC2 or Amazon Relational Database Service (Amazon RDS). dataset. An additional bonus for you to explore is to expand this architecture to use Amazon QuickSight for easy visualization with its built-in integration with Amazon Athena. organization using Athena and vice versa. You can also use it to perform ad hoc analysis and run interactive queries for data that’s stored on Amazon S3. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.