Amazon Redshift

 

Introduction

Amazon Redshift is one of the most widely-used services in the AWS ecosystem, and is a familiar component in many cloud architectures. In this article, we’ll cover the key facts you need to know about this cloud data warehouse, and the use cases it is best suited for. We’ll also discuss the limitations and scenarios where you might want to consider alternatives.

What is Amazon Redshift?

Amazon Redshift is a fully managed cloud data warehouse offered by AWS. First introduced in 2012. Today Redshift is used by thousands of customers, typically for workloads ranging from hundreds of gigabytes to petabytes of data.

Redshift is based on PostgreSQL 8.0.2 and supports standard SQL for database operations. Under the hood, various optimizations are implemented to provide fast performance even at larger data scales,. This includes massively parallel processing (MPP) and read-optimized columnar storage.

What is a Redshift Cluster?

A Redshift cluster represents a group of nodes provisioned as resources for a specific data warehouse. Each cluster consists of a leader and compute nodes. When a query is executed, Redshift’s MPP design means it distributes the processing power needed to return the results of an SQL query between the available nodes. It does this automatically.

Determining cluster size depends on the amount of data stored in your database. This also depends on the number of queries being executed, and the desired performance. 

Scaling and managing clusters can be done through the Redshift console, the AWS CLI, or programmatically through the Redshift Query API.

What Makes Redshift Unique?

When Redshift was first launched, it represented a true paradigm shift from traditional data warehouses provided by the likes of Oracle and Teradata. As a fully managed service, Redshift allowed development teams to shift their focus away from infrastructure and toward core application development. The ability to add compute resources automatically with just a few clicks or lines of code, rather than having to set up and configure hardware, was revolutionary and allowed for much faster application development cycles.

Today, many modern cloud data warehouses offer similar linear scaling and infrastructure-as-a-service functionality. A few notable products including Snowflake and Google BigQuery. However, Redshift remains a very popular choice and is tightly integrated with other services in the AWS cloud ecosystem.

Amazon continues to improve Redshift, and in recent years has introduced federated query capabilities, serverless, and AQUA (hardware accelerated cache).

Redshift Use Cases

Redshift’s Postgres roots mean it is optimized for online analytical processing (OLAP) and business intelligence (BI) – typically executing complex SQL queries on large volumes of data rather than transactional processing which focuses on efficiently retrieving and manipulating a single row.

Some common use cases for Redshift include:

• Enterprise data warehouse: Even smaller organizations often work with data from multiple sources such as advertising, CRM, and customer support. Redshift can be used as a centralized repository that stores data from different sources in a unified schema and structure to create a single source of truth. This can then feed enterprise-wide reporting and analytics.
• BI and analytics: Redshift’s fast query execution against terabyte-scale data makes it an excellent choice for business intelligence use cases. Redshift is often used as the underlying database for BI tools such as Tableau (which otherwise might struggle to perform when querying or joining larger datasets).
• Embedded analytics and analytics as a service: Some organizations might choose to monetize the data they collect by exposing it to customers. Redshift’s data sharing, search, and aggregation capabilities make it viable for these scenarios, as it allows exposing only relevant subsets of data per customer while ensuring other databases, tables, or rows remain secure and private.
• Production workloads: Redshift’s performance is consistent and predictable, as long as the cluster is adequately-resourced. This makes it a popular choice for data-driven applications, which might use data for reporting or perform calculations on it.
• Change data capture and database migration: AWS Database Migration Service (DMS) can be used to replicate changes in an operational data store into Amazon Redshift. This is typically done to provide more flexible analytical capabilities, or when migrating from legacy data warehouses.

Redshift Challenges and Limitations 

While Amazon Redshift is a powerful and versatile data warehouse, it still suffers from the limitations of any relational database, including:

• Costs: Because storage and compute are coupled, Redshift costs can quickly grow very high. This is especially noted when working with larger datasets, or with streaming sources such as application logs.
• Complex data ingestion: Unlike Amazon S3, Redshift does not support unstructured object storage. Data needs to be stored in tables with predefined schemas. This can often require complex ETL or ELT processes to be performed when data is written to Redshift. 
• Access to historical data: Due to the above limiting factors, most organizations choose to store only a subset of raw data in Redshift, or limit the number of historical versions of the data that they retain. 
• Vendor lock-in: Migrating data between relational databases is always a challenge due to the rigid schema and file formats used by each vendor. This can create significant vendor lock-in and make it difficult to use other tools to analyze or access data.

