Amazon Redshift vs Oracle ADW: 9 Simple Key Differences
Given that the world of Big Data often involves the world of Data Warehouses and dealing with massive datasets, it should come as no surprise that a modern Cloud Data Warehouse should be easily Scalable, Fault-Tolerant, and Secure.
Table of Contents
This post compares two Cloud Data Warehouse Services — Amazon Redshift vs Oracle ADW. It should also have fast query performance as well as support for a wide range of data formats. Companies are looking to leverage these features along with the flexible pay-as-you-go cloud model to innovate more, pay less, and ensure data safety.
In order to assist Data and Analytics Leaders, Architects, and implementers in evaluating and determining which solutions best meet their business goals.
At the time of writing this post, there are three variants of Redshift; Amazon Redshift Provisioned Clusters, Redshift Spectrum, and Redshift Serverless. This post will compare Redshift’s serverless offering with Oracle’s Autonomous Data Warehouse.
Table of Contents
- What is a Data Warehouse?
- What is Amazon Redshift Serverless?
- What is Oracle Autonomous Data Warehouse (ADW)?
- Amazon Redshift vs Oracle ADW Key Differences
- Amazon Redshift vs Oracle ADW – Pricing
- Amazon Redshift vs Oracle ADW – Architecture
- Amazon Redshift vs Oracle ADW – Performance
- Amazon Redshift vs Oracle ADW – Scalability
- Amazon Redshift vs Oracle ADW – Deployment Options
- Amazon Redshift vs Oracle ADW – Ease of Use
- Amazon Redshift vs Oracle ADW – Flexibility
- Amazon Redshift vs Oracle ADW – Security
- Amazon Redshift vs Oracle ADW – Machine Learning
What is a Data Warehouse?
If you have data and need somewhere to analyze it, need to combine it with data from other applications, and need to safely and securely share your data discoveries with your colleagues, then a Data Warehouse is what your company needs.
A Cloud Data Warehouse can help you get insights as quickly as possible by running Real-Time Analytics on complex data from disparate sources without worrying about managing the underlying infrastructure.
Modern Cloud Data Warehouse platforms provide fast querying capabilities over both structured and semi-structured data using familiar SQL-based clients and BI tools. The fast querying performance is achieved via machine learning, Massive Parallel Query Processing (MPP), Columnar Storage, and High-Performance Disks.
Some common use-cases that are listed for Data Warehouses include:
- Accelerating analytics workloads.
- Unifying your data lake and operational data stores.
- Analyzing global sales data.
- Analyzing ad impressions and clicks.
- Aggregating gaming data.
- Analyzing social trends.
- Consolidating reporting and integrating various applications.
What is Amazon Redshift Serverless?
Amazon Redshift (serverless option) is a Low-Cost, Flexible, distributed MPP database (Massive Parallel Processing) provided as a service. AWS introduced Redshift Serverless in AWS re: Invent 2021, which now means that you don’t have to provision any infrastructure. Redshift Serverless makes it easy to run and scale analytics in seconds without the need to specify the cluster configuration and manage data warehouse infrastructure.
This makes it easier to adopt compared to a regular Amazon Redshift cluster since it has fewer operational responsibilities and there are fewer infrastructure choices to make. Any user—from Data Analysts, Data Scientists, Developers, to Business Professionals—can get actionable insights from data by running simple queries on the data in the Redshift data warehouse.
Companies use Amazon Redshift because it makes it simple and cost-effective to efficiently store and merge large datasets from disparate sources. Amazon Redshift also makes it easy to analyze the data using existing business intelligence tools.
Key Features of Amazon Redshift
Here are some key features of Amazon Redshift:
- Processing in Parallel: Parallel processing is used in conjunction with a distributed design strategy that leverages multiple CPUs to process huge datasets.
- Tolerance for Mistakes: Organizations may rely on the Data Warehouse’s Fault and Error Tolerance to ensure continuous operation when performing mission-critical processes in the Cloud.
