AWS (Amazon Web Services) is one of the leading providers of Cloud Services. It provides Cloud services like Amazon Redshift, Amazon S3, and many others for Data Storage. Extract, Transform, Load are 3 important steps performed in the field of Data Warehousing and Databases. So, extracting and loading data from these data storage is one of the important steps in the ETL (Extract, Transform, Load) process.
In the ETL process, you load raw data into Amazon Redshift. After performing Transformation and Schema Management on this data, your Amazon Redshift now has valuable Analysis-ready data. However, you don’t want to perform queries on Amazon Redshift directly. You wish to load this data into your local system or other Cloud service platforms like Amazon S3 to perform queries on this data. This is where unloading Data from Amazon Redshift to S3 helps. AWS S3 is an AWS storage repository that is connected with almost all AWS-supported data and Analysis services.
In this article, you will be introduced to Amazon Redshift and Amazon S3. You will also learn about the key features of these AWS Tools. Moreover, you will understand various methods to unload data from Amazon Redshift to S3. At the end of this article, you will explore some of the limitations associated with these methods. So, read along to gain a better understanding of unloading data from Amazon Redshift to S3.
Prerequisites
Before proceeding further to unload data from Redshift to S3, you need the following:
- An Amazon Redshift Account.
- An Amazon S3 Account.
- Working knowledge of Amazon Redshift.
- Working knowledge of Amazon S3.
Introduction to Amazon Redshift
Amazon Redshift is a popular Cloud Data Warehouse provided by Amazon. Amazon Redshift can handle petabyte-scale workloads. In addition, it allows you to query and aggregate exabytes of Structured and Semi-Structured Data across your Data Warehouse, Operational Database, and Data Lake using standard SQL.
Key Features of Amazon Redshift
Amazon Redshift supports Massively Parallel Architecture which is made using a cluster of processing nodes. In addition, there are a plethora of other features that make Amazon Redshift so popular. Let’s discuss some of the key features of Amazon Redshift.
- Supports Virtual Private Cloud: Within Virtual Private Cloud, users can start Amazon Redshift and manage access to the Cluster via the virtual networking environment.
- Encryption: Amazon Redshift Data may be encrypted and configured during the creation of Redshift tables.
- Secure Connections: SSL(Secure Sockets Layer) encryption is used to encrypt client-Redshift communications.
- Scalable: The number of nodes in your Amazon Redshift Data Warehouse can be easily scaled with a few easy clicks. You may also scale your storage capacity without performance losses.
- Cost-Effective: Amazon Redshift is an efficient alternative to traditional data storage. It supports an on-demand pricing structure.
To explore Amazon Redshift, visit the official website here.
Introduction to Amazon S3
Amazon Simple Storage Service (Amazon S3) delivers industry-leading Scalability, Accessibility, Security, and Object Storage performance. Data can be saved as objects within “Bucket” resources. It supports a single object up to a size of 5 terabytes. Objects can be accessed through S3 Access Points or by the Bucket Hostname. Moreover, Amazon S3 offers easy-to-use management features to help you manage your data.
Key Features of Amazon S3
Amazon S3 is a completely managed Object Storage service accessed entirely through web APIs and AWS-provided CLI utilities. It provides various features to its user. Let’s discuss some of the key features of Amazon S3.
- Easy to Use: You can use and interact with Amazon S3 easily at a low cost.
- Secure: Amazon S3 enables SSL data transport and automatically encrypts the data once it is loaded. By establishing Bucket policies through AWS IAM, the user has full control over his data.
- Scalable: Amazon S3 is highly scalable and you don’t need to worry about any storage.
- High Performance: Amazon S3 is built into Amazon’s CloudFront, which offers low latency content to end-users and fast data transfer rates without any minimum use obligations.
- AWS Integrations: Amazon S3 is integrated with an ample number of AWS services like Amazon CloudFront, Amazon Kinesis, Amazon RDS, AWS Lambda, etc.
To explore Amazon S3, visit the official website here.
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Methods to Unload Data from Amazon Redshift to S3
Amazon Redshift is developed around industry-standard SQL with added functionality to manage large datasets & support high-performance analysis & reporting of those datasets. Following are the two methods that you can follow to unload your data from Amazon Redshift to S3:
Method 1: Unload Data from Amazon Redshift to S3 using the UNLOAD command
Amazon Redshift supports the “UNLOAD” command which takes the result of a query, and stores the data in Amazon S3. This command works opposite to the “COPY” command where it grabs the data from an Amazon S3 bucket and puts it into an Amazon Redshift table.
