Conquering Data Challenges For Your Generative AI Success
Embarking on the journey of implementing successful Generative AI requires a strong and reliable foundation, with data preparation at its heart.
Amazon Athena
Continuous data collection and meaningful analysis on time is a crucial process for any business or organization, however, it can be challenging if you are not using the right set of technologies and processes.
Unstructured or semi-structured data is often received in multiple iterations and doesn’t provide the full picture. Data collected in this way can create data silos and lead to low standards of data quality and accuracy.
Data storage and management can be tricky business, especially when you’re trying to keep things like data quality, validity, and security in check.
Amazon Athena makes it quick and easy to run interactive SQL queries on data files stored in Amazon S3, without having to load or format the data first. So you can focus on your analysis, not on ETL jobs.
You can point Amazon Athena to the data files stored in S3 and immediately start querying these data files using standard SQL and get the results within seconds.
Amazon Athena is great for low latency, interactive data analysis of large datasets stored in Amazon S3 in a wide variety of data formats such as CSV, JSON, ORC, Avro, or Parquet.
Amazon Athena lets you query data where it lives without moving, loading, or migrating it. You can query the data from relational, non-relational, object, or on-premises custom databases.
With Athena’s federated query feature, you can run SQL across data stored in different types of sources. Athena uses various data source connectors that invoke an AWS Lambda function to execute the federated query. A data source connector is a code that translates between your target data source and Athena.
Athena provides built-in connectors for popular data stores including Amazon Redshift and Amazon DynamoDB. You can use these connectors to enable SQL analytics on structured, semi-structured, object, graph, time series, and other types of data.
Amazon Athena is the perfect tool for anyone with SQL knowledge who wants to extract insights from a wide range of data sources without learning a new programming language.
With on-demand analysis and support for multiple data stores, Athena makes it easy to get the information you need, when you need it.
And because Athena can also integrate with various BI tools and SQL clients via ODBC and JDBC, it’s easy to explore your data and generate reports using your favorite software tools fast.
Amazon Athena is serverless, which means you don’t have to worry about configurations, software updates, or database administration. You can focus on data analysis as soon as you receive the data files in S3.
With Amazon Athena, you only pay for the queries you run. You’re charged based on the amount of data scanned by each query.
By compressing, partitioning, or converting your data to a columnar format, you can get significant cost savings and performance gains because each of those operations reduces the amount of data that Athena needs to scan to execute a query.
Athena gives you blazing-fast query results, even on large datasets. With Athena, you don’t have to worry about managing or tuning clusters for optimal performance.
Athena is the perfect tool for quickly analyzing your big data without doing any time-consuming data-loading or data-migration activities.
Athena can only access your data stored in S3 if you grant IAM users the data access policies.
If your data is stored in an encrypted form, Athena can also query this data easily from S3.
In addition, Athena integrates with KMS (AWS Key Management Service) and provides you with the option to encrypt your result sets.
Athena is the perfect solution for anyone sick of data transformation or data loading. With schema on reading technology, your table definitions will apply to your data in S3 while queries are executed.
Athena uses the AWS Glue catalog to store metadata information about databases and tables.
And since AWS Glue is integrated across a wide range of AWS services – including S3, Amazon Aurora, RDS, MySQL, Amazon RDS PostgreSQL, and Amazon Redshift – it provides out-of-the-box integration with Amazon EMR and any Apache Hive Metastore-compatible application.
AWS Glue automatically crawls your data sources to identify data formats and suggest schemas and transformations.
If you want to do any kind of meaningful analysis on time-based data, you need to continuously load a high volume of data from storage into a database after running it through processes like data cleansing, formatting, and validation.
This can be a complex and time-consuming task for any organization, requiring resources, time, storage space, and compute infrastructure. We can help you with the following Amazon Athena solutions:
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