Due to these limitations, Redshift is often a less than ideal solution for use cases that require diverse access to very large volumes of data, such as exploratory data science and machine learning. In these cases, many organizatio

Amazon Redshift

 Amazon Redshift is a fully managed data warehouse service in the cloud. Its datasets range from 100s of gigabytes to a petabyte. The initial process to create a data warehouse is to launch a set of compute resources called nodes, which are organized into groups called cluster. After that you can process your queries.

How to Set Up Amazon Redshift?

Following are the steps to set up Amazon Redshift.

Step 1 − Sign in and launch a Redshift Cluster using the following steps.

• Sign in to AWS Management console and use the following link to open Amazon Redshift console − https://console.aws.amazon.com/redshift/

• Select the region where the cluster is to be created using the Region menu on the top right side corner of the screen.

• Click the Launch Cluster button.

• The Cluster Details page opens. Provide the required details and click the Continue button till the review page.

• A confirmation page opens. Click the Close button to finish so that cluster is visible in the Clusters list.

• Select the cluster in the list and review the Cluster Status information. The page will show Cluster status.

Step 2 − Configure security group to authorize client connections to the cluster. The authorizing access to Redshift depends on whether the client authorizes an EC2 instance or not.

Follow these steps to security group on EC2-VPC platform.

• Open Amazon Redshift Console and click Clusters on the navigation pane.

• Select the desired Cluster. Its Configuration tab opens.

• Click the Security group.

• Once the Security group page opens, click the Inbound tab.

• Click the Edit button. Set the fields as shown below and click the Save button.

  • Type − Custom TCP Rule.

  • Protocol − TCP.

  • Port Range − Type the same port number used while launching the cluster. By-default port for Amazon Redshift is 5439.

  • Source − Select Custom IP, then type 0.0.0.0/0.

Step 3 − Connect to Redshift Cluster.

There are two ways to connect to Redshift Cluster − Directly or via SSL.

Following are the steps to connect directly.

• Connect the cluster by using a SQL client tool. It supports SQL client tools that are compatible with PostgreSQL JDBC or ODBC drivers.

  Use the following links to download − JDBC https://jdbc.postgresql.org/download/postgresql-8.4-703.jdbc4.jar

  ODBC https://ftp.postgresql.org/pub/odbc/versions/msi/psqlodbc_08_04_0200.zip or http://ftp.postgresql.org/pub/odbc/versions/msi/psqlodbc_09_00_0101x64.zip for 64 bit machines

• Use the following steps to get the Connection String.

  • Open Amazon Redshift Console and select Cluster in the Navigation pane.

  • Select the cluster of choice and click the Configuration tab.

  • A page opens as shown in the following screenshot with JDBC URL under Cluster Database Properties. Copy the URL.

• Use the following steps to connect the Cluster with SQL Workbench/J.

  • Open SQL Workbench/J.

  • Select the File and click the Connect window.

  • Select Create a new connection profile and fill the required details like name, etc.

  • Click Manage Drivers and Manage Drivers dialog box opens.

  • Click the Create a new entry button and fill the required details.

• Click the folder icon and navigate to the driver location. Finally, click the Open button.

• Leave the Classname box and Sample URL box blank. Click OK.

• Choose the Driver from the list.

• In the URL field, paste the JDBC URL copied.

• Enter the username and password to their respective fields.

• Select the Autocommit box and click Save profile list.

Features of Amazon Redshift

Following are the features of Amazon Redshift −

• Supports VPC − The users can launch Redshift within VPC and control access to the cluster through the virtual networking environment.

• Encryption − Data stored in Redshift can be encrypted and configured while creating tables in Redshift.

• SSL − SSL encryption is used to encrypt connections between clients and Redshift.

• Scalable − With a few simple clicks, the number of nodes can be easily scaled in your Redshift data warehouse as per requirement. It also allows to scale over storage capacity without any loss in performance.

• Cost-effective − Amazon Redshift is a cost-effective alternative to traditional data warehousing practices. There are no up-front costs, no long-term commitments and on-demand pricing structure.

What is AWS Snowball?

 

What is AWS Snowball?

It is a facility that provides secure, robust, and sturdy devices so one can bring AWS computing and storage ability to their edge environments and transfer data to and from AWS. These devices are generally known as AWS Snowball devices or AWS Snowball Edge devices.