So, to export your data from Amazon Redshift to S3 using the “UNLOAD” command, enter the following commands:
UNLOAD ('select-statement')
TO 'S3://object-path/name-prefix'
iam_role 'arn:aws:iam::<aws-account-id>:role/<role-name>'
[ option [ ... ] ]
where option is
{ [ FORMAT [ AS ] ] CSV | PARQUET
| PARTITION BY ( column_name [, ... ] ) [ INCLUDE ]
| MANIFEST [ VERBOSE ]
| HEADER
| DELIMITER [ AS ] 'delimiter-char'
| FIXEDWIDTH [ AS ] 'fixedwidth-spec'
| ENCRYPTED [ AUTO ]
| BZIP2
| GZIP
| ZSTD
| ADDQUOTES
| NULL [ AS ] 'null-string'
| ESCAPE
| ALLOWOVERWRITE
| PARALLEL [ { ON | TRUE } | { OFF | FALSE } ]
| MAXFILESIZE [AS] max-size [ MB | GB ]
| REGION [AS] 'aws-region' }
To learn more about the “UNLOAD” command, read Guide to Redshift Unload Command.
Load Amazon S3 to Redshift
Load Amazon S3 to BigQuery
Method 2: Unload Data from Amazon Redshift to S3 in Parquet Format
Apache Parquet is an Open Source file format accessible for any Hadoop ecosystem. It is designed for efficient flat column data storage compared to row-based formats such as CSV.
You may unload the results of an Amazon Redshift query as an Apache Parquet, into your Amazon S3. The Parquet format can be unloaded up to 2 times faster and occupies up to 6 times less capacity than text formats in Amazon S3. This allows you to preserve your Amazon Redshift data transformation and enhancement in an open manner on your Amazon S3 data lake.
To unload data from Amazon Redshift to S3 in Parquet format, enter the following commands:
UNLOAD ('select-statement')
TO 'S3://object-path/name-prefix'
iam_role 'arn:aws:iam::<aws-account-id>:role/<role-name>'
FORMAT AS PARQUET
MAXFILESIZE <file_size>
You can modify the above options as per your use case. But there are some of the limitations associated with the Parquet format, that you need to keep in mind while entering the commands. Some of these are:
- The Parquet Unload does not employ the compression of file level. The SNAPPY is used to compress each row group.
- The maximum default file size is 6.2 GB if MAXFILESIZE has not been defined. The file size can vary between 5 MB to 6.2 GB. When the file is written, the actual file size is estimated and this may not be equal to the size you provide.
- Amazon Redshift aims to build parquet files that have 32-MB row groups of the same size to enhance scanning performance.
- If a column utilizes the data format of TIMESTAMPTZ, it solely unloads timestamp values. The information about the time zone is not unloaded.
To read more about unloading data from Redshift to S3, you can refer to Amazon Web Services Documentation here.
Limitations of Unloading Data From Amazon Redshift to S3
Now, that you gained the basic knowledge of unloading data from Redshift to S3, let’s discuss some of the limitations associated with the above method.
- The “UNLOAD” command writes the data serially and sorts it completely if the “ORDER BY” query is used. For a data file, the maximum amount is 6.2 GB. “UNLOAD” generates extra files up to 6.2 GB each if the file’s data size is larger than the limit.
- Implicit data type conversions that occur by default might cause data corruption.
- The manual process to set up Redshift and S3 for data export can become cumbersome and complex.
Conclusion
In this article, you learned about Amazon Redshift and Amazon S3. You also understood the method to unload data from Amazon Redshift to S3 using the “UNLOAD” command. In addition, you also understood the other method to unload in Parquet format. At the end of the article, you also explored some of the limitations associated with the above two methods.
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Frequently Asked Questions
1. Can Redshift connect to S3?
Yes, Redshift can connect to Amazon S3. You can load data from S3 into Redshift using the COPY command and export data from Redshift to S3 using the UNLOAD command.
2. How do I push data from Redshift to S3?
You can push data from Redshift to S3 using the UNLOAD command. This command exports the result of a query from Redshift to one or more files in an S3 bucket.
3. What is the relationship between Redshift and S3?
Amazon Redshift and Amazon S3 are closely integrated. S3 is often used as a staging area for data that will be loaded into Redshift, and Redshift can also unload data to S3 for archiving or further processing.
4. How do I migrate data from RDS to S3?
To migrate data from RDS to S3, you can:
–Export Data from RDS: Use the mysqldump command for MySQL or pg_dump for PostgreSQL to export data.
–Store in S3: Upload the exported data files to an S3 bucket.
-AWS DMS: Alternatively, use AWS Database Migration Service (DMS) to automate the migration directly from RDS to S3.
Shubhnoor is a data analyst with a proven track record of translating data insights into actionable marketing strategies. She leverages her expertise in market research and product development, honed through experience across diverse industries and at Hevo Data. Currently pursuing a Master of Management in Artificial Intelligence, Shubhnoor is a dedicated learner who stays at the forefront of data-driven marketing trends. Her data-backed content empowers readers to make informed decisions and achieve real-world results.
