Azure Redshift Comparison: 4 Critical Differences
Cloud technology has been pivotal in changing the business landscape worldwide. Today, companies no longer have to worry about data storage resources as they can get these services on-demand from cloud service providers at a fraction of the price. With the cloud, businesses can now store valuable data regarding their customers, products, and employees and use this information to inform key decisions.
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So big has Cloud technology become in recent years that numerous related fields have sprung up. One of these is Data Warehousing. In simple terms, a Data Warehouse is a system tasked with storing organizational data from the database as well as external sources. Some of the widely used Data Warehouses are Amazon Redshift, Azure, Google BigQuery, Snowflake, etc. Many times companies get confused between Azure Redshift or BigQuery Redshift, etc to decide which suits them best.
Such systems can store historical information allowing the parent company to source the data and use it to influence important decisions through Analytics. Over the years, numerous companies have turned to Data Warehousing solutions due to the numerous benefits they stand to gain. Global tech companies have responded to the rise in demand by designing and launching different Data Warehousing platforms all available to users.
In this article, you will get a comprehensive introduction to some of the biggest data warehousing platforms available to users today, namely Amazon Redshift and Azure Synapse. By the end, you should have a key understanding of the difference between Azure Redshift.
Table of Contents
- Introduction to Amazon Redshift
- Introduction to Azure Synapse
- Key Differences Between AWS Redshift and Azure Synapse
Introduction to Amazon Redshift
Amazon Redshift is a popular Data Warehousing solution capable of handling data on an exabytes scale. Over the years, it has become synonymous with successful companies all over the world due to the numerous benefits it offers.
It is worth noting that AWS Redshift uses Massively Parallel Processing Technology (MPT). This enables it to perform various operations on large data volumes at lightning speed. To put this into perspective, AWS Redshift can work with data on the exabytes scale usually denoted by 1018. That’s pretty impressive!
Data hosted on Amazon Redshift is always encrypted, providing an extra layer of security for users. It can be deployed with just a few clicks and offers tonnes of features to enable users to import data easily.
Key Features of Amazon Redshift
Below are some of the key features that have enabled Amazon Redshift to stand apart from the pack:
- Automation capabilities– With AWS Redshift, you do not have to perform repetitive tasks such as generating daily, weekly, or monthly reports as the platform has automation capabilities.
- Intelligent Optimization– When querying large data sets, there are several ways you can query information using the same parameters. Amazon Redshift helps in such situations by providing tools and data to improve queries. The software will also offer tips to enhance the Database automatically.
- SQLFriendly– Amazon Redshift is based on PostgreSQL, meaning all SQL queries can work on the platform.
- Data Encryption– This is an extra security feature part of the Amazon Redshift operation. The user can decide which data needs encryption and which does not.
To know more about Amazon Redshift, click here.
Introduction to Azure Synapse
Azure Synapse is a Data Warehousing tool. It gives users a platform to build a modern Data Warehouse system with an infinite resource that encompasses data warehousing and Big Data Analytics. Using the service, clients can query data on their terms, either using services on-demand or provisioned scalable resources.
Accordingly, Azure Synapse has four main components.
- SQL Pool and SQL On-demand- Applicable in enterprise data warehousing.
- Apache Spark- Used for Big Data
- Synapse Pipelines- Used for data integration, ETL, and ELT.
- Synapse Studio
All these aspects are unified into a user-friendly interface that offers an unmatched experience for users.
Key Features of Azure Synapse
Below are some of the key features of Azure Synapse
- Centralized Data Management Capability– Massively Parallel Processing (MPT) powers Azure Synapse enabling it to process astonishingly high workloads in a split second.
- HTAP Implementation– This technology allows for real-time integration between Azure Synapse and Azure Databases in your system.
- Machine Learning Integration– With Azure Machine Learning integration capabilities, you can predict and score ML models to generate predictions within your data scope.
- Data Sharing– After integrating with Azure Data Share, employees can share Data Lake and Data Warehouse either internally or externally.
To know more about Azure Synapse, click here.
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Key Differences Between AWS Redshift and Azure Synapse
By now, it is evident that both Amazon Redshift and Azure Synapse platforms offer unmatched Data Warehousing functionalities for companies. Placing them on a pedestal and comparing them can help determine which is the best for your business. The comparison criteria between Azure Redshift are listed below:
- Azure Redshift: Architecture and Pricing
- Azure Redshift: Data Protection
- Azure Redshift: Performance
- Azure Redshift: Administration
1) Azure Redshift: Architecture and Pricing
AWS Redshift and Azure Synapse use different pricing models, albeit both consider computation and storage as critical factors when determining to price for services offered.
In Redshift, Clusters contain nodes that are configurations with CPU, memory, storage, and IOPS resources. With this in mind. Redshift offers three varied nodes types with prices ranging from $0.24 to $13.04 per hour.
Azure has Data Warehouse Units that comprise CPU, memory, and IOPS. It is worth noting that storage is excluded since Azure separates computational and storage resources. DWU prices start at $1.20 to $360 per hour.
2) Azure Redshift: Data Protection
These platforms have varied methods of ensuring user data is protected from accidental deletion.
AWS Redshift automatically takes snapshots that track changes to user data. The system takes the snapshots every hour or after 5GB of node data changes (depending on whichever comes first). However, users are also free to make manual snapshots. Finally, the platform offers free storage for these snapshots in an amount equal to the user’s current subscription.
Azure Synapse takes automatic snapshots throughout the day. However, unlike Redshift, Snapshot counts towards the total billed storage.
3) Azure Redshift: Performance
There is really nothing much to compare here since both these platforms perform well under huge data loads.
4) Azure Redshift: Administration
Both these platforms require a reasonable amount of attention from administrators.
Amazon Redshift Data Warehouse comprises nodes that make up a cluster. Each Cluster runs an AWS Redshift engine with Databases. Administrators can then perform various tasks on these clusters, such as scaling
While Azure services can be set to autoscale, scaling up an Azure Synpase Analytics data warehouse requires administrator attention. Administrators can perform other functions, including data partitioning and other performance optimization techniques.
In this article, you learnt about Azure Synapse, Amazon Redshift, and what are the key differences of Azure Redshift. It is clear that Azure and Amazon Redshift are robust platforms capable of meeting even the tallest data warehousing needs. But which is better between Azure Redshift? This will vary depending on your needs. It is better to test each of them out and see which fulfills your needs. Hopefully, the information above is a good starting point when comparing the two systems.Visit our Website to Explore Hevo
Amazon Redshift and Azure stores data from multiple sources and every source follows a different schema. Instead of manually writing scripts for every source to perform the ETL (Extract Transform Load) process, one can automate the whole process. Hevo Data is a No-code Data pipeline solution that can help you transfer data from 100+ sources to Amazon Redshift or other Data Warehouse of your choice. Its fault-tolerant and user-friendly architecture fully automates the process of loading and transforming data to destination without writing a single line of code.
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