May 12, 2022

Top 20 Amazon Athena Interview Questions and Answers


        Amazon Athena is an interactive query service that makes it simple to use normal SQL to evaluate data in Amazon S3. Because Athena is serverless, you don't have to worry about maintaining infrastructure, and you just pay for the queries you run.

Athena is simple to operate. Simply point to your Amazon S3 data, define the schema, and begin querying using regular SQL. The majority of results arrive in seconds. There's no need for complicated ETL procedures to prepare your data for analysis with Athena. This makes it simple for anyone with SQL expertise to study massive datasets fast.

AWS Glue Data Catalog is pre-integrated with Athena, allowing you to construct a uniform metadata repository across multiple services, explore data sources to locate schemas, populate your Catalog with new and amended table and partition definitions, and maintain schema versioning.

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Ques. 1): What is Amazon Athena all about?


Amazon Athena is an interactive query service that makes it simple to use normal SQL to evaluate data in Amazon S3. Because Athena is serverless, there is no infrastructure to set up or operate, and you can immediately begin analysing data. You don't even have to load your data into Athena; it works with S3 data immediately. Simply log into the Athena Management Console, create your schema, and begin querying. Amazon Athena works with a range of standard data formats, including CSV, JSON, ORC, Apache Parquet, and Avro, and leverages Presto with full SQL support. While Amazon Athena is great for interactive analytics and interacts with Amazon QuickSight for quick visualisation, it's not the most user-friendly platform. it can also handle complex analysis, including large joins, window functions, and arrays.

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Ques. 2): What makes Amazon Athena, Amazon EMR, and Amazon Redshift different?


Different demands and use cases are addressed by query services like Amazon Athena, data warehouses like Amazon Redshift, and advanced data processing frameworks like Amazon EMR. All you have to do now is pick the correct tool for the job. For enterprise reporting and business intelligence workloads, Amazon Redshift provides the fastest query performance, especially for those utilising extremely sophisticated SQL with numerous joins and sub-queries. When compared to on-premises deployments, Amazon EMR makes running highly distributed processing frameworks like Hadoop, Spark, and Presto straightforward and cost effective. You can execute bespoke apps and code on Amazon EMR, as well as configure particular computing, memory, storage, and application parameters to maximise your analytic needs. Amazon Athena makes it simple to execute interactive queries over S3 data without having to set up or manage any servers.

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Ques. 3): When should I utilise Amazon EMR and when should I use Amazon Athena?


Amazon EMR is capable of much more than just conducting SQL queries. You can use EMR to conduct a variety of scale-out data processing activities for applications like machine learning, graph analytics, data transformation, streaming data, and almost anything else you can think of. If you utilise custom code to handle and analyse extremely huge datasets with the latest big data processing frameworks like Spark, Hadoop, Presto, or Hbase, you should use Amazon EMR. Amazon EMR allows you complete control over the configuration and applications installed on your clusters.

If you want to conduct interactive SQL queries against data on Amazon S3 without having to manage any infrastructure or clusters, you should utilise Amazon Athena.

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Ques. 4): What data formats is Amazon Athena compatible with?


Amazon Athena can handle a wide range of data formats, including CSV, TSV, JSON, and Textfiles, as well as open source columnar formats like Apache ORC and Apache Parquet. Snappy, Zlib, LZO, and GZIP compressed data formats are also supported by Athena. You can increase speed and lower costs by compressing, dividing, and adopting columnar formats.

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Ques. 5): I'm getting data from Kinesis Firehose. How can I use Athena to query it?


You can use Amazon Athena to query your Kinesis Firehose data if it's hosted on Amazon S3. Simply construct an Athena schema for your data and begin querying. To improve efficiency, we recommend dividing the data into parts. ALTER TABLE DDL instructions can be used to add partitions produced by Kinesis Firehose. Get more information on partitions.

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Ques. 6): How can I make my query perform better?


By compressing, splitting, or turning your data into columnar formats, you can increase the performance of your query. Apache Parquet and Apache ORC are two open source columnar data formats that Amazon Athena supports. By allowing Athena to scan less data from S3 when executing your query, converting your data into a compressed, columnar format minimizes your costs and increases query performance.

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Ques. 7): What is a federated query, exactly?


If you have data in places other than Amazon S3, you may use Athena to query it or create pipelines to extract data from numerous sources and put it in Amazon S3. You can perform SQL queries against data stored in relational, non-relational, object, and custom data sources using Athena Federated Query.

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Ques. 8): Can I do ETL (Extract, Transform, Load) using federated queries?


Athena stores query results in an Amazon S3 file. This means Athena may be used to make federated data accessible to other users and apps. Use Athena's CREATE TABLE AS function to perform analysis on the data without having to query the underlying source frequently. You may also query the data using Athena's UNLOAD function and save the results in a specific file format to Amazon S3.

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Ques. 9): What embedded ML use cases does Athena support?


