A Data Warehouse is a central repository of data, which often originates from disparate sources. Businesses use Data Warehouses to analyze their data and make more informed decisions. Data warehouses enable enterprises to store enormous amounts of data in one place as Transactional Databases are typically not intended for complex reporting or queries across terabytes or petabytes of data.
Choosing the right solution to warehouse your data is just as necessary as how you collect data for Business Intelligence. Businesses need their data to be well-sorted, accessible, and easy to manipulate. Amazon’s Redshift is a Data Warehouse tool that offers such a blend of features. Amazon Redshift gathers data from the underlying table or tables using the user-specified SQL statement and stores the result set. You can even use the Redshift Create View command to help you to create a materialized view. Read on to find more about Amazon Redshift and how to create a view!
Table of Contents
Prerequisites
- Working knowledge of Amazon Redshift.
- An Amazon Redshift account.
Introduction to Amazon Redshift
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Amazon Redshift is a managed, fast, Cloud-based, Petabyte-scale Data Warehouse service by Amazon Web Services (AWS). Amazon Redshift is one of the first Cloud-native Data Warehouses and has a robust customer base. Users also have access to resources in Redshift Documentation that are useful for extracting value from their Data Warehousing initiatives.
Amazon Redshift has a similar querying language to PostgreSQL and is available through JDBC/ODBC. Anyone familiar with it can use their SQL skills to start engaging with Amazon Redshift Clusters. Also, Amazon Redshift is a Massively Parallel Processing (MPP) Database that gives it an edge in performance.
Key Features of Amazon Redshift
These are a few tactical features listed below:
1) Column-oriented Database
Data can be organized by rows or columns. Row-oriented systems can quickly process a large number of small operations, also known as Online Transaction Processing, or OLTP. In contrast, Amazon Redshift uses a Column-oriented Database that provides better speed when accessing large amounts of data. For example, in Amazon Redshift, users have to generally apply a smaller number of queries to much larger datasets. For managing queries on massive datasets, a Column-oriented Database allows Amazon Redshift to execute queries quickly.
2) End-to-End Data Encryption
Encryption is an essential pillar of data protection, and every business requires data privacy and security regulations. Amazon Redshift offers robust and customizable encryption options. You can choose an encryption standard that best fits your needs. Security encryption features are an AWS-managed or a customer-managed key, encrypted and unencrypted clusters, AWS Key Management Service or HSM, and single or double encryption.
3) Massively Parallel Processing (MPP)
MPP is where several processors apply a “Divide and Conquer” strategy to large data jobs. In the MPP method, a large data processing job is organized into smaller jobs distributed among a cluster of compute nodes. Since Amazon Redshift has to complete massive data jobs, MPP allows the processors to complete their computations simultaneously rather than sequentially.
Understanding the Fundamentals of SQL
SQL or Structured Query Language, developed in the 1970s, is a computer language widely used for querying and editing information on the data stored in Database Management Systems. It is extensively used for manipulating (execute, retrieve, insert, create, update, delete) Structured Data, particularly Relational Database Management Systems (RDBMS).
SQL is popular among Database Administrators and Data Analysts for handling information running and analytical queries. There are five basic types of SQL commands—Data Control Language (DCL), Data Definition Language (DDL), Data Query Language (DQL), Data Manipulation Language (DML), Transaction Control Language (TCL).
Views in SQL
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A VIEW in SQL is a virtual table containing rows, columns, and data from one or multiple tables. View does not exist physically in the database or holds any data. Similar to an SQL table, the name of the View is unique in a database. Views are beneficial for presenting specific datasets through complex queries in large databases, thereby assisting users to manipulate data while keeping the original data intact.
Understanding Tables Views in Amazon Redshift
Creating views on your tables helps organize your analysis. Especially in OLAP systems like Amazon Redshift, you can arrange long queries into smaller parts as the analysis is usually quite complex.
The Redshift Create View command can help you create a view for Amazon Redshift data with ease. You need to pass the query that you wish to use for the View as a parameter. You also have to give the following details:
- Name: This is the name of the View/table that you will create. If you want a Temporary View only for the current session, prefix the ‘name’ with a ‘#’ sign.
