Organizations face a discernible lag in performance with the ever-increasing rise in data. Traditional data warehouses become a financial burden with time despite proper planning as companies also suffer storage limitations.

However, Amazon rolled out Redshift, providing a cloud-based data warehouse solution that not only addresses data storage and processing issues but also integrates with business intelligence tools for data analysis.

As it utilizes SQL at the backend, analysts get the edge of using the benefits of the query language to interact with real-time data stored in data warehouses. This article gives an overview of the Amazon Redshift extract command. It explains the importance and benefits of Redshift and SQL queries.


  • Understanding of SQL Date and Time data type.

Introduction to Redshift

Amazon Redshift Extract- Redshift Logo

The ever-increasing rise in data is a big concern for traditional data warehouses, resulting in a lack of productivity. However, Redshift architecture tackles this challenge with massive parallel processing (MPP) and data compression techniques.

Redshift is a cloud-based, petabytes scaled data warehouse solution provided by Amazon. Redshift offers a blend of features and easily integrates with business intelligence (BI) tools using Redshift Data API. With SQL at the backend, it enables analysts to draw valuable insights from the huge underlying data.

Benefits of Redshift

To get the most of Redshift, it is essential to understand what it brings to the table before deciding to integrate it with your system. Below are a few benefits of using Redshift:

1. Performance

Redshift delivers reliable query performance for datasets ranging from a few gigabytes to petabytes of data. The technique of columnar storage, data compression, and zone maps in Redshift reduces the amount of I/O memory needed to perform queries.

To save a significant part of memory, a part of the table is queried by creating Redshift materialized views. Views allow analysts to achieve significantly faster query performance, which improves extract, transform, and load (ETL) data processing jobs.

2. Secure

Redshift provides comprehensive security to satisfy user requirements at no extra cost. With just a couple of parameter settings, Amazon Redshift allows using a secure sockets layer (SSL) to secure data in transit. It also enables users to configure firewall rules to control network access of data warehouses.

3. Pricing

Based on the type and number of nodes in the cluster, AWS caters to businesses of all sizes. Redshift pricing starts at $0.25 per hour with no prior commitments and up-front costs.

With a pause and resume feature, you only pay for backup storage when the cluster is paused. This feature not only frees organizations from planning and purchasing data warehouse capacity but also enables them to manage finance when data gets scaled with time.

4. Automation

Redshift automates some routine maintenance tasks so that users focus on generating insights and not on data warehouses. To ease the user experience, most administrative tasks such as backup and data replication are automated.

In addition, all-new capabilities are released transparently, eliminating the need to schedule and apply upgrades and patches.

5. Integration

As Amazon Redshift has an extensive list of partners, it offers large-scale integration of many industry-leading tools for performing ETL processes, data modeling, and data visualization.

6. Flexible

Redshift runs SQL at the backend to execute queries within the Query editor or to connect an external SQL client tool, BI tool. With Redshift Data API, all types of data — traditional, cloud-based, serverless web services-based applications — can be accessed effortlessly.

As API manages and buffers data, it is asynchronous (results can be retrieved later), and query results are stored for 24 hours.

Understanding the Fundamentals of SQL

Amazon Redshift Extract- Fundamentals of SQL
Amazon Redshift Extract- Fundamentals of SQL

A programming language involves a set of instructions to perform specific tasks on computers. SQL is one such programming language that assists in dealing with complex data in databases more effectively.

Organizations have to store and manage huge amounts of data to stack extensive information that requires a database.

A database includes many tables that have numerous rows and columns in an organized format. SQL queries help in handling the entire database through various commands. Here are the key aspects to understanding the SQL language:

Types of Commands

SQL commands are instructions used to communicate with databases to perform desired tasks. Based on the type of information required, SQL queries are classified into five parts as shown below:

1. Data Definition Language

Data definition language (DDL) deals with the structure of tables within the database. DDL commands include — CREATE, ALTER, DROP, and TRUNCATE. All DDL commands are auto-committed, which means they permanently save all changes in the current database.

2. Data Manipulation Language

Data manipulation language (DML) deals with the modification of data in databases. DML commands include — INSERT, UPDATE, and DELETE. As DML commands are not auto-committed, they can be rolled back.

3. Data Query Language

Data query language (DQL) is used to fetch desired data from a database. It consists of a ‘SELECT’ command to choose attributes of interest, and a ‘WHERE’ clause to return specific results from the entire data set.

4. Data Control Language

Data control language (DCL) deals with the authorization of users for accessing a database. The DCL command consists of — ‘GRANT’ and ‘REVOKE’ that gives database administrators authority to give and take back various permissions allocated to users.

