Athena is best suited when we need to run the queries against some weblogs for troubleshooting the issues in the site. But services like Amazon Athena makes it easier to run the interactive queries against the extensive data by directly uploading them in Amazon S3 and don’t worry about managing the infrastructure and handling the data. So, when we need to run the queries against extensive structured data and need to apply lots of joins across the tables, and then we should go for Amazon Redshift. The query the engine in Amazon Redshift has been optimized for performing well especially in the use cases where we need to run several complex queries like joining several large datasets. When comparing to a data warehouse like Amazon Redshift, it should be best chosen when the data is to be taken from several different sources, like retail sales the system, financial systems or any other sources and we have to store the data for a A more extended period to build any report based on that data. Click to explore about, Amazon SageMaker When to Use Amazon Athena and not Other Big Data Services? ![]() ![]() AWS SageMaker uses Jupyter Notebook and Python with boto to connect with the s3 bucket, or it has its high-level Python API for model building. Another thing is the initialization time, in Athena, we can straight away query the data on Amazon S3, but in Redshift, we have to wait for the cluster to get active and once the cluster is activated, only then we are allowed to query the data. Speed and PerformanceĪs Amazon Athena is serverless, which makes it quicker and easier to execute the queries on Amazon S3 without taking care of the server and the cluster to set up or manage. We can easily add columns in bulk and also easily do the partitioning of the table in Athena, whereas Redshift requires to configure all the cluster properties, and also it takes much time for a cluster to get active. User ExperienceĬoming to the user interface, Amazon Athena provides a simple UI.Getting started with Athena is much more comfortable, all need to do is create a database, select the table name and specify the location of the data on Amazon S3. So, here Athena edges out as compare to Redshift. Athena also supports data types like arrays and objects, but when comparing it with Redshift, it does not give support to such data types. Some of the other reasons for choosing Athena over others can be Data FormatsĪmazon Athena service works with several different data formats as discussed above. It also provides support to various data formats like structured, semi-structured and unstructured. One of the best reasons for choosing Amazon Athena is that it provides serverless Querying of the data which is stored in Amazon S3 with the help of standard SQL. Click to explore about, Guide to Amazon Redshift Why Choose Amazon Athena? A type of data warehouse service in the Cloud which is fully managed, reliable, scalable and fast and is a part of Amazon’s Cloud Computing. With the help of Amazon Athena, we can process any of data, whether it is structured, semi-structured or unstructured data, i.e., it can handle the data in CSV, avro or in columnar formats like parquet and orc. It even does not need to load the data in Athena.Īll we require to do is to point to the data in Amazon S3, define the particular schema and start querying using the standard SQL. ![]() ![]() It can quickly analyze the data with the help of Amazon S3 using standard SQL. Due to its serverless feature, it needs no infrastructure to manage or to setup. Amazon Athena is a serverless interactive query service or interactive data analysis tool which is used for processing complex queries and in a lesser amount of time.
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