- Column_name: It’s an optional command to rename the columns of the table/View that you’ll create. By default, the names of the columns will be derived from the original table.
- OR REPLACE: This command tells Amazon Redshift what to do if you already have a View with the existing name. It will replace the name only if the query is different.
In the coming sections, you’ll learn the syntax of using the Amazon Redshift Create View command. You’ll come across few examples of the Amazon Redshift Create View command that will help you get started in no time!
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Syntax for Redshift Create View Command
Here are the 2 ways in which you can use the Amazon Redshift Create View command to create the desired view for your data:
1) Syntax to Create a General View
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2) Syntax to Create a Materialized View
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These were two ways you use the Amazon Redshift Create View command to start creating desired table views.
7 Key Parameters for Redshift Create View Command
Here are some of the essentials parameters that you must provide while using the Amazon Redshift Create View command:
1) Or Replace
Similar to tables, Views can have unique names. To create a new View for the same name that already exists in the database, you can replace it with the OR REPLACE command. However, you can only replace a View with a new query that uses the same column names and data types and generates an identical set of columns.
2) Create or Replace View
This will lock the View for reads and writes until the operation completes. When replacing a View, the system preserves properties like granted privileges and ownership.
3) Name
You can either give a Schema Name (such as myschema.myview) as the name of the View and the View is created using the specified schema. Otherwise, the View is made in the current schema.
You can also have a Temporary View visible only in the current session by specifying a View name that begins with ‘#.‘
4) Column_Name
This is for giving a list of names to be used for the columns in the View. If you don’t pass this information, the system will take the column names from the query. You can define a maximum number of 1,600 columns in a single View.
5) Query
A query is an inquiry in the form of SELECT statements about a set of data.
6) With No Schema Bindings
Amazon Redshift’s No Schema Binding clause specifies that the View isn’t bound to the underlying database objects, such as tables and user-defined functions. With the No Schema clause, users can create Views even if the referenced object doesn’t exist as there is no dependency between the View and the objects it references.
Since there is no dependency on the object, users can also alter or drop a referenced object without affecting the View. Also, Amazon Redshift checks for dependencies only when you query the view.
If you want to view details about late binding views, run the following command:
PG_GET_LATE_BINDING_VIEW_COLS
You must qualify tables and views referenced in the SELECT statement with a schema name when you include the WITH NO SCHEMA BINDING clause. The schema must exist when the View is created, even if the referenced table doesn’t exist.
For example, the following statement will return an error:
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However, the following statement will run successfully:
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7) Late Binding View
Until the view is queried, a Late-Binding View doesn’t check the underlying database objects, such as tables and other views. As a result, users can add, alter, or drop the underlying objects and the View won’t drop or refresh. However, the queries to the Late-Binding View will fail if you drop underlying objects. The query will also fail if it references columns in the underlying object that isn’t present in the late-binding view.
You can create a Late-Binding view by including the ‘WITH NO SCHEMA BINDING‘ clause in your command. Here is an example that will create a view with no schema binding:
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The following example shows how you can alter the objects of an underlying table without recreating the view:
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By keeping the above mentioned parameters in mind, you can use thee Amazo Redshift Create View command efficiently!
Examples of Redshift Create View Command
Here are some examples of the Redshift Create View command:
The following command creates a ‘myevent’ View from a table called EVENT:
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The following command creates a ‘myuser’ View from a table called USERS:
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The following command creates or replaces a ‘myuser’ view from a table called USERS:
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The following example creates a No Schema Binding View:
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Limitations of Amazon Redshift Table Views
- You cannot DELETE or UPDATE a Table View. You can create Temporary Views that last only for the current session, but you cannot affect its data. To store the results of underlying queries, you have to use a Materialized View that you can refresh at your convenience.
- Table View is a pseudo-table and isn’t physically materialized, so there’s no actual table created in the database. Instead, the query executes every time you request access to the Table View.
Conclusion
Amazon Redshift offers a scalable data processing solution that delivers lightning-fast performance without extensive investment in infrastructure. Amazon Redshift also provides access to compliance features, various Data Analytics tools, and AI and ML applications. With Redshift Create View command, users can better visualize specific rows and columns pertinent to their current task rather than analyzing the entire complex and large datasets.
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