The following examples show how Athena can be used in a variety of sectors. What-if analysis and Monte Carlo simulations are available to financial risk data analysts. To aid in the creation of richer and forward-looking business dashboards that forecast revenues, business analysts may use linear regression or forecasting models to predict future values. K-means clustering methods could aid marketing analysts in determining their various client categories. Logical regression models could be used by security analysts to uncover abnormalities and detect security incidents in logs.

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Ques. 10): What capabilities does Athena ML have?


Athena provides machine learning inference (prediction) capabilities using a SQL interface. You can also use an Athena UDF to perform pre- or post-processing logic on your result set. Multiple calls can be batched together for increased scalability, and inputs can be any column, record, or table. Inference can be performed during the Select or Filter phases.

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Ques. 11): Is Athena highly available?


Yes. Amazon Athena is highly available, executing queries across many facilities and intelligently routing queries correctly if one of the facilities is unavailable. Athena's underlying data store is Amazon S3, which makes your data highly available and durable. Amazon S3 provides a reliable infrastructure for storing essential data, with 99.999999999 percent object durability. Your information is duplicated across numerous facilities and devices inside each facility.

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Ques. 12): What should I do to lower the costs?


By compressing, splitting, and turning your data into columnar formats, you can save 30 percent to 90 percent on query costs while also improving performance. Each of these actions reduces the quantity of data that Amazon Athena must scan in order to complete a query. Apache Parquet and ORC, two of the most popular open-source columnar formats, are supported by Amazon Athena. On the Athena console, you can view how much data was scanned for each query.

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Ques. 13): Are there any other fees related with Amazon Athena?


Your source data is invoiced at S3 rates because Amazon Athena queries data directly from Amazon S3. When you perform a query through Amazon Athena, the results are saved in an S3 bucket of your choosing, and you are charged at standard S3 rates for these result sets. We recommend that you keep an eye on these buckets and utilise lifecycle policies to limit how much data is kept.

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Ques. 14): Does the User Defined Functions (UDFs) are supported by Athena?


User-defined functions (UDFs) in Amazon Athena allow you to create new scalar functions and utilise them in SQL queries. While Athena has built-in capabilities, UDFs allow you to conduct custom processing such as data compression and decompression, redaction of sensitive material, and bespoke decryption.

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Ques. 15): In Amazon Athena, how can I add new data to an existing table?


If your data is partitioned, you'll need to run an ALTER TABLE ADD PARTITION metadata query to add the partition to Athena once new data is available on Amazon S3. If your data isn't partitioned, simply adding new data (or files) to an existing prefix will add them to Athena.

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Ques. 16): What exactly is a SerDe?


Serializer/Deserializer are libraries that teach Hive how to understand different data formats. You must mention a SerDe in Hive DDL statements so that the system knows how to interpret the data you're pointing to. SerDes is used by Amazon Athena to analyse data read from Amazon S3. SerDes is the same notion in Athena as it is in Hive. The following SerDes are supported by Amazon Athena:

Apache Web Logs: "org.apache.hadoop.hive.serde2.RegexSerDe"

CSV: "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"

TSV: "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"

Custom Delimiters: "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"

Parquet: ""

Orc: ""


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Ques. 17): Can I query data processed with Amazon EMR using Amazon Athena?


Yes, Amazon Athena and Amazon EMR both support many of the same data formats. The Athena data catalogue is compatible with the Hive metastore. If you're utilising EMR and already have a Hive metastore, you can query your data straight away without affecting your Amazon EMR operations by executing your DDL statements on Amazon Athena.

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Ques. 18): How are table definitions and schema stored in Amazon Athena?


To keep information and schemas about the databases and tables you create for your data saved in Amazon S3, Amazon Athena employs a managed Data Catalog. You can use the AWS Glue Data Catalog with Amazon Athena in regions where AWS Glue is accessible. Athena uses an internal Catalog in regions where AWS Glue is not available.

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The catalogue can be modified using DDL statements or the AWS Management Console. Unless you delete them directly, any schemas you define are automatically stored. Athena leverages schema-on-read technology, which means that when queries are run, your table definitions are applied to your data on S3. There’s no data loading or transformation required. You can delete table definitions and schema without impacting the underlying data stored on Amazon S3.

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Ques. 19): Can I use Athena to run any Hive query?


Hive is only used by Amazon Athena for DDL (Data Definition Language) and for creating, modifying, and deleting tables and partitions. For a complete list of statements that are supported, please check here. When you run SQL queries on Amazon S3, Athena uses Presto. To query your data in Amazon S3, you can use ANSI-Compliant SQL SELECT queries.

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Ques. 20): Is data partitioning possible with Amazon Athena?


Yes. You can segment your data on any column with Amazon Athena. Partitions reduce the quantity of data scanned by each query, resulting in cost savings and faster performance. The PARTITIONED BY clause in the CREATE TABLE statement allows you to specify your partitioning plan.  

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Ques. 21): What is the purpose of data source connectors?


A data source connector is a piece of AWS Lambda code that bridges the gap between your target data source and Athena. You can conduct SQL queries on federated data stores after using a data source connector to register a data store with Athena. When a query is conducted on a federated source, Athena invokes the Lambda function, which is tasked with executing the parts of your query that are unique to the federated source.

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