Harnessing Redshift Super Data Type: Examples & Insights

AWS Redshift is a well-known data warehousing solution that can handle exabytes of data. You might be interested in using the service for a variety of purposes, including real-time analytics, combining multiple data sources, log analysis, and more.

Amazon Redshift SUPER Data type is a collection of schemaless array and structure values that encompasses all other Amazon Redshift scalar kinds & enables semi-structured data to be stored, transformed, and analyzed. Data stored in semi-structured databases doesn’t follow a rigid schema as it does in relational databases. 

In this article, you will understand what Redshift SUPER data type is and what Amazon Redshift is, and why you should use it.

What is Amazon Redshift?

AWS Redshift is Amazon Web Services’ solution for data warehousing. Amazon Redshift is a fully managed petabyte-scale cloud data warehouse product for storing and analyzing large data sets. One of Amazon Redshift’s main strengths is its ability to handle large amounts of data – capable of processing unstructured and structured data up to exabytes. Data migrations of large scale can also be accomplished with the service. Redshift is AWS’ Data Warehousing Solution. Like other Data Warehouses, Redshift is used for Online Analytical Processing (OLAP) Workloads. 

The service, like many others offered by AWS, is easily deployed with a few clicks and provides a variety of import options. Redshift data is encrypted as well for an additional layer of security.

Using Redshift, you can gather relevant insights from a vast amount of data. AWS provides a simple interface for automatically creating clusters, eliminating the need to manage infrastructure.

To know more about AWS Redshift, follow the official documentation here.

Key Features of Amazon Redshift

• Redshift allows users to write queries and export the data back to Data Lake.
• Redshift can seamlessly query the files like CSV, Avro, Parquet, JSON, ORC directly with the help of ANSI SQL.
• Redshift has exceptional support for Machine Learning, and developers can create, train and deploy Amazon Sagemaker models using SQL.
• Redshift has an Advanced Query Accelerator (AQUA) which performs the query 10x faster than other cloud data warehouses.
• Redshift’s Materialistic view allows you to achieve faster query performance for ETL, batch job processing, and dashboarding.
• Redshift has a petabyte scalable architecture, and it scales quickly as per need.
• Redshift enables secure sharing of the data across Redshift clusters.
• Even when thousands of queries are running at the same time, Amazon Redshift delivers consistently fast results.
• Each value stored or retrieved by Amazon Redshift has a data type with a predetermined set of related attributes. When tables are formed, data types are defined.

Benefits Of Amazon Redshift

• Speed: With the use of MPP technology, the speed of outputting large amounts of data is unprecedented. The cost AWS provides for services is unmatched by other cloud service providers.
• Data Encryption:  Amazon provides data encryption for all parts of your Redshift operation. The user can decide which processes need to be encrypted and which ones do not. Data encryption provides an additional layer of security. 
• Familiarity: Redshift is based on PostgreSQL. All  SQL queries work with it. In addition, you can choose the SQL, ETL (extract, transform, load), and business intelligence (BI) tools you are familiar with. You are not obligated to use the tools provided by Amazon.
• Smart Optimization: If your dataset is large, there are several ways to query the data with the same parameters. Different commands have different levels of data usage. AWS Redshift provides tools and information to improve your queries. These can be used for faster and more resource-efficient operations. 
• Automate Repetitive Tasks: Redshift can automate tasks that need to be repeated. This can be an administrative task such as creating daily, weekly, or monthly reports. This can be a resource and cost review. It can also be a regular maintenance task to clean up your data. You can automate all of this using the actions provided by Redshift. 
• Simultaneous Scaling: AWS Redshift automatically scales up to support the growth of concurrent workloads.
• Query Volume: MPP technology shines in this regard. You can send thousands of queries to your dataset at any time. Still, Redshift is never slowing down. Dynamically allocate processing and memory resources to handle increasing demand.  
• AWS Integration: Redshift works well with other AWS tools. You can set up integrations between all services, depending on your needs and optimal configuration.
• Redshift API: Redshift has a robust API with extensive documentation. It can be used to send queries and get results using API tools. The API can also be used in Python programs to facilitate coding. 
• Safety: Cloud security is handled by Amazon, and application security in the cloud must be provided by the user. Amazon offers access control, data encryption, and virtual private clouds to provide an additional level of security.  
• Machine Learning: machine-learning concepts are used by Redshift to predict and analyze queries. In addition to MPP, this makes  Redshift perform faster than any other solution on the market.  
• Easy Deployment: Redshift clusters can be deployed anywhere in the world from anywhere in minutes. In minutes, you’ll have a powerful data warehousing solution at a fraction of the price of your competitors.
• Consistent Backup: Amazon automatically backs up your data regularly. It can be used for recovery in the event of an error, failure, or damage. Backups are distributed in different locations. This eliminates the risk of confusion on your site.
• AWS Analytics: AWS offers many analytical tools. All of this works well with Redshift. Amazon provides support for integrating other analytics tools with Redshift. Redshift being the child of the AWS community has native integration capabilities with AWS analytics services.  
• Open Format: Redshift can support and provide output in many open formats of data. The most commonly supported formats are Apache Parquet and Optimized Row Columnar (ORC) file formats.
• Partner Ecosystem: AWS is one of the first cloud service providers that started the market of Cloud Data Warehouses. Many customers rely on Amazon for their infrastructure. In addition, AWS has a strong network of partners to build third-party applications and provide implementation services. You can also leverage this partner ecosystem to see if you can find the best implementation solution for your organization.

