Amazon RedShift Pros & Cons

Amazon Redshift Pros:

Let's take a look at some of the benefits of Amazon Redshift:

Exceptionally fast - Redshift is very fast when it comes to loading data and querying it for analysis and reporting purposes. Redshift has a Massively Parallel Processing (MPP) architecture that allows you to load data at breakneck speed. Moreover, by using this architecture, Redshift distributes and parallelizes your requests on several nodes.

High Performance - As noted in the previous paragraph, Redshift achieves high performance through massive parallelism, efficient data compression, query optimization, and distribution.

Redshift gives you the ability to define a column-based encoding for data compression. If not specified by the user, redshift automatically assigns a compression encoding. Data compression helps reduce memory clutter and dramatically improves I / O speed. To learn more about this, check out our blog titled Amazon Redshift Architecture.

Horizontal Scalability - Scalability is a very important point for any data warehousing solution and Redshift is doing a great job in this area. Redshift is scalable horizontally. Whenever you need to increase storage capacity or run it faster, simply add more nodes using the AWS Console or Cluster API and everything will be upgraded immediately.

Massive Storage Capacity - As expected in a data warehousing solution, Redshift offers considerable storage capacity. A basic configuration can give you a range of data storage in petabytes. In addition, Redshift gives you the ability to choose the type of Dense Storage computing nodes that can provide large storage space using hard drives at a very low price. You can further increase storage by adding more nodes to your cluster, which can go well beyond the petabyte of the data range.

Attractive and Transparent Pricing - Pricing is a very strong point in favor of Redshift, it is considerably less expensive than alternatives or an on-site solution. Redshift has 2 pricing models, pay on demand and reserved instance. This gives you the opportunity to classify this expense as an operational expense or capital.

SQL Interface - Redshift Query Engine is based on ParAccel which has the same interface as PostgreSQL If you are already familiar with SQL, you do not need to learn a lot of new technicians to start using the Redshift query module. Because Redshift uses SQL, it works with existing Postgres JDBC / ODBC drivers and connects easily to most Business Intelligence tools.

AWS Ecosystem - Many companies are already using their infrastructure on AWS, EC2 for servers, S3 for long-term storage, RDS for databases, and this number is growing steadily. Redshift works fine if the rest of your infra is already on AWS and you benefit from the location of the data and the cost of transporting the data is comparatively low. For many companies, S3 has become the de facto destination for cloud storage. Redshift being virtually co-located with S3, it can access

Security - Amazon Redshift comes with various security features. There are options such as VPC for network isolation, various ways to manage access control, data encryption, and so on. The data encryption option is available in several places in Redshift. To encrypt the data stored in your cluster, you can enable cluster encryption when the cluster is started. In addition, to encrypt data in transit, you can enable SSL encryption. When loading data from S3, redshift allows you to use server-side encryption or client-side encryption. Finally, at the time of loading the data, the command S3 or Redshift copy manages the decryption respectively.

Amazon Redshift Cons:

Amazon Redshift Limitations and Drawbacks:

This section describes some of the limitations and disadvantages of Amazon Redshift.

Does not impose uniqueness - It is not possible in redshift to impose uniqueness on inserted data. Therefore, if you have a distributed system and write data on Redshift, you will need to handle the uniqueness yourself, either on the application layer or by using a data deduplication method.

Support for Parallel Sending Only by S3, DynamoDB, and Amazon EMR - If your data is in Amazon S3 or in relational DynamoDB or Amazon EMR, Redshift can load it using Massively Parallel Processing processing, which is very fast. But for all other sources, parallel loading is not supported. You will either have to use JDBC inserts or scripts to load data into Redshift. You can also use an ETL solution such as Hevo, which allows you to load your data into Redshift in parallel from hundreds of sources.

Requires a good understanding of the sort and dist keys. - Sort keys and distribution keys decide how data is stored and indexed on all Redshift nodes. Therefore, you must fully understand these concepts and correctly define them on your tables for optimal performance. There can only be one distribution key for a table, and this cannot be changed later, which means that you need to think about and anticipate future workloads before deciding on the Dist key. You can read our blog in detail about Amazon Redshift distribution keys and Amazon Redshift sort keys.

Cannot be used as a live application database - Although Redshift is very fast when you run queries on a huge amount of data or run reports and scans, it is not fast enough for applications Live Web. You will need to extract data into a caching layer or vanilla instance of Postgres to transmit redshift data to web applications.

Data on Cloud - While this is a good thing for most people, in some cases of use, this can be a concern. So, if you're concerned about data privacy or if your data has extremely sensitive content, you may not be comfortable putting it on the cloud.

Conclusion:
Amazon Redshift is an solution for data warehousing. We gave a brief overview of Amazon Redshift: the pros and cons. He has some limitations, but he is way ahead of alternatives like Bigquery and Snowflake. You may need to learn a few things to use it wisely, but once you understand, it will work smoothly.

If you choose to set up an Amazon Redshift data warehouse, one of the biggest hurdles to overcome is to seamlessly import data from your existing data sources into Redshift. The challenge increases if you need this data in real time. Writing custom scripts for this purpose can be tricky, affecting the accuracy and consistency of the data.

At Hevo, we built a data integration platform that can help transfer data from hundreds of different sources to Redshift in near real time without having to write code. You can connect to any data source using the Hevo user interface, and instantly move data from any data source to Redshift.

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