What is an Amazon Snowball Device?

A Snowball device is a petabyte-scale data transfer solution that utilizes reliable appliances to transport large quantities of data to and from the AWS cloud. Using Snowball tackles general challenges that are faced when dealing with large-scale data transfers including extreme network expenses, long transfer duration, and security issues.

Key Features of AWS Snowball

• Fast data transfer: It is possible as the encryption is performed on the device itself, which, in turn, facilitates a high data throughput rate and shorter data transmission duration.
• Edge computing workloads: AWS OpsHub is a user interface for simple management and monitoring, enabling the users to efficiently deploy edge computing workloads.
• Clustering: Clustering enables the creation of a local storage tier, which has increased durability for better performance.
• Encryption: Encryption is done automatically on all data transferred to Amazon Snowball with 256-bit encryption keys.
• Portability: Rugged and portable case of Snowball facilitates durability.
• End-to-end tracking: All locations that are covered by the Snowball device are specified as they are being delivered to the desired location.
• Secure: Once the job of specific data transmission is completed, AWS ensures that secure data erasure is performed on Snowball.

Before starting to use Snowball, one needs to place an order for the same on AWS. Once it is received, one can follow these steps to ensure successful usage of the device.

Step 1: Connect the Snowball Device to the local network

• Connect the turned off Snowball to the local network
• The power cable is to be attached to the back of the device and then to be plugged into a reliable power supply
• Press the power button and wait till the e-link display says ready
• After the device is ready, the following will be displayed on the screen

• The device is now ready to transfer data.

Step 2: Data Transfer

• The first step in transferring data using a Snowball is getting the credentials
• After the credentials are received, one needs to download and install the Snowball client tool
• The next step is to run and authenticate the access to the device
  • For authentication, open the command prompt and type in the following command:

snowball start -i [Snowball IP Address] -m [Path/to/manifest/file] -u [29 character unlock code]

• After authentication, a confirmation message is received and now one can begin the transfer of data from the Snowball.

Want to learn more about AWS? Check out our AWS Tutorial now!

Step 3: Disconnect the Snowball

• Once all data transfer with the device has ended, one can turn it off by pressing the power button located above the LCD display. It takes about 20 seconds for the device to shut down.

Benefits of AWS Snowball

The following are the benefits of Snowball:

• Scalable: The data that the client wants to transfer is transported to cloud storage or specifically Amazon S3. Snowball, with the help of multiple devices, enables a very speedy transfer of terabytes of data in an efficient manner.
• Security: At AWS, security is of utmost importance. It is considered a shared responsibility between the consumer and AWS.

The shared responsibility model comprises the security of the cloud and security in the cloud.

• Security of the cloud: The infrastructure that runs services of the Amazon cloud is protected by AWS. The security of the AWS cloud is checked on a regular basis by third parties so as to maintain its effectiveness.

• Security in the cloud: This is the part where the clients’ responsibilities are determined by AWS. Data sensitivity, the consumers’ company requirements, and applicable regulations are the responsibilities of the clients.
• High speed: Due to huge traffic over the web these days, it takes approximately a month to transfer huge amounts of data, which can be done in a week’s time when using this.
• Flexible data recovery: Amazon Snowball tends to ease the tedious job of data transmission from the clients’ own premises.
• Simple and compatible: Amazon Snowball has a lucid working; the management consoles work on providing a better experience to the clients while surfing AWS.
• Economical: As the number of internet users is perpetually increasing, the time it takes to transfer any data across the network is tiringly copious and, hence, very expensive. This is where Snowball comes in, as it is fast and inexpensive as compared to the internet.

Use Cases of Snowball

Few use cases are mentioned below:

• Cloud data migration: The migration of digital assets to the cloud is done efficiently with the help of Snowball.
• Content distribution: It eases the process of distributing the data as it can be directly sent to a client’s or customer’s location.
• Tactical edge computing: Organizations that take responsibility for the safety of the public, continuously require data collection from sensors, drones, and cameras. It provides security, flexible storage, and powerful computing, which is suitable for such organizations.

What is Snowball Edge?

This facilitates the technology as it provides an increase in the local computational power and it automatically encrypts data on the device without the need for Snowball client download. As compared to AWS Snowball, AWS Snowball Edge provides faster and comparatively reliable services.

Features of Amazon RDS

 Amazon RDS (Relational Database Service) is a fully-managed SQL database cloud service that allows to create and operate relational databases. Using RDS you can access your files and database anywhere in a cost-effective and highly scalable way.

