Most companies ride on cloud-based data warehouse technologies to stay competitive. Nowadays this trend is being followed by the majority of firms dealing with large chunks of data. It gives organizations a clear advantage to keep pace with the fast-changing dynamics in the competitive business market.
It has been said that the use of big data resources also necessitates proper planning. Before going ahead with the idea, a company needs to identify the best option by narrowing down the set of leading options.
When one goes by this rule, two options come under the spotlight as the leading choices – Amazon Redshift and Amazon Athena. Both have their share of positive and negatives.
Which one makes for a better option in Redshift vs Athena? We will weigh the pros and cons of both to find the answer to this question.
Amazon Redshift corresponds to Postgre SQL 8.0.2. It is a cloud-based data warehouse technology that is made to deliver a robust input-output performance in connection with the fast query. The best part about this service is that it performs equally well for all datasets.
A user needs to set it up properly, though. Once they set up the datasets in the form of cluster servers, the Redshift takes care of the rest. The smart analysis mechanism of the engine not only analyzes the data but also provides the outcomes in quickly.
Amazon Athena also handles query but unlike the other service, it is based on the fundamental SQL technology. Also, this service only deals with data linked with Amazon S3.
One of the key point about Athena is that this service is serverless. Due to the ability of this service to operate without a server, it is portable which makes it easy to carry. As far as its functioning is concerned, it analyzes all kinds of data that belong to Amazon S3.
Redshift vs Athena: A Systematic Comparison Based on Features
After getting the basic overview of both the services, lets run a comparison between the two to find out which one is a better choice. In doing so, we will consider some of the fundamental characteristics concerning both the services.
As far as AWS Athena vs Redshift spectrum is concerned, the former has an edge over the latter in terms of partitioning. While Redshift does not have the feature of partitioning a key on its own, Athena does the job effortlessly. It goes a step further to identify data from the individual libraries and divide them into chunks automatically to deliver the desired outcomes.
When considering the support for different types of data, both the services race neck and neck. While Redshift is incompatible with arrays and maps, Athena supports data in these forms.
Though Redshift gives a user the edge in terms of performance, as it supports a wide range of datasets. It does have downsides too, the first to name is that it takes a long time to start the functioning.
Sometimes a user may have to wait for hours to get Redshift started. This can slow down their working, especially at the time of need. Athena wins in this part. It starts instantly which translates into the fact that it saves time of the user. Unlike Redshift’s need to set up datasets manually, Athena does everything on its own. This saves a user the hassle of undertaking the pain of arranging the data on their own.
An important thing to remember here is that Athena is only useful for Amazon S3 data.
On the positive front of performance, Redshift offers a competitive edge to the user as it is capable of handling a wider range of data in comparison to Athena. Despite being slower than Athena in terms of reading or aggregating data in the form of a table, Redshift provides jQuery results at a faster speed.
As far as the security of the services is concerned, the two services use distinct arrangements to secure the data of users. While Redshift does the job with a security group, Athena uses the identity access management technology to protect user data from the prying eyes. Being a cloud service, the former also supports various other forms of encryption.
Redshift has an edge over Athena with regard to upgrading as the process is much simpler for the former than the latter. Athena, on the other hand, suffices for it with its brisk performance of arranging Amazon S3 data into querying tables. The need for the manual arrangement of different datasets into the table slows down the speed of Amazon’s Redshift service.
The comparison of Amazon Redshift spectrum vs Athena leads to an interesting outcome in terms of pricing. While Redshift services are based on an hourly rate without any penalty even after exceeding the number of queries, Athena does not charge for unsuccessful queries. Further, Athena saves money with compressed data.
The pricing of both services makes sense, though. Going by the operational costs of both the services, we can safely say that Redshift is designed for dealing with larger chunks of data in comparison to Athena.
Brief Comparison of Data Warehouse Performance
Performance of Redshift is completely dependent on the way your cluster is defined, whereas performance of Athena depends on the way you hit your query. In case of Athena, if you query a large file selecting all columns, without any filter condition, you will see a degraded performance.
The concept of Distribution key and Sort key is followed in Redshift.
How your data is distributed inside the node is defined by distributed key, whereas Sort key defines how the data will be stored in the blocks.
You query performance is driven by the distribution key during the joins, while filter operations are where the Sort keys come into effect.
Going by the results of the aforementioned features, it can be a little tricky on the part of users to pick a winner between AWS Redshift vs Athena. Both offer a unique set of functionalities to cater to the need of users in their own ways. So, the decision to decide which one leads the race from the other rests at the discretion of users. They can make a decision based on how well a particular service help meet their requirements.