Pros and cons of Amazon Redshift

Before you decide if Amazon Redshift suits your data requirements, it is crucial to know the nature of. An knowledge of the benefits and drawbacks associated with Amazon Redshift will help you make a well-informed choice.

What exactly is Amazon Redshift?

Amazon Web Services (AWS) is the very first public cloud service to provide the cloud-based, petabyte-scale storage service. The service is known as Amazon Redshift and is the most well-known cloud-based data warehouse.

Amazon has a large number of businesses as clients. Yet, competition in the area is increasing as well, with Google Big Query, Snowflake along with Oracle Automation Data Warehouse eyeing some of the lucrative cloud market for data warehouses.

Amazon Redshift has been around since 2013 and has gone through numerous improvements. Amazon Redshift Spectrum, AWS Athena and the ever-present massively scalable storage service, Amazon S3, compliment Amazon Redshift and offer all the tools needed to build an data warehouse or data lake at an enterprise scale. Let’s dig a bit deeper to discover the advantages and disadvantages for SQL Workbench Redshift in more detail.

Amazon Redshift’s advantages Amazon Redshift

Widely accepted

Amazon Redshift has a thriving and a large customer base, being it is among the first cloud-based technology for data warehousing. A robust ecosystem of experienced resources are available to help businesses in the process of generating benefits from their data warehousing initiatives.

Administration ease

Amazon Redshift offers an assortment of tools designed to ease the administrative burden commonly entailed with managing databases. Tools are provided to easily create clusters and automate backups of the database up to allow you to increase the size of your data warehouse upwards and downwards. These tasks required database administrators previously. With the tools specifically that are available through Amazon Redshift, users can press a few buttons or make use of REST APIs in order to accomplish these tasks.

Ideal for data lakes

Amazon Redshift Spectrum extends the capabilities that Redshift has by permitting it to increase the capacity of storage and compute independently of each other. It also makes queries on the data within S3 buckets.

It is easy to ask questions

Amazon Redshift has a similar querying language that is similar to PostgreSQL. Anyone who is familiar with PostgreSQL can utilize their SQL abilities to begin working using Redshift Clusters. JDBC as well as ODBC support allow developers to access their Redshift clusters by using an DB query tool of their choice. Redshift console also permits users to create queries and also work with the database. However, those who are power users may prefer using a different software of their preference. Many business intelligence software on the market today can be paired with Amazon Redshift.

Columnar storage

When rows are added to the database of a relational type it is usually in a row-format. Although row formats are efficient for writing operations however, they are not as efficient when reading. Columnar compression utilizes redundant data in every row and a column-oriented compression technique can compress the missing data in fields more effectively. By compressing column data, the footprint of storage on the disk will be greatly diminished. A query based on columns can be able to scan the data with less footprint and transmit a lesser amount of data through the network or the I/O subsystem to the compute Node to process. This results in a substantial increase in the efficiency of processing analytical queries.
Performance

Amazon Redshift is an MPP database. MPP is a shorthand as Massively Parallel Processing. A streamlined use of columnsar algorithms for storage as well as techniques for partitioning data provide Amazon Redshift an edge in terms of performance.

Scalability

The capacity to scale is among the most crucial aspects of a database which is why Amazon Redshift is no different. Scaling the Redshift cluster is easy as compared to scaling an on-premises database. Hardware expansion-related issues that arise from internal processes, VM resizing, and data rebalancing between the nodes are completely managed through Amazon Redshift and hidden under the gui of a UI button or an REST API request.

Security

Security is an important obstacle in the adoption by many businesses of cloud-based services. But, it’s important to understand that cloud services provide an incredibly higher level of security when properly configured as compared to the internal IT (Information Technology) teams Security configurations. The sheer size of public clouds lets them hire more staff and use them to manage and secure the cloud’s environment 24x7x365.

Amazon Webservices is no different. When we speak of Amazon Redshift security, it is not possible to do it by itself. The security features provided through Amazon Redshift are available to users in addition to the security features implemented at the cloud service layer. Access management and identity protection that is robust as well as role-based access control (RBAC) and encryption during transport and at rest, as well as SSL connections are just a few of the security features available on Redshift.

AWS ecosystem is strong AWS ecosystem

If you’re considering Amazon Redshift as your data warehouse, there are several environments operating on AWS. While it is important to select an appropriate application for your workload is, it’s crucial to take into consideration other factors such as community support price and discounting and the capabilities within the business.

A decision to choose a specific technology has both tactical and strategic implications. It’s not a big deal for smaller companies. However, larger enterprises with well-established teams need to take these elements into consideration when making a decision on any software purchase for example, selecting the right data warehouse. With the wide range of options available in AWS businesses can gain by bundling their services in order to gain more benefits from the services they use.

Pricing

Numerous factors affect the cost of purchasing the Amazon Redshift cluster. Anyone thinking of Amazon Redshift as their data warehouse needs to understand these elements thoroughly to avoid unanticipated surprises.

The cons of Amazon Redshift

Amazon Redshift is a data storage system that is designed to be a warehouse. The whole service is designed and tuned to an exact workload, which is analytics processing. Perhaps you’re interested in databases that can perform efficient transaction processing. In this scenario, AWS has several other options like Amazon Aurora, Amazon RDS, DynamoDB, and others that you could look into.

There is no multi-cloud option.

The ecosystem plays an important function in the selection of software, the lack of choice is viewed as a way for the software provider to lock customers in to their offerings. Amazon Redshift, unlike Snowflake it is available only through AWS. If you’re a client or a customer of Azure, GCP, or Oracle Cloud and are looking to look at the solutions offered by these cloud providers before you decide to use Amazon Redshift.

Amazon Redshift is not 100 percent controlled

Although the tools provided by Amazon make it less necessary for a database administrator full-time, it doesn’t remove the requirement for one. Amazon Redshift is known to be unable to handle storage effectively in a system susceptible to frequent deletions. The maintenance of sort order is essential to achieve efficient performance metrics. This aspect of databases aren’t commonly understood by developers, and some could argue that they need not bother. It’s true.

The present advancements in technology for databases can remove the requirement for users to be aware of the basics of database administration and control the database to provide optimal performance, without the need for an administrator of databases. Snowflake along with Oracle Autonomous data warehouses have achieved huge progress in this direction. Amazon Redshift has already released many features such as automated table sorting and automatic vacuum deletes and automated analysis, which are proving that it is making progress in this area.

Concurrent execution

The issue of concurrent execution has become a common issue when working with MPP databases. If there are multiple concurrent users are working on the same queries Redshift might encounter problems with performance. Additionally because of the lack of separating storage and computing the read workload is affected due to the high-speed writing that is happening in the database as a result of an enormous batch processing job.

Resizes of clusters cause disruptions of the service for the user. While it is not a major issue is experienced, the inability to provide continuous resizing of clusters and capabilities, is as a disadvantage in a market which has competition offering the capability to scale up or down without interruption. This small disruption is manageable for the majority of businesses, but it is it is a problem for some.

The choice of keys affects the performance and cost

In the cloud the cloud, performance = cost.

Users must be careful in constructing their strategies for key distribution and sorting while being aware of the future needs. They should regularly review the accuracy of their type of key and distribution keys as more data is introduced into Amazon Redshift. Amazon Redshift data warehouse. Unoptimal designs can raise the cost associated with Redshift. Redshift data warehouse as the performance of the system declines and this can lead to problems with satisfaction of users. It is possible to increase the size of your cluster to tackle the issue but it would also raise your costs. But, a carefully planned approach to managing the cluster will allow companies to reap the maximum benefit the Amazon Redshift investment before scaling up.

Master Node

The Master Node performs an essential function within the Redshift architecture as it orchestrates queries such as allocation, execution and aggregation as well as the execution results. Clients only interact via the master Node and so, a master node is the only point of failure to the entire environment.

This is not a serverless design.

Amazon Redshift is an old timer when it comes to cloud-based data warehouses. Redshift isn’t without its flaws as it was designed several years back. Serverless technology allows the manufacturer to perform an increased level of optimization of the hardware, which results into lower costs for clients. The cost will be lower in the event that the same hardware is used by three users vs. one. Old guards can benefit because they have been around for a long period of time and continuously innovating over a long period. They can be more beneficial than the perceived disadvantages, but sometimes they don’t.

Conclusion

The selection of data warehouses is based on the needs of your business and budget, the present state of your company, and the plans to utilize this data warehouse. We don’t believe that there is a correct or incorrect choice regarding the technology you choose. Contact us with any questions about which data warehouse is an appropriate match for your business. Our data architects can assist you to make the best choice for your company.

We believe strongly in how data can be used to improve business and how businesses of all sizes are able to gain from fast advancements in cloud data warehouse technology. Check out our article about our reasons for believing that it’s time for every business to recognize the benefits of having a data warehouse within their the business world and to invest heavily into data warehouses.