![]() Sophisticated query optimization, scaling processing across thousands of nodes, so results areįast-even with large data sets and complex queries. Massive amounts of data-up to exabytes-stored in a data lake built on S3. By contrast, Amazon Redshift allows you to run Redshift SQL queries directly against Amazon Redshift is a large-scale, managed data warehouse service that supports massive The reader to read, decompress, and process the columns that are only required for the currentĪnother way to perform in-place querying of data assets in a data lake built on Amazon S3 is to ORC and Parquet formats are columnar formats that allows Using ORC-formatted or Parquet-formatted inventory files because these formats provide faster Athena supports querying S3 inventory files in ORC, Parquet, or CSV format. On the replication and encryption status of the objects for business, compliance, and regulatory ![]() Inventory is an S3 tool to help manage storage. Athena can also be used to query S3 inventory using standard SQL. When querying an existing table, Athena uses Presto under the hood, a distributed SQLĮngine. You can create the table and use Athena to query theĭata based on a metadata store that integrates with the ETL and data discovery features of AWS Glue. Metadata store for the data stored in S3. It can also be used with third-party reporting andīusiness intelligence tools by connecting these tools to Athena with a JDBC driver.Īthena also natively integrates with AWS Glue Data Catalog which provides a persistent Athena integrates with Amazon QuickSight for easy data visualization. SQL without first aggregating or loading the data into Athena. You can also use Athena to run one-time queries using ANSI Supported data asset formats include CSV, JSON, or columnar data formats You can use Athena to process unstructured, semi-structured, and In the data lake built on S3 and begin using standard SQL to run one-time queries and get resultsĪthena is serverless, so there is no infrastructure to set up or manage, and you only payįor the volume of data assets scanned during the queries you run.Īthena scales automatically-running queries in parallel-so results are fast, even with largeĭatasets and complex queries. With aįew actions in the AWS Management Console, you can use Athena directly against data assets stored Service that makes it easier for you to analyze data directly in S3 using standard SQL. Reducing latency to retrieve the data and optimizing cost. Users can also delimit the result set, thus, S3 Select operates on objects stored in CSV, JSON, or Apache Parquet format,Īnd other compression formats such as GZIP or BZIP2. Using S3 Select, users can run SQL statements to filter and retrieve only a subset of data stored That improves the performance of accessing large amounts of data from your data lake built on S3. In addition to in-place querying using Athena and Redshift Spectrum, S3 also providesĬapabilities to retrieve subset of your data through S3 Select and S3 Glacier Select , The previous sections, provides the data discovery and ETL capabilities, and Amazon Athena andĪmazon Redshift Spectrum provides the serverless in-place querying capabilities. Transformed data into these environments, and then running query jobs. Method of performing an ETL process, creating a Hadoop cluster or data warehouse, loading the ![]() This makes the ability to analyze vast amounts of unstructured data accessible toĪny data lake user who can use SQL, and makes it far more cost effective than the traditional Users can query S3 data without any additional infrastructure, and only pay for the queries Services that not only helps perform in-place querying, but also avoids the procurement and This section provides an overview of serverless Presto on Amazon EMR and various partner tools. There are various tools to perform in-place querying for data stored in a data lake, such as Separate analytics platforms or data warehouses. This allows users to run sophisticatedĪnalytical queries directly on their data stored in S3, without having to copy and load data into In-place transformation and querying of data assets. ![]() One of the most important capabilities of a data lake on AWS is the ability to perform ![]()
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