Why Amazon Redshift?

Amazon Redshift is an Online Analytic Processing Columnar Database. Redshift is based on the PostgreSQL 8.0.2 database. This means that it can be queried with regular SQL queries. That doesn’t make it unique. One of the reasons Redshift stands out from other big data engines is the fast response to queries on large data sets.

The Massively Parallel Processing (MPP) design of Redshift is responsible for its fast querying. MPP uses a large number of processing units to perform computations in parallel. It is sometimes possible to deliver a process using processors spread across multiple servers. 

Replicate your Data into Amazon Redshift:

Understanding Redshift SUPER Data Type

• What is Redshift SUPER Data Type?
• Examples of Redshift SUPER Data Types

What is Redshift SUPER Data Type?

As an Amazon Redshift data type, SUPER supports storing schemaless arrays and structures containing Amazon Redshift scalars and potentially nesting arrays and structures. With Redshift SUPER Data type, semistructured data can be stored in a variety of formats, including JSON. It is possible to add a new scalar column, called SUPER, to store semistructured data, and then write queries from the scalar columns and the SUPER column. 

Currently, the Amazon Redshift platform supports two types of semistructured data – the Redshift SUPER data type and the Spectrum.

You should use the Redshift SUPER data type when inserting or updating small batches of JSON data. In addition, the Redshift SUPER Data type is ideal when you need strong consistency, predictable query performance, support for complex queries, and ease of use with changing schemas and schemaless data.

These are the properties of the Redshift SUPER Data type:

• A scalar value in Amazon Redshift:
  • A null
  • A boolean
  • Small int, integers, big ints, decimals, or floating points (such as float4 or float8).
  • String values like varchar or char.
• A complex value is:
  • An array of scalar or complex values.
  • As well as a tuple or object, attributes may be mapped to their names and values as well (scalar or complex).

Neither of the two types of complex values has any restrictions based on regularity.

Redshift SUPER data types can be used to store semistructured data when in a schemaless form. Hierarchical data models can always be changed, but old version data can still coexist in the same SUPER column.

Examples of Redshift SUPER Data Types

The following examples illustrate how to use semistructured data with the Redshift SUPER Data type.

• Flexible and Rapid Insertion of JSON Data: In Amazon Redshift, JSON can be parsed into a SUPER value and stored as a rapid transaction. Inserting data into the table with shredded attributes of Redshift SUPER Data type can be performed up to five times faster than if it were inserted into a table with conventional columns. As an example, let’s say that the incoming JSON contains elements [“a”:.., “b”:.., “c”:…, …}. The performance of inserting the incoming JSON into a table TR with columns ‘a’, ‘b’, ‘c’, and so on will be greatly enhanced if the JSON is stored into a table TJ with a single SUPER column S. The performance advantage of Redshift SUPER data types becomes apparent when a JSON file contains hundreds of attributes. As well, Redshift SUPER data types do not require regular schemas. The incoming JSON does not need to be inspected and cleaned up before storage. Imagine, for example, that some JSON requests contain a string “c” attribute and others contain an integer “c”, but these do not contain the Redshift SUPER data type.  In this case, that data must either be separated into c_string and c_int columns, or it must be cleaned up. Redshift SUPER data types, on the other hand, save the entirety of JSON data during ingestion without losing any information. You can then analyze the information using the PartiQL extension of SQL.
• Flexibility in Querying: Your semistructured data (such as JSON) can be retrieved without schema once stored as a Redshift SUPER data type value. If you are familiar with PartiQL, you can use its lax semantics and dynamic typing to run queries and locate deeply nested data without having to impose a schema beforehand.
• Query Flexibility for (ETL) Process into Conventional Materialized Views: Once your semistructured and schemaless data is stored as Redshift SUPER Data type, you can use PartiQL materialized views to inspect it and shave it into materialized views. A materialized view with shredded data is an excellent example of maximizing performance and user experience over traditional analytics. The materialized views of Amazon Redshift provide better performance when performing analytics on the shredded data. Additionally, clients wishing to view their ingested data in a conventional schema can utilize views to present the data. The PartiQL materialized views can be queried after the JSON or Redshift SUPER data type has been extracted into their conventional columnar materialized views.