Features of Amazon RDS

Amazon RDS has the following features −

• Scalable − Amazon RDS allows to scale the relational database by using AWS Management Console or RDS-specific API. We can increase or decrease your RDS requirements within minutes.

• Host replacement − Sometimes these situations occur when the hardware of Amazon RDS fails. There is no need to worry, it will be automatically replaced by Amazon.

• Inexpensive − Using Amazon RDS, we pay only for the resources we consume. There is no up-front and long-term commitment.

• Secure − Amazon RDS provides complete control over the network to access their database and their associated services.

• Automatic backups − Amazon RDS backs up everything in the database including transaction logs up to last five minutes and also manages automatic backup timings.

• Software patching − Automatically gets all the latest patches for the database software. We can also specify when the software should be patched using DB Engine Version Management.

How to Set Up Amazon RDS?

Step 1 − Login to AWS management console. Use the following link to open Amazon RDS console − https://console.aws.amazon.com/rds/

Step 2 − Select the region where the DB instance is to be created, at the top right corner of the Amazon RDS console.

Step 3 − Select Instances in the navigation pane, then click Launch DB Instance button.

Step 4 − The Launch DB Instance Wizard opens. Select the type of instance as required to launch and click the Select button.

Step 5 − On the Specify DB Details page, provide the required details and click the Continue button.

Step 6 − On the Additional configuration page, provide the additional information required to launch the MySQL DB instance and click the Continue button.

Step 7 − On Management options page, make the choices and click the Continue button.

Step 8 − On the Review page, verify the details and click the Launch DB Instance button.

Now DB instance shows in the list of DB instances.

How to Connect Database to MySQL DB Instance?

Following are the steps to connect a database on MySQL DB instance −

Step 1 − Type the following command in the command prompt on a client computer to connect a database on MySQL DB instance (using the MySQL monitor).

Step 2 − Replace <myDBI> with DNS name of your DB instance, <myusername> with your master user-name & <mypassword> with your master password.

PROMPT> mysql -h <myDBI> -P 3306 -u <myusername> -p

After running the above command, the output looks like −

Welcome to the MySQL monitor.  Commands end with ; or \g. 
Your MySQL connection id is 350 
Server version: 5.2.33-log MySQL Community Server (GPL)
Type 'help;' or '\h' for help. Type '\c' to clear the buffer.  
mysql>

How to Delete a DB Instance?

After completing the task, we should delete the DB instance so will not be charged for it. Follow these steps to delete a DB instance −

Step 1 − Sign in to the AWS Management Console and use the following link to open the Amazon RDS console.

https://console.aws.amazon.com/rds/

Step 2 − In the DB Instances list, select the DB instances to be deleted.

Step 3 − Click the Instance Actions button and then select the Delete option from the dropdown menu.

Step 4 − Select No in the Create Final Snapshot.

Step 5 − Click the Yes, Delete to delete the DB instance.

Cost of Amazon RDS

When using Amazon RDS, pay only for only the usage without any minimum and setup charges. Billing is based on the following criteria −

• Instance class − Pricing is based on the class of the DB instance consumed.

• Running time − Price is calculated by the instance-hour, which is equivalent to a single instance running per hour.

• Storage − Bill is calculated as per the storage capacity plan chosen in terms of per GB.

• I/O requests per month − Billing structure also includes total number of storage I/O requests made in a billing cycle.

• Backup storage − There is no additional charges for backup storage up to 100% of database. This service is free only for active DB instances.

What is Amazon RDS

What is Amazon RDS?

Amazon RDS is AWS's relational database service. It's a fully managed relational database. There are many different types of databases. Very broadly speaking, database engines are either relational or non-relational. Of course, there are other types of databases, but they tend to be used for very specific use cases. Relational and non-relational databases are more commonly used for applications.
An example of a relational database would be MySQL, Postgres, or MariaDB. The most popular non-relational database is Mongo followed by Redis, though Redis is typically used for cache and not long-term data storage.

What is AWS RDS?