5. Transaction Control Language

Transaction Control Logic (TCL) deals with a set of instructions grouped as a single execution unit. The TCL commands consist of — ‘COMMIT,’ ‘SAVEPOINT,’ ‘ROLLBACK,’ and ‘SET TRANSACTION.’ TCL commands cannot be used while creating a table (DDL) and are hence used only with DML commands.

A transaction initiates with a specific task and concludes when all the relative tasks are logically executed. TCL consists of two results — success and failure. If any of the functions fails to run, then the entire transaction process fails.

Benefits of SQL

SQL is also called the language of a database, making it one of the most valuable assets for companies. Below are a few benefits of SQL:

1. Speed

Using SQL queries, millions of rows in structured data can be retrieved very quickly. Moreover, analysts optimize queries by understanding data statistics and tuning the performance by taking corrective actions.

2. Standards

As SQL standards are governed by ISO and ANSI (ISO/IEC 9075:2016), it is universally accepted as the database query language.

3. Simple

As SQL commands are similar to English words, it doesn’t require prior knowledge of programming languages like Java or C++. 

Syntax of Redshift Extract Command

The EXTRACT function returns a part of the date or time from TIMESTAMP, TIME, or TIMETZ expression, such as a day, month, year, hour, minute, second, millisecond, microsecond. Below is the general syntax for the EXTRACT command:

EXTRACT ( datepart FROM { TIMESTAMP 'literal' | time | timetz } )


SQL queries consist of a series of arguments as input to SQL functions, which can either be a text or a number. Below are a few arguments to consider:

  • datepart: This is a Redshift SQL function that accepts integer arguments. It includes specific parts of the date value like — year, month, day. For further details, refer to Date parts for Date or Timestamp functions.
  • literal: A timestamp value, which is enclosed in single quotes and preceded by the ‘timestamp’ keyword.
  • timestamp | times | timestz: A ‘timestamp,’ ‘timestamptz,’ ‘time’, or ‘timetz’ column or an expression that implicitly converts to a timestamp, timestamp with time zone, time, or timetz.

Return Type

Functions are grouped by the data types of their argument and their return value. Extract command returns an integer if the argument is ‘timestamp,’ ‘time’, or ‘timetz.’ Similarly, it would return double precision if the argument is ‘timestamptz.’

Examples of Redshift Extract Query

Here are a few examples of Redshift Extract Query:

Redshift Extract Query: Timestamp Column

If we want to find the week number of sales, when the price paid was $10,000 or more, we can use the below query:

Redshift Extract- Timestamp Column
Timestamp Column

Similarly, if we want to return minute value from literal ‘timestamp’ value, use the below query:

Redshift Extract- Return Timestamp
Return Timestamp

Time Column

Sometimes a column may have a time (hh:mm:ss) data type. For instance, time_val (time type) is a column present in the time_test table, having three values as below:

Redshift Extract- Time Column
Time Column

A user can query hours, minute, or milliseconds values from the time_val column using the below queries: 

Redshift Extract- Query Time Hours
Query Time Hours
Redshift Extract- Query Time Minutes
Query Time Minutes
Redshift Extract- Query Time Seconds
Query Time Seconds

Timetz Column

If you want to know the values of a timetz_value column (timetz type) in a timetz_test table, use the below query:

Redshift Extract- Timetz Column
Timetz Column

It should be noted that literals are not converted to UTC (coordinated universal time) before initiating the extraction process. For the above result, if the user wants to extract hours, or milliseconds from a literal value, use the following queries:

Redshift Extract- Timetz Column Hours
Timetz Column Hours
Redshift Extract- Timetz Column Date_Part
Timetz Column Date_Part
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Traditional data warehousing technology battles querying large datasets and experiences delays in achieving desirable results.

Amazon Redshift Extract accompanies a simple SQL at the backend while connecting with BI tools that display critical parameters on the dashboard interface to query exabytes of data.

As Redshift also connects with other AWS products and services, it enables analysts to provide secure, cost-effective, and scalable solutions for the expansion of businesses.

Schedule your demo to discover our powerful, easy-to-use ETL tool, praised for its accuracy and range of integrations.

Amit Kulkarni
Technical Content Writer, Hevo Data

Amit Kulkarni specializes in creating informative and engaging content on data science, leveraging his problem-solving and analytical thinking skills. He excels in delivering AI and automation solutions, developing generative chatbots, and providing data-driven AI & ML solutions. Amit holds a Master's degree and a Bachelor's degree in Electrical Engineering, consistently achieving distinction in his studies.

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