Tag: Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In 2019, Athena added support for federated queries to run SQL […]

This blog post was last reviewed and updated May, 2022 to include AWS Lake Formation resource sharing model. Many AWS customers use a multi-account strategy to host applications for different departments within the same company. However, you might deploy services like Amazon QuickSight using a single-account approach, which raises challenges when you need to use […]

MEDHOST has been providing products and services to healthcare facilities of all types and sizes for over 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their patient care and operational excellence with its integrated clinical and financial EHR solutions. MEDHOST also offers a comprehensive Emergency Department Information System with […]

This post provides guidance on how to configure Amazon Athena federation with AWS Lambda and Amazon Redshift, while addressing performance considerations to ensure proper use.

Toyota Motor Corporation (TMC), a global automotive manufacturer, has made “connected cars” a core priority as part of its broader transformation from an auto company to a mobility company. In recent years, TMC and its affiliate technology and big data company, Toyota Connected, have developed an array of new technologies to provide connected services that […]

Developing extract, transform, and load (ETL) data pipelines is one of the most time-consuming steps to keep data lakes, data warehouses, and databases up to date and ready to provide business insights. You can categorize these pipelines into distributed and non-distributed, and the choice of one or the other depends on the amount of data […]

Most organizations have to comply with regulations when dealing with their customer data. For that reason, datasets that contain personally identifiable information (PII) is often anonymized. A common example of PII can be tables and columns that contain personal information about an individual (such as first name and last name) or tables with columns that, if joined with another table, can trace back to an individual. You can use AWS Analytics services to anonymize your datasets. In this post, I describe how to use Amazon Athena to anonymize a dataset.  You can then use AWS Lake Formation to provide the right access to the right personas.

This is a guest post by Sara Miller, Head of Data Management and Data Lake, Direct Energy; and Zhouyi Liu, Senior AWS Developer, Direct Energy. Enterprise companies like Direct Energy migrate on-premises data warehouses and services to AWS to achieve fully manageable digital transformation of their organization. Freedom from traditional data warehouse constraints frees up […]

Amazon QuickSight is a fast, cloud-powered, business intelligence service that makes it easy to deliver insights and integrates seamlessly with your data lake built on Amazon Simple Storage Service (Amazon S3). QuickSight users in your organization often need access to only a subset of columns for compliance and security reasons. Without having a proper solution […]

We parse through millions of scooter and user events generated daily (over 300 events per second) to extract actionable insight. We selected AWS Glue to perform this task. Our primary ETL job reads the newly added raw event data from Amazon S3, processes it using Apache Spark, and writes the results to our Amazon Redshift data warehouse. AWS Glue plays a critical role in our ability to scale on demand. After careful evaluation and testing, we concluded that AWS Glue ETL jobs meet all our needs and free us from procuring and managing infrastructure.