• Amazon RDS is a Relational Database Cloud Service
• Amazon RDS minimizes relational database management by automation
• Amazon RDS creates multiple instances for high availability and failovers
• Amazon RDS supports PostgreSQL, MySQL, Maria DB, Oracle, SQL Server, and Amazon Aurora

---

Relational Databases

• Amazon RDS stores data as tables, records, and fields
• Values from one table can have a relationship to values in other tables. Relationships are a key part of relational databases.
• Relational databases are often used for storing transactional and analytical data
• Relational databases provide stability and reliability for transactional databases

---

RDS Pricing - Pay as You Go

Amazon RDS is pay as you go. It is comprised of 3 parts:

1. Hosting. You can choose from different types of hosting depending on your need
2. Storage and Operations. Storage is billed per gigabyte per month, and I/O is billed per million-request
3. Data transferred

 

What exactly is Amazon S3?

Amazon S3, also known as Amazon Simple Storage Service, has been used by enterprises worldwide for a long time. It is recognized as one of AWS’s most widely used cloud storage offerings. It offers characteristics that allow you to store and retrieve an unlimited quantity of data without time constraints or limitations. 

With S3, there are no geographical limitations to data retrieval or upload. However, the pricing model is determined by how frequently it is retrieved. Amazon Simple Storage Service is an entirely redundant data storage system that allows you to store and recover any quantity of data from anywhere on the internet.

Amazon S3 is a cloud-based object storage solution that is simple to use. S3 provides industry-leading scalability, availability, access speed, and data security. In various circumstances, S3 can be utilized to store practically any quantity of data. Static websites, mobile applications, backup and recovery, archiving, corporate applications, IoT device-generated data, application log files, and extensive data analysis are all common uses for the storage service. Amazon S3 also has simple management tools. These tools, which you may access via the online console, command line, or API, let you arrange data and fine-tune access controls to meet project or regulatory requirements.

Amazon S3 organizes data into logical buckets, making it convenient and straightforward for users to find what they’re looking for. S3 also has an object storage facility for files, data, and metadata. But, again, its motive is to make it simple for individuals to locate data or files when they need them.

 

What exactly is the Amazon Glacier?

If you’re searching for a cost-effective way to back up your most static data, Amazon Glacier is the way to go. It’s often used for data backup and archiving. Customers should expect to pay around $0.004 per GB per month to retain their critical data for the long term.

The most incredible thing about Amazon Glacier is that it is a managed service, so you don’t have to worry about monitoring or maintaining your data. Amazon Glacier’s key selling point is that it can store data that isn’t accessed regularly for a long time. 

When opposed to S3, Amazon Glacier’s use cases are far more focused. As a result, it is a more robust solution for firms looking to protect sensitive and inactive data. With Amazon Glacier, you may store your source data, log files, or business backup data.

The only objective of Amazon Glacier’s development is to manage long-term data storage. Hence, it’s not designed for frequent retrievals. As a result, the retrieval speed with Glacier may be slow. But then the low-cost feature of Amazon Glacier compared to S3 draws the main business. Amazon Glacier is optimized for data that is retrieved infrequently and for which retrieval durations of several hours are acceptable to keep costs low. As a result, with Amazon Glacier, significant savings over on-premises options, customers can store considerable or minor amounts of data for as little as $0.01 per gigabyte per month.

Amazon Glacier is a low-cost storage service that offers secure and long-term data backup and archiving and is optimized for data that is retrieved infrequently and for which retrieval durations of several hours are acceptable to keep costs low.

 

Let’s explore in detail the features of Amazon Glacier–

• Archives: As archives, you save data in Amazon Glacier. You can use an archive to represent a single file or bundle many files to upload as a single archive. To get archives from Amazon Glacier, you must first start a job. In most cases, jobs are completed in 3 to 5 hours. After that, your archives are stored in vaults.

• Security: Amazon Glacier uses Secure Sockets Layer (SSL) to encrypt data in transit and automatically saves data encrypted at rest using Advanced Encryption Technology (AES) 256, a secure symmetric-key encryption standard with 256-bit encryption keys.

 

Let’s dive into more detail to study the features of Amazon S3–

• Bucket criteria: Objects containing 1 byte to 5 terabytes of data can be written, read, and deleted. You can store an unlimited number of things. Each object is saved in a bucket and accessed using a unique key supplied by the developer.
  A bucket can be kept in any of the available regions. You can select an area to reduce latency, lower expenses, or meet regulatory criteria.
  
• Scalability: Using Amazon S3, you won’t have to worry about storage issues. Instead, we can save as much information as possible and access it whenever we want.

• Low-cost and simple to use: Amazon S3 allows users to store vast data for very little money.

