AWS Big Data Blog

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. This is a guest post co-written by Seva Feldman at ironSource Mobile and Eran Levy at Upsolver. ironSource, in their own words, is