Before you decide if Amazon Redshift suits your data needs, it’s important 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 first cloud public service provider that offers 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 customers. Yet, competition in the sector is growing and there are 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 seen many improvements. Amazon Redshift Spectrum, AWS Athena and the ubiquitous massively scalable data storage service, Amazon S3, compliment Amazon Redshift and offer all the necessary tools to create an data warehouse or data lake that is enterprise-scale. Let’s dive a little deeper to discover the advantages and disadvantages for Amazon Redshift in more detail.
Amazon Redshift’s advantages Amazon Redshift
Widely accepted
Amazon Redshift has a thriving and loyal customer base. It is it is among the first cloud-based data warehouse technologies. A robust ecosystem of experienced experts is readily available to assist companies in gaining benefits from their data warehousing initiatives.
Administration is easy
Amazon Redshift offers an assortment of tools that can help reduce the administrative burden that is typically incurred when managing the database. The tools are available to build clusters quickly and automate backups of the database up to allow you to increase the size of your data warehouse both up and down. All of these tasks required database administrators prior to. With the special tools that are that are available through Amazon Redshift, users can hit a few buttons and make use of REST APIs in order to accomplish these tasks.
Ideal for data lakes
Amazon Redshift Spectrum extends the capacity offered by Redshift, allowing it to increase the capacity of storage and compute independently of one another and performs queries on data inside S3 buckets.
The ease of asking questions
Amazon Redshift has a similar querying language that is similar to PostgreSQL. Anyone who is familiar with PostgreSQL can apply their SQL expertise to get started using Redshift Clusters. JDBC along with ODBC support allow developers to access their Redshift clusters with an DB query tool of their choice. Redshift console allows users to make 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 are compatible with Amazon Redshift.
Columnar storage
When rows are entered into an existing relational database the data is typically saved in a row-format. Although row formats are efficient when writing but they do not perform as well when reading. Columnar compression makes use of redundant data for each row and a column-oriented compression method can compress data that is missing in fields faster. By compressing the column data the footprint of storage on the disk is significantly diminished. A query based on columns can be able to scan with a smaller footprint of data 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 improvement in the speed of processing analytical queries.
Performance
Amazon Redshift is an MPP database. MPP is a shorthand as Massively Parallel Processing. Effective use of columnsar algorithms for storage as well as techniques for partitioning data can give 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 a breeze as compared to scaling an on-premises database. Hardware expansion-related issues that arise from internal processes, VM resizing, and shifting data between nodes are completely managed through Amazon Redshift and hidden under the cover of a UI button or an HTTP API.
Security
Security is an important obstacle in the adoption by many businesses of cloud-based services. But, it’s important to recognize that cloud services provide the highest level of security when they are properly set up in comparison to internally-designed IT (Information Technology) teams and their security configurations. The size of cloud services allows them to employ more staff and use them to manage and secure the cloud environment 24/7.
Amazon Webservices is no different. When we speak of Amazon Redshift security, it can’t be done by itself. The security features offered via 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 and access control based on role (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 thinking of Amazon Redshift as your data warehouse, you’ve got several environments operating on AWS. As crucial as choosing the right applications for your workloads is, it’s crucial to take into consideration other elements such as community support as well as pricing and discounting and the capabilities within the business.
The choice of a particular technology can have both tactical and strategic implications. It’s not a big deal for smaller companies. However, larger companies with established teams should take these elements into consideration when making a decision on any software purchase and, in particular, choosing an AWS data warehouse. With the variety of services available through AWS companies can profit by bundling their offerings to gain more benefits from the services they use.
Pricing
Numerous factors affect the price you pay for the Amazon Redshift cluster. Anyone who is considering Amazon Redshift as their data warehouse needs to understand these elements in depth to avoid any unanticipated surprises.
