Amazon Athena Advantages for Security and Speed
Amazon Athena Advantages for Security and Speed
Athena was launched by amazon in 2016 as a data analysis tool. It has generated a lot of excitement with the release of Athena. It is used for processing complex queries in a short time. Since it is server less, this means no infrastructure to manage thus all hassles for setting up servers is ruled out. It helps make it easy to analyze data on Amazon S3, making use of the standard SQL. It is by the new Athena query engine that the real power of S3 storage is completely unleashed along with no need for maintenance. There is no need for any kind of infrastructure, and querying can be started by the creation of a table and loading of data inside it.
Athena and S3 deliver results very quickly and are on par with the power of sophisticated, traditional data warehousing systems. Athena is the best option for one who uses relational databases like MySQL or traditional data warehouses for analytics workloads he wants to undertake.
- Serverless: Athena needs no warehouse to function. There is no need to worry about configuration, software updates, failures or scaling your infrastructure as the datasets and number of users grows. Athena automatically takes care of all of this for, so that one can focus on the data, not the infrastructure.
- Fast and Easy analysis of the dataset: Complex ETL jobs need not be performed by the programmers while preparing large datasets for analysis. Anybody knowing SQL can perform data analysis quickly and efficiently as well. The programmers can start interactive querying simply by pointing Athena to specific datasets in Amazon S3 and defining the schema. Athena produces the results within seconds as it starts executing the queries in parallel as soon as the query is generated.
- Pay per query: With Amazon Athena, you pay only for the query you run. Charges are procured based on the amount of data that is scanned by each query. A user can create a table, drop a table or alter a table without incurring any extra charges. Significant cost savings and performance gains can be made by compressing, partitioning or converting the data into the columnar format. Ultimately each of these operations saves the TB of data to be scanned. However, users must remember that Athena uses S3 as the underlying data store. They need to pay extra charges to store, access, and transfer data stored in S3 while using Athena.
- Highly available and durable: Amazon Athena is readily available every time to execute the queries. In case any facility is unreachable, it uses all its computing resources across multiple facilities to answer the query by automatically routing it appropriately. Moreover, amazon S3 is highly available and durable. It provides a durable and reliable infrastructure to store data and 99.99999999% of the data stored in it is durable.
- Integrated: The programmers have the option to integrate Athena with AWS Glue. Integration with AWS Glue makes it easy for users to feed or load data for analysis on the system. Glue’s fully-managed ETL capabilities can be used to transform data or convert it into columnar formats to optimize query performance and reduce costs thereby. This integration also helps the user to discover schema by crawling varying data sources, unify metadata repository across AWS services, and maintain multiple schema versions.
What should you know about Amazon Athena?
The things to be considered before taking the plunge are:
- Schema and table definitions Firstly you need to have data on S3 and with data; one has to make a database and tables. The positional order is essential while creating a schema for data on S3 otherwise the result will not be as expected.
For example, if you have a source file with ID, DATE, CAMPAIGN ID, RESPONSE, ROI and OFFERID columns, then your schema should reflect that structure:
“ID”,” DATE”,” CAMPAIGN ID”,” RESPONSE”,” ROI”,” OFFERID”
“0122”,”December 10, 2015″,”123432100A”,”8.43″,”A102B”,”4985″
“0123”,”December 10, 2015″,”543421111A”,”2.61″,”A904F”,”3212″
- Data formats: ORC, JSON, CSV, and Parquet are some formats on which Athena works. Amazon suggests converting data to columnar storage formats using Apache Parquet. Compressed and columnar formats aid in better performance and cut the storage costs as well.
- Speed and Performance: Athena provides the fastest results by using computer resources across multiple availability zones. Also, redundant data storage promises fast and durable data services.
- Supported functions: Athena uses PrestoDB, open-source software as its SQL query engine. Users can enter ANSI-standard SQL into this tool and interface directly with Amazon S3 data. This includes standard SQL functions like SELECT and relational operators like JOIN Athena supports Hive DDL for table or partition creation, modification, and deletion.
- Limitations: Athena allows users to submit only one query at a time. Only 5 concurrent queries for each account are allowed at a time.
Amazon Athena — Interactive SQL Queries for Data in Amazon S3
Amazon Athena — Interactive SQL Queries for Data in Amazon S3
- Integration with leading BI tools: Reporting and analysis of data can be done by the user by creating an interface between Athena and some other BI tool like Amazon quick sight.
- Security: The IAM policies AWM identity helps in creating full control over the dataset. The data is stored in various S3 buckets to manage access. Various tools like tableau and Power BI are used to further limit access to data. The security features can be fine-tuned to allow different people to see the dataset or also to grant access to the data of other users.
- Interface: The menu structure is easy to navigate and includes four primary tabs: Query Editor, Saved Queries, History, and Catalog Manager. As long as you have experience running SQL-queries, you will not need any specific training to use the tool.
Benefits of Amazon Athena
- Start querying instantly:: Athena is server less; therefore, one can put queries without involving in setup formalities and managing the infrastructure. Just point to your data in S3, define the schema and start querying.
- Economical: With Athena, one has to pay only for the queries that you run. You are charged $5 per terabyte scanned by your queries. You can save from 30% to 90% on your per-query costs and get better performance by compressing, partitioning, and converting your data into columnar formats
- Open, Powerful and standard: Amazon Athena uses Presto with ANSI SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Avro, and Parquet. Athena can handle complex problems very quickly. Also, the queries are resolved in an integrated and reliable way.
- Very fast: Most of the results come back in seconds. As soon as a query is formed Athena starts working in parallel all across its multiple facilities and formulates the results in the desired format.
Some good uses for Amazon Athena
Athena helps to analyze structure, unstructured and semi-structured data in an integrated way. Athena can be used to solve ad hoc queries using ANSI SQL, without the need to aggregate or load the data into Athena. It can be used to generate reports or analyse and explore data using BI tools.
Data analysis is a very complex tool and always professionals keep on trying to simplify it. The giant Amazon offers AWS service amazon Athena to solve the complex data analysis problem.
The pay-for-usage pricing model attracts analysts who believe in the power of this kind for a querying system that is in their budget or needs complex systems and DevOps support. The user interface is easy to use and is intuitive for anybody with a basic knowledge of SQL.
Companies that depend on S3 and require a quick but reliable query service might find Athena an ideal solution. It is primarily for those companies that prefer to not set up their infrastructure or want the simplicity of using Athena for a spot or ad-hoc analysis.
AWS Athena is continuously integrating sophisticated BI tools that are capable of producing reports and visualizations.
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