- End-to-End Encryption: To ensure users’ privacy and security, all data handled on the Cloud is encrypted. There are numerous methods for distributing keys for encrypted data.
- Maximum Performance with Machine Learning (ML): Amazon Redshift offers robust Machine Learning (ML) capabilities with high throughput and speed.
- SageMaker Help: It allows users to construct and train Amazon SageMaker models for Predictive Analytics utilizing data from their Amazon Redshift Warehouse, making it a must-have for today’s Data Professionals.
What is Oracle Autonomous Data Warehouse (ADW)?
Oracle ADW is a fully Autonomous, Machine-Learning powered, Self-driving, Self-securing, Self-patching, Cloud Data Warehouse that requires zero database administration.
Oracle automates all end-to-end management of the database instance. Oracle ADW uses AI/ML to automatically audit and configures the database for high-performance queries meaning that you can simply load data and run queries with no need to define indexes, create partitions, or consider any details about parallelism and partitioning. Back-ups, patches, upgrades, and auto-scaling are also performed automatically without manual intervention.
At any time, you can use the service console, command-line interface, or the REST API, to stop/start the database instance and to make resource changes to the CPUs or the storage capacity. When you apply resource changes, your Autonomous Database instance will automatically shrink or grow without requiring any downtime or service interruptions.
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- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
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Amazon Redshift vs Oracle ADW Key Differences
Here are some important key differences between Amazon Redshift vs Oracle ADW
- Amazon Redshift vs. Oracle ADW – Pricing
- Amazon Redshift vs. Oracle ADW – Architecture
- Amazon Redshift vs. Oracle ADW – Performance
- Amazon Redshift vs. Oracle ADW – Scalability
- Amazon Redshift vs Oracle ADW – Deployment Options
- Amazon Redshift vs Oracle ADW – Ease of Use
- Amazon Redshift vs Oracle ADW – Flexibility
- Amazon Redshift vs Oracle ADW – Security
- Amazon Redshift vs Oracle ADW – Machine Learning
1) Amazon Redshift vs Oracle ADW – Pricing
The pricing of Amazon Redshift and Oracle ADW are mentioned below:
AWS Redshift Serverless Pricing
Amazon Redshift serverless is billed per second while you are querying or loading data. You don’t get billed when your Data Warehouse is idle. It has an auto stop/start feature that makes it feel genuinely on-demand—no use, then no cost.
When using Amazon Redshift Serverless, you are billed separately for the compute and storage that you use. Compute pricing is based on demand and is billed per second. For storage capacity, you pay hourly per GB. This typically adds up to $0.024 per GB per month.
You can try out Amazon Redshift Serverless with a $500 free trial. You will get the free AWS credits when you first create a database with Amazon Redshift Serverless. These credits cover the costs for computing, storage, and snapshot usage.
Oracle ADW Pricing
Oracle ADW costs are based on dedicated resources (Memory and CPUs). They offer per-second billing and the ability to pause the data warehouse when not in use, for example during weekends. There are many different configurations available to choose from, like a quarter rack, half rack, and full rack and the option to have them fitted with different machine types. So the cost all depends on your configuration.
Verdict – While it’s possible to stop/start your Oracle ADW instance, operating in Redshift’s serverless environment means that you are consuming and paying for resources only when your queries are running. You also don’t have to concern yourself about over or under-provisioning resources meaning that you can repurpose headcount in more strategic areas. This makes Amazon Redshift the more cost-effective solution.
2) Amazon Redshift vs Oracle ADW – Architecture
The Architecture of Amazon Redshift and Oracle ADW are compared below:
Redshift Serverless uses a cloud-native serverless architecture that leverages RA3 instances to separate the scaling relationship between computing and storage. By using RA3 nodes, adding and removing nodes is only typically done when more computing power is needed (CPU/Memory/IO) mainly because for most use cases, workloads will almost always be CPU, memory, or I/O bound before becoming storage bound.