• Security: Amazon S3 allows data to be transferred via SSL, and the data is automatically encrypted once it is uploaded. Additionally, by defining bucket policies using AWS IAM, the user has complete control over their data.

• Enhanced Performance: Amazon S3 is connected with Amazon CloudFront, which distributes material to end users with minimal latency and high data transfer speeds without any minimum usage commitments.

  
  

• Integration with AWS services: Amazon S3 is connected with Amazon CloudFront, Amazon CloudWatch, Amazon Kinesis, Amazon RDS, Amazon Route 53, Amazon VPC, AWS Lambda, Amazon EBS, Amazon DynamoDB, and other AWS services.

Transition from S3 to S3 Glacier


Let’s have a look at when this transition is appropriate:

• When a large amount of data is accumulated but immediate access to it is not necessary.
• When it comes to archiving.
• When putting together a backup plan.
• S3 Glacier’s budget is significantly reduced when dealing with big amounts of data.

Expedited, Standard, and Bulk Retrieval are the three archive extraction modes (also known as retrieval tiers) available in Amazon S3 Glacier to satisfy varying access time and cost needs.

• In 1–5 minutes, you can have your archives ready.
• Standard extraction, which produces archives in 3-5 hours.
• Batch retrieval costs $0.0025 per GB and allows for cost-effective access to massive amounts of data (up to a few petabytes).
• The cost of retrieving data varies.

What are the steps to moving to Amazon S3 Glacier?

• Decide how much data you’ll be working with.
• Decide how frequently you’ll need to access data from the backup.
• Determine how much time you’ll have to wait for your backup.
• Consider whether you need to use the API to obtain data.

You can choose if you should transform from normal S3 to Amazon S3 Glacier based on this information, as well as which technological aspects will be crucial for your job.

Battle of Amazon S3 Vs Glacier

 

• S3 is mainly used for frequent data access, whereas Amazon Glacier is primarily utilized for long-term data storage.
• Amazon Glacier does not support hosting static online content, whereas S3 does.
• The data is saved in the logical buckets on S3. However, Amazon Glacier stores data in the form of archives and vaults.
• Object migrating from one storage class to another is possible with S3. On the other hand, the Glacier items will only be moved to the Deep Archive storage type.
• When compared to Amazon Glacier, Amazon S3 is more expensive. The many retrieval options included inside these storage technologies account for this disparity.
• The minimum storage day with S3 is 30 days, while the minimum storage day with Glacier is 90 days.
• Setting up Amazon Glacier is simple; however, S3 is more complicated.
• Glacier makes it faster and easier to create and organize archives or vaults, whereas S3 takes time to develop folders or buckets properly.

Similarities between Amazon Glacier And S3 

 

• Both Amazon Glacier and Amazon S3 are expected to provide 99.999999999 per cent object durability across multiple availability zones.
• Both S3 and Amazon Glacier have a high availability rate.
• Both Glacier and S3 have no theoretical limit on the amount of data you may store.
• Both Glacier and S3 allow for direct uploading of things.
• SLAs are provided for both Glacier and S3.

 

Conclusion

Amazon S3 is a web-based cloud storage service designed for online backup and archival of data and applications on Amazon Web Services (AWS). Disaster recovery, application hosting, and website hosting are all possible with Amazon S3. Amazon S3 Glacier offers long-term storage for any data format. Data can be accessed in three to five hours on average. A developer may utilize Amazon Glacier in conjunction with storage lifecycle management to move rarely used data to cold storage to save money.

The most significant distinction between the two Amazon storage services is that S3 is meant for real-time data retrieval, whilst Amazon Glacier is utilized for archival. Therefore, S3 Glacier should only be used for low-cost storage scenarios when data isn’t needed right away. On the other hand, S3 is recommended for organizations that require frequent and quick access to their data.

These are a handful of the explanatory qualities that illustrate how AWS Glacier and S3 differ and how they are similar. As a result, select the appropriate AWS storage solution to match your data storage and retrieval requirements. 

At Encaptechno, we design AWS certified solutions to help you plan and implement an Amazon Web Services (AWS) migration strategy to improve your applications. Our team at Encaptechno has the expertise to plan a seamless migration of all aspects of your computing, application, and storage operations from your current infrastructure to the AWS Cloud. Reach out to us today. We would be glad to hear from you about your project goals and discuss how we can help!

• Inexpensive cost: Amazon Glacier is a pay-per-gigabyte-per-month storage solution as low as $0.01 per gigabyte per month.