As businesses generate massive amounts of data, decision-making has become more reliable. However, there’s the constant challenge of managing and analyzing the data to gain valuable insights. To handle a colossal amount of data, organizations often use cloud storage services like Amazon S3 and Redshift. Both storage systems play a crucial role in supporting your storage and analytics needs. But, the ability to move data between S3 and Redshift can define the efficiency of your entire analytics workflow. So, let’s understand the two AWS services and the ways to establish a streamlined connection for better data movement.
What is Amazon S3?
Amazon S3 (Simple Storage Service) is a fully-managed cloud-based object storage service provided by Amazon Web Services (AWS). It offers a scalable platform for storing and retrieving unstructured, semi-structured, and structured data.
The data in Amazon S3 is stored as objects within buckets. Objects are the files and associated metadata that are stored in the containers called buckets. To store data in Amazon S3, you must first create a bucket, then specify a bucket name and AWS Region. After creating a bucket, you can upload your data to the bucket as objects. Each object in the bucket has a unique identifier—a key or key name.
By default, S3 buckets and objects are private, so you’ll have access only to the S3 resources you create. To grant permissions or manage access to your Amazon S3 resources, you can use bucket policies, S3 Access Points, Access Control Lists (ACLs), and AWS Identity and Access Management (IAM) policies.
Here are some key features of Amazon S3:
- Integration with other AWS Services: You can seamlessly integrate Amazon S3 with other AWS services, like Amazon Redshift, AWS Athena, and AWS Glue. This enables you to build powerful and scalable applications.
- Security: Amazon S3 automatically encrypts all objects uploaded to the buckets. It supports server-side encryption like Amazon S3-managed keys (SSE S3), AWS Key Management Service (KMS) keys (SSE-KMS), and customer-provided keys (SSE-C).
- Scalability: Amazon S3 is built to handle virtually unlimited storage requirements. However, individual objects can range in size from 0 bytes up to 5 TB.
- Versioning: S3 supports versioning, enabling you to maintain multiple object versions in a bucket. This is particularly useful to restore objects upon accidental deletion or overwriting.
What is Redshift?
Amazon Redshift is a fully-managed, petabyte-scale cloud data warehouse solution by Amazon Web Services (AWS). You can use Redshift to store and query large volumes of analytical data efficiently.
An Amazon Redshift warehouse is a collection of computing resources called nodes, which are organized into a group called a cluster. Each cluster has a leader node and one or more compute nodes. The leader node receives queries from client applications, parses them, and develops query execution plans. Then, it coordinates the parallel execution of the plans with the compute nodes and aggregates the intermediate results. Compute nodes run query execution plans and transmit the data among themselves to serve these queries. The results of the queries are then returned to the client applications.
Here are some essential features of Amazon Redshift that you’ll find useful:
- Columnar Storage: The data in Redshift is stored in a columnar format. When compared to traditional row-based databases, a columnar format allows for faster query processing.
- Massive Parallel Processing (MPP) technology: Redshift uses MPP technology to distribute and process data across multiple nodes in a cluster.
- Scalability: Redshift allows you to scale your data warehousing needs without worrying about hardware or infrastructure management.
- Integration with Other AWS Services: You can easily integrate Redshift with other AWS tools like AWS Glue, Amazon Kinesis, and Amazon S3. This helps load data from different sources into Redshift for real-time or batch data processing, enabling you to obtain valuable insights.
- Data Encryption: Redshift uses encryption to secure your data at rest or in transit. For protecting data in transit, Redshift uses SSL or client-side encryption. And for data at rest, Amazon Redshift uses server-side encryption or client-side encryption.
Methods to Load Data from S3 to Redshift
You can load data from S3 to Redshift by using one of the following methods:
- Method #1: Using the
- Method #2: Using AWS Glue
- Method #3: Using SaaS alternatives like Estuary
Method #1: Using COPY Command to Load Data from S3 to Redshift
You can use the Redshift
COPY command to load your S3 data to Redshift. It supports data formats like CSV, JSON, and Parquet.
Before you get started with the data-loading process, you must create an Amazon S3 bucket, a Redshift cluster, and a schema to load the data. You can use AWS CLI or the web console to upload data files into an S3 bucket. Then, run the
COPY command to copy data from one or multiple S3 buckets to Redshift tables. You can execute the
COPY command using the Redshift CLI, API calls, or through the AWS Management Console.