With 48vCPUs and 64TB of storage per node, these instances effectively separate compute from storage. And because ra3.16xlarge clusters must have at least two nodes, the minimum cluster size is a whopping 128TB which makes storage a non-issue.
This eliminates the need to add nodes to the cluster just because disk space is low deciding to add a node vs scale back is much simpler. You can also join data in your RA3 instance with data in S3 as part of your data lake architecture, to independently scale storage and compute.
Oracle ADW Architecture
Oracle ADW runs on the Oracle Exadata infrastructure, and like Redshift can also tune itself, and can scale OCPUs automatically. Exadata is an optimized database server that uses Oracle database software. These machines deliver high levels of I/O and SQL processing performance for online transaction processing (OLTP), data warehousing (DW), a mix of the two, or as a platform for consolidation of several databases, by leveraging intelligent caching, a massively parallel grid architecture using Oracle RAC.
Oracle RAC (Real Application Clusters) is software that enables clustering and high availability in Oracle database environments. This allows the Oracle database instance to run on two or more nodes thus enabling horizontal scalability and high availability while accessing shared storage.
Verdict – Overall, based on our benchmarks, you can bet that 9/10 times you are going to get superior performance and cost savings with Amazon Redshift.
3) Amazon Redshift vs Oracle ADW – Performance
The Performance between Amazon Redshift and Oracle ADW are mentioned below:
You can store data and query at very low latency. Amazon Redshift Serverless automatically provisions just the right amount of capacity for you in seconds to deliver fast performance for even the most demanding and unpredictable workloads.
Redshift’s Advanced Query Accelerator (AQUA) leverages the elasticity of the RA3 instances. It is aimed at workloads that use “near-line” data sitting remotely on Amazon Redshift Managed Storage, hot data in high-performance SSDs while using the Nitro hypervisor and FPGAs to accelerate the processing of cooler data sitting on S3 and operational data stores.
The RA3 nodes have been optimized for fast storage I/O in several ways, including local caching. The nodes also include improved network bandwidth, and a new type of block-level caching that prioritizes frequently-accessed data based on query access patterns at the block level.
Oracle ADW Performance
Oracle ADW runs on Exadata Machines in Oracle’s Cloud Infrastructure. This affords you performance gains that are unreachable on most cloud and on-prem platforms that run Oracle. Generally, Oracle ADW takes far less effort to manage and operate; in large part due to built-in automation that handles many database management tasks autonomously.
For example, it doesn’t require any special consideration about table design, query optimization, or how to perform joins and aggregations. The database instance automatically optimizes itself to ensure the highest level of performance across various use cases.
Verdict – Performance-wise, this is Hobson’s choice. Little separates Oracle ADW from Redshift serverless. However, if you’re comfortable with Oracle and Exadata, and you’re already invested in Oracle products, then you should stay far away from Redshift as it is heavily integrated with AWS products. On the other hand, if you want performance at any scale without having to worry about the underlying infrastructure or planning for resources then Amazon Redshift is a no-brainer.
4) Amazon Redshift vs Oracle ADW – Scalability
The Scalability of Amazon Redshift and Oracle ADW are mentioned below:
Amazon Redshift serverless is easily scalable—the data warehouse can easily scale up or down without manual intervention. Redshift’s elastic resize capabilities can perform modifications within seconds and without downtime.
As your demand evolves with more concurrent users and new workloads, Redshift uses machine learning (ML) techniques to automatically provision adequate compute resources to adapt to the changes thus reducing operational burden.
Oracle ADW Scalability
In Oracle ADW, you can scale both compute and storage independently on demand. The computing resources (CPUs, storage, and IO resources) that you initially defined during setup can be increased or decreased at run-time when demand spikes without any downtime. Autoscaling is completely automated, and no user intervention is required and this provides true capacity-on-demand.
