Category: Amazon Athena

April 2024: This post was reviewed for accuracy. Although structured data remains the backbone for many data platforms, increasingly unstructured or semi-structured data is used to enrich existing information or create new insights. Amazon Athena enables you to analyze a wide variety of data. This includes tabular data in CSV or Apache Parquet files, data […]

As organizations expand analytics practices and hire data scientists and other specialized roles, big data pipelines are growing increasingly complex. Sophisticated models are being built using the troves of data being collected every second. The bottleneck today is often not the know-how of analytical techniques. Rather, it’s the difficulty of building and maintaining ETL (extract, transform, and load) jobs using tools that might be unsuitable for the cloud. In this post, I demonstrate a solution to this challenge.

This blog post shows how to use the CREATE TABLE AS SELECT (CTAS statement) in Athena. It also shows how to automate the creation of unique tables that represent a subset of the AWS CloudTrail data. This helps us audit Amazon Athena usage.

This post walks through three scenarios to enable trusted users to access Athena using temporary security credentials. First, we use SAML federation where user credentials were stored in Active Directory. Second, we use a custom credentials provider library to enable cross-account access. And third, we use an EC2 Instance Profile role to provide temporary credentials for users in our organization to access Athena.

In this blog post, we describe the complete solution for collecting, analyzing, and visualizing VPC flow log data. In addition, we created a single AWS CloudFormation template that lets you efficiently deploy this solution into your own account.

August 2024: This post was reviewed and updated for accuracy. In part 1 of this series, we demonstrated how to build a data pipeline in support of a data lake. We used key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we discuss […]

In this two-part series, we show you how to build a data pipeline in support of a data lake. We use key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we focus on generating simple inferences from that data that can support RTP parameters.

This is a guest post by Yossi Wasserman, a data collection & innovation team leader at Similar Web. SimilarWeb, in their own words: SimilarWeb is the pioneer of market intelligence and the standard for understanding the digital world. SimilarWeb provides granular insights about any website or mobile app across all industries in every region. SimilarWeb […]

In this post, we discuss how Pagely worked with Beyondsoft, an AWS Advanced Consulting Partner, to use ConvergDB, an open-source tool developed by Beyondsoft, to build a DevOps-centric data pipeline. This pipeline uses AWS Glue to transform application logs into optimized tables that can be queried quickly and cost effectively using Amazon Athena.

In this post, we show you how to export data from a DynamoDB table, convert it into a more efficient format with AWS Glue, and query the data with Athena. This approach gives you a way to pull insights from your data stored in DynamoDB.