Loading Data into SUPER Columns

Redshift SUPER data type can be used for loading, storing, and querying hierarchical and generic data in Amazon Redshift. Amazon Redshift supports various functions for loading JSON files and converting them to Redshift SUPER data type format. For example, you can load data into SUPER columns using the json_parse function and the COPY command. In addition to JSON and Avro, including text, comma-separated value (CSV), Parquet, and ORC.

• Parsing JSON Documents into SUPER Columns
• Copying JSON Document into Single SUPER Column

Parsing JSON Documents into SUPER Columns

The json_parse function can be used to insert or update JSON data into a SUPER column. In this function, data is parsed from JSON and converted into a Redshift SUPER data type, which can be used in INSERT and UPDATE statements.

In the example below, JSON data is inserted into a SUPER column. Amazon Redshift treats a value that does not have the json_parse function as a single string rather than a JSON-formatted string that must be parsed.

Amazon Redshift requires you to pass the entire document to column values when updating the Redshift SUPER data type column, and it doesn’t support partial updates.

INSERT INTO region_nations VALUES(0,
   'lar deposits. blithely final packages cajole. regular waters are final requests. regular accounts are according to',
   'AFRICA',
   JSON_PARSE('{"r_nations":[
      {"n_comment":" haggle. carefully final deposits detect slyly agai",
         "n_nationkey":0,
         "n_name":"ALGERIA"
      },
      {"n_comment":"ven packages wake quickly. regu",
         "n_nationkey":5,
         "n_name":"ETHIOPIA"
      },
      {"n_comment":" pending excuses haggle furiously deposits. pending, express pinto beans wake fluffily past t",
         "n_nationkey":14,
         "n_name":"KENYA"
      },
      {"n_comment":"rns. blithely bold courts among the closely regular packages use furiously bold platelets?",
         "n_nationkey":15,
         "n_name":"MOROCCO"
      },
      {"n_comment":"s. ironic, unusual asymptotes wake blithely r",
         "n_nationkey":16,
         "n_name":"MOZAMBIQUE"
      }
   ]
}'));

Copying JSON Document into a Single SUPER Column

Creating a table with a single Redshift SUPER type data column allows you to copy a JSON document into that column.

CREATE TABLE region_nations_noshred (rdata SUPER);

You will need to copy the Amazon Simple Storage Service (Amazon S3) data into a single super data column. You must specify the no shred option in the FORMAT JSON clause for JSON data to be ingested into one SUPER data column.

COPY region_nations_noshred FROM 's3://redshift-downloads/semistructured/tpch-nested/data/json/region_nation'
REGION 'us-east-1' IAM_ROLE 'arn:aws:iam::xxxxxxxxxxxx:role/Redshift-S3'
FORMAT JSON 'noshred';

Once COPY has successfully ingested the JSON, your table will have a rdata SUPER data column containing the object’s data. As a result, all JSON hierarchy properties are preserved within ingested data. Additionally, to make query processing more efficient, the leaves are converted to Amazon Redshift scalar types.

The original JSON string can be retrieved using the following query.

SELECT rdata FROM region_nations_noshred;

Upon generating a SUPER data column, Amazon Redshift makes it available through JDBC as string data through JSON serialization.

Conclusion

In this post, you learned how Redshift Super data types improve storage efficiency, performance, or streamline analysis. Amazon Redshift SUPER Data type, which is coupled with PartiQL, also makes it easier to query relational and hierarchical data models.

As you collect and manage your data across several applications and databases in your business, it is important to consolidate it for a complete performance analysis of your business. However, it is a time-consuming and resource-intensive task to monitor the Data Connectors continuously. To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse like Amazon Redshift, or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data.

FAQ on Redshift Super Data Type