The database can use three times more CPU and IO resources, depending on workload requirements, and up to three times the reserved base storage, depending on storage requirements. For example, if your reserved base storage is 1 TB, Oracle ADW will initially allocate 1 TB of storage. But when the allocated storage grows to 900GB or more, the database instance will scale to 2 TB and so on up to 3 TB.
Verdict – Scalability is an important feature in a modern cloud data warehouse. The ability to scale up as usage and workloads increase and the ability to scale down to control costs during idle periods is a crucial requirement. From the Amazon Redshift vs Oracle ADW info., Both Amazon Redshift and Oracle ADW can scale up or down without downtime or disruption.
5) Amazon Redshift vs Oracle ADW – Deployment Options
The Deployment Options for Amazon Redshift and Oracle ADW are compared below:
Redshift serverless allows customers to deploy fully managed, highly scalable data warehouses on a serverless platform. From an account provisioning standpoint, you can set up a new account and spin up a Redshift data warehouse in a couple of minutes.
The fact that it’s serverless means that you can free up your IT team with zero server management, zero-configuration deployments management such as planning for capacity.
Oracle ADW Deployment Options
Oracle ADW is really fast to set up (like 2 minutes to create a new database). You have the choice of two versions of two instance types. The Exadata Database Machine X2-2 and the X2-8. The X2-2 type can grow from 2 12-core database servers with 192 GB of memory and 3 Exadata Storage Servers to 8 12-core database servers with 1,152 GB of memory and 14 Exadata Storage Servers, all in a single rack.
You can deploy Oracle ADW on your On-Premises Datacenter to address data sovereignty, security, and performance concerns. With this option, you also stand to benefit from low latency fast data processing. Alternatively, you can also deploy Oracle ADW on a dedicated OCI (Exadata Cloud Infrastructure), instead of shared infrastructure with other tenants for more isolation and data governance.
Verdict – Oracle ADW provides more deployment options for enterprises that are reluctant to move their workloads to the public cloud.
6) Redshift vs Oracle ADW – Ease of Use
The Ease of Use for Amazon Redshift and Oracle ADW are compared below:
It’s extremely easy to run analytical workloads in Redshift serverless. There is an abstraction from the underlying infrastructure which means that you can run and scale analytics without having to manage a data warehouse cluster. The fact that it’s serverless doesn’t mean that there are no servers, it just means that you’re not responsible for managing and provisioning these servers.
This is outsourced to the cloud provider (AWS) and you’re only responsible for business logic. Redshift also has easy-to-use DDL and DML queries. The in-built functions are easy to use and help significantly in the faster execution of queries.
Oracle ADW Ease of Use
In Oracle ADW, patching, backup, maintenance, security, upgrades, performance tuning, etc, are automatically done for you (as well as duplication/failovers, if you want). Backups are very easy to restore. Disaster recovery is granted thanks to fault domains provided by Oracle.
The latest enhancements in terms of Oracle ADW’s UI for data ingesting, loading, charting and data give it a complete SaaS feel. Oracle ADW is using automation to deliver a point-and-click, drag-and-drop experience that’s so intuitive that virtually anyone from business professionals to data scientists can use it. Working with data in Oracle ADW does not require you to know SQL, which is how Redshift and every other cloud data warehouse works today.
Verdict – Oracle ADW has more built-in functionalities and has a more SaaS feel compared to Redshift which makes it extremely easy to run ad hoc analysis even for non-technical users.
7) Amazon Redshift vs Oracle ADW – Flexibility
The Flexibility of Amazon Redshift and Oracle ADW are compared below:
Amazon Redshift is integrated with other AWS data stores, such as Amazon Simple Storage Service (Amazon S3) data lake, Amazon Aurora, and Amazon Relational Database Service (RDS) databases. You can run federated queries on data sitting in these data stores without having to load the data into Redshift.
Amazon Redshift Serverless also supports advanced SQL functionality such as semi-structured data support. You can use any JDBC/ODBC-compliant tool or the Amazon Redshift Data API to query your data.
Oracle ADW Flexibility
Oracle ADW supports multi-workload requirements, within a single converged database engine, including operational, analytic, JSON document, graph, ML, and blockchain. You can also run queries on cooler data sitting in Oracle Cloud Infrastructure Object Storage, Azure Blob Storage, Amazon S3, Google Cloud Storage, Wasabi Hot Cloud Storage, and GitHub Repository. These queries won’t run as fast compared to data stored on Oracle ADW but do afford that flexibility.
Verdict – In Oracle Autonomous Database, transactional data can be combined with analytical data with relative ease. It is possible to run queries on transactional and semi-structured data in AWS Redshift. However, the fact that Oracle ADW handles this natively and that you can query data sitting on other cloud platforms makes it the most flexible solution.
8) Amazon Redshift vs Oracle ADW – Security
The Security Features between Amazon Redshift and Oracle ADW are compared below:
Redshift effectively implements Secure Socket Layer (SSL) encryption for establishing cluster connections between third-party applications and the AWS platform. Data is encrypted both in transit and at rest using an AWS-managed key. You can optionally create your custom key and use the AWS Identity and Access Management (IAM) roles to give permissions to access other AWS resources.
Oracle ADW Security
Oracle ADW uses always-on encryption to safeguard your data at rest and in transit. It autonomously applies security patches and updates. The database instance also actively monitors for external and internal threats to identify sensitive data. It automatically masks data, issues alerts on risky users and configurations and discovers suspicious attempts to access data.
Verdict – Oracle ADW automates the processes of conducting regular security assessments, user and privilege analysis, sensitive data discovery, sensitive data protection, and activity auditing.
The same functionality can be achieved in Amazon Redshift although you must manually make the configurations using disparate tools resulting in the potential for misconfiguration and exposing risks. Therefore, in our opinion, Oracle ADW out of the box is a more secure offering.
9) Amazon Redshift vs Oracle ADW – Machine Learning
The Machine Learning compatibility for Amazon Redshift and Oracle ADW are compared below:
Amazon Redshift does not have built-in machine learning and predictive analytics. Redshift’s customers have to use other tools, provided by AWS (such as SageMaker for AI/ML) or third-party vendor tools. AI/ML in Redshift involves implementing another service and learning another set of tools to access predictive insights.
This comes at a cost in terms of integration costs as well as data movement and coordination between systems. It can also result in delays to data usage and the insights generated for customers.
Oracle ADW Machine Learning
Oracle ADW customers can use the built-in Machine Learning and Advanced Analytics in ADW to automate the discovery of new insights, generate predictions, and add “AI” to data. This requires no additional investment or integration on the part of the customers.
Customers shared the key elements of Oracle’s data management and machine-learning capability, including in-database implementation of machine-learning algorithms, intelligent defaults, and fully parallelized machine learning models that can be accessed using SQL.
Verdict – Machine learning is an important workload for data warehouse customers who want to drive predictive actions from data. Machine learning capabilities are not available out of the box; Amazon Redshift customers must implement additional services such as SageMaker or other third-party tools, which add integration costs, time and effort for moving data, and operational complexity.
Oracle ADW customers find Machine Learning as a built-in capability is easy to use and requires minimal training, whereas their Amazon Redshift counterparts incur integration costs, time and effort for moving data, and operational complexity.
Oracle ADW and Amazon Redshift Serverless are extremely easy to provision, use and maintain. In both systems, customers don’t have to grapple with manual labor-intensive administration and management.
In our opinion, Amazon Redshift’s serverless data warehouse offers increased scalability, smart pricing, and improved savings. However, Oracle’s Autonomous Data Warehouse is trying to accomplish something different.
With Oracle’s Cloud Data Warehouse, customers do get a single, converged cloud database that should meet their business requirements, removing the need to buy specialized databases and integrate data from multiple sources.
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