This is the basic
COPY command syntax:
plaintextCOPY table_name [column_list] FROM data_source CREDENTIALS access_credentials [options]
table_nameis the target table and must already exist in Redshift.
column_listis optionally used to mention the specific columns of the
table_nameyou’d like to map the incoming data.
data_sourceincludes the path to the data file.
- AWS access
credentialsare required for authorization.
After you’ve run the
COPY command, your S3 data will be successfully loaded to Amazon Redshift.
Note: You can also use the UNLOAD command to store Redshift query results in AWS S3. It is, in a way, opposite to the COPY command. To know how to perform a Redshift unload to S3, you can read this article.
Method #2: Using AWS Glue to Load S3 Data to Redshift
AWS Glue is a serverless, fully-managed data integration platform that simplifies the movement of data between Amazon services. You can use AWS Glue to connect to more than 70 diverse data sources, including S3. You can also create ETL jobs that extract data from S3, transform it, and load the transformed data into Redshift.
AWS Glue also uses COPY commands to copy data to Redshift. Before you use AWS Glue, ensure you have an Amazon S3 bucket containing the data you want to load to Redshift. You must create an IAM role by adding the resource in the
IAMGlueServiceRole (IAMRole) and give it access to S3. After configuring the IAMRole, create a new crawler and provide the S3 bucket path. Attach the IAMRole to the target database, then set the output and scheduling. Create and run the crawler in AWS Glue Service. This will load your S3 data into Redshift.
Method #3: Using SaaS Alternatives Like Estuary to Copy from S3 to Redshift
COPY command and AWS Glue help load your S3 data into Redshift, there are some potential drawbacks associated with them. AWS Glue is batch-oriented, and you can schedule your data transfers at a minimum of 5-minute intervals when loading data into Redshift. If real-time data loading is crucial for your use case, these may not be the best choice. On the other hand, the
COPY command only supports JSON, AVRO, and CSV data sources for data transfers.
To overcome the challenges associated with the other two methods, you can use third-party ETL tools like Estuary Flow. Flow offers integration capabilities for real-time data transfer. By using Flow’s built-in connectors, you can also connect to a wide range of data sources without writing a single line of code.
Here’s how you can go about using Estuary Flow to load data from S3 to Redshift:
Prerequisite: Set up Redshift to connect with Flow. Learn how here.
Step 2: Once you’ve logged in, locate the Captures option on the left side of the Estuary dashboard. On the Captures page, click on the New Capture button. This step is to set up the source end of the data pipeline.
Search for S3 in Source Connectors.
Then, click on the Capture button. You’ll be directed to the S3 Capture connector page.
Fill in all the required fields, then click on Next. Then click on Save and Publish. This will capture your data in S3 into a Flow collection.
Step 3: The next step is to set up the destination end of the pipeline. In this case, it’s Amazon Redshift. Click on Materializations on the left side of the Estuary dashboard. On the Materializations page, click on New Materialization.
Search for Amazon Redshift in Search Connectors, then click on the Materialization button.
You’ll be redirected to the Redshift connector page, where you must fill in the required fields like Address, database user name, and Password. You will also have to provide the details of Amazon S3 buckets, along with the access and secret access key for read/write permission.
Once you’ve filled in the required information, click on Next, then click on Save and Publish.
This will set up the automated data transfer process from S3 to Redshift. For more information on the process, here’s all the documentation to help out:
- How to Set Up a Data Pipeline like this one
- The Amazon S3 Source Connector
- The Amazon Redshift Materialization Connector
A copy from S3 to Redshift can be achieved using the COPY command or AWS Glue. However, there’s extensive manual effort involved in these methods, and you’re required to have an understanding of how associated Amazon AWS services work. If you are looking for a simple and reliable data migration solution that can transfer data in real-time, you can use Estuary Flow. With its extensive and ever-growing connectors to different databases, you can build robust real-time data pipelines quickly.
Estuary Flow supports seamless integration for efficient data transfers between a range of source and destination data platforms. Get started with Estuary Flow today; your first pipeline is free!