Category: Analytics

Amazon QuickSight announced the launch of Cross Data Source Join, which allows you to connect to multiple data sources and join data across these sources in Amazon QuickSight directly to create data sets used to build dashboards. For example, you can join transactional data in Amazon Redshift that contains customer IDs with Salesforce tables that […]

In Part 1 of this post series, you learned how to use Apache Airflow, Genie, and Amazon EMR to manage big data workflows. This post guides you through deploying the AWS CloudFormation templates, configuring Genie, and running an example workflow authored in Apache Airflow.

This post introduces an architecture that helps centralized platform teams maintain a big data platform to service thousands of concurrent ETL workflows, and simplifies the operational tasks required to accomplish that.

his post shows how to access and manage data in multiple accounts from a central AWS Lake Formation account. The walkthrough demonstrates a centralized catalog residing in the master Lake Formation account, with data residing in the different accounts. The post shows how to grant access permissions from the Lake Formation service to read, write and update the catalog and access data in different accounts.

This post shows how ironSource uses Upsolver to build, manage, and orchestrate its data lake with minimal coding and maintenance. We discuss why ironSource opted for a data lake architecture based on Amazon S3, how ironSource built the data lake using Upsolver, how to create outputs to analytic services such as Amazon Athena, Amazon ES, and Tablea, and the benefits of this solution.

The first post of this series discusses two key AWS Glue capabilities to manage the scaling of data processing jobs. The first allows you to horizontally scale out Apache Spark applications for large splittable datasets. The second allows you to vertically scale up memory-intensive Apache Spark applications with the help of new AWS Glue worker types. The post also shows how to use AWS Glue to scale Apache Spark applications with a large number of small files commonly ingested from streaming applications using Amazon Kinesis Data Firehose. Finally, the post shows how AWS Glue jobs can use the partitioning structure for large datasets in Amazon S3 to provide faster execution times for Apache Spark applications.

In this post, I show how to use AWS Step Functions and AWS Glue Python Shell to orchestrate tasks for those Amazon Redshift-based ETL workflows in a completely serverless fashion. AWS Glue Python Shell is a Python runtime environment for running small to medium-sized ETL tasks, such as submitting SQL queries and waiting for a response. Step Functions lets you coordinate multiple AWS services into workflows so you can easily run and monitor a series of ETL tasks. Both AWS Glue Python Shell and Step Functions are serverless, allowing you to automatically run and scale them in response to events you define, rather than requiring you to provision, scale, and manage servers.

This post focuses on active security for Amazon Redshift, in particular DataSunrise’s capabilities for masking and access control of personally identifiable information (PII), which you can back with DataSunrise’s passive security offerings such as auditing access of sensitive information. This post discusses DataSunrise security for Amazon Redshift, how it works, and how to get started.

This post discusses installing notebook-scoped libraries on a running cluster directly via an EMR Notebook. Before this feature, you had to rely on bootstrap actions or use custom AMI to install additional libraries that are not pre-packaged with the EMR AMI when you provision the cluster. This post also discusses how to use the pre-installed Python libraries available locally within EMR Notebooks to analyze and plot your results. This capability is useful in scenarios in which you don’t have access to a PyPI repository but need to analyze and visualize a dataset.

In this post, we present a solution for analyzing Google Analytics data using Amazon Athena. We’re including a reference architecture built on moving hit-level data from Google Analytics to Amazon S3, performing joins and enrichments, and visualizing the data using Amazon Athena and Amazon QuickSight. Upsolver is used for data lake automation and orchestration, enabling customers to get started quickly.