Pros and Cons of SQL Workbench Redshift
Amazon Redshift is a data warehouse system designed for. The whole service is tuned and optimized for particular workloads, such as analytics processing. If you’re looking for databases that can perform efficient transaction processing. In this scenario, AWS has several other options like Amazon Aurora, Amazon RDS, DynamoDB, and others which you might want to look into.
It is not a multi-cloud solution.
The ecosystem plays a crucial function in the selection of software, the absence of choices is seen as a way for the software provider to lock customers in to their offerings. Amazon Redshift, unlike Snowflake and Snowflake, is only accessible through AWS. If you’re a client or a customer of Azure, GCP, or Oracle Cloud be sure to examine the options offered by these cloud providers before you decide to choose Amazon Redshift.
Amazon Redshift is not 100 100% controlled
While the tools offered by Amazon make it less necessary for a database administrator full-time, it doesn’t completely eliminate the need for one. Amazon Redshift is known to have problems handling storage effectively in a system that is prone to frequent deletions. Sort order maintenance is essential to achieve efficient performance metrics. The aspects that affect the databases aren’t commonly understood by developers, and some could argue that they need not bother. They would be right.
The present advancements in the field of database technology could eliminate the requirement for users to comprehend the basics of database administration and control the database to ensure optimal performance, without the need for an administrator for the database. Snowflake as well as Oracle Autonomous data warehouses have made huge strides in this direction. Amazon Redshift has already released several features, including automatic table sorting as well as automatic vacuum deletes and automatic analysis, showing that it is making progress in this area.
Concurrent execution
The issue of concurrent execution has become a well-known issue for MPP databases. If there are multiple concurrent users are running query, Redshift might encounter issues with performance. Furthermore due to the absence of separating storage and computing the read workload is affected due to the high-speed writing taking place within the database because of an enormous batch processing job.
Resizes of clusters cause disruptions of the service for the user. Though it’s not too disruptive is experienced, the inability to provide continuous resizing of clusters and capabilities, is as a disadvantage in a market that has competitors who offer the ability to scale down and up without interruption. The minor inconvenience is acceptable for the majority of businesses, but it is it can be a hindrance for some businesses.
The choice of keys affects performance and costs
In the world of cloud computing performance is the price.
Users should carefully plan their strategies for key distribution and sorting while being aware of the future needs. They should regularly review the reliability of their type of key and distribution keys as more data is in Amazon Redshift. Amazon Redshift data warehouse. Unoptimal designs can raise the cost for Redshift. Redshift data warehouse due to the fact that the performance of the system declines and, in turn, causes problems with user satisfaction. It is simple to increase the size of the cluster to tackle the issue however, it will raise your costs. However, a well-thought-out approach to managing the cluster will allow companies to make the most from their Amazon Redshift investment before scaling up.
Master Node
It plays a crucial part within the Redshift architecture as it orchestrates queries such as allocation, execution and aggregation, as well as the results of their execution. Clients only interact directly with the master, so, a master node serves as a only point of failure to the entire environment.
It is not a serverless architecture.
Amazon Redshift is an old timer when it comes to cloud-based data warehouses. Redshift has its limitations and was developed many years back. Serverless technology allows the seller to achieve an increased level of optimization of the hardware, which results to lower costs for the customers. The cost will be lower when the same equipment is used by three users vs. one. Old guards can benefit due to their presence for a long time , and constantly innovating. The benefits can outweigh perceived disadvantages, and at times they don’t.
Conclusion
The decision to choose data warehouses is based on the needs of your business and your budget, as well as the present state of your company, and the plans for using your data warehouse. We don’t believe that there is a definitive correct or incorrect choice regarding the technology you choose. Please contact us if you have any questions about which data warehouse is the right match for your business. Our data architects can assist you to make the best choice for your business.
We believe strongly in that power of information and the ways that organizations of all sizes can gain from fast advancements in cloud-based data warehouse technologies. Check out our article about the reasons we believe it’s the right time for all businesses to recognize the benefits of having a data warehouse within their businesses and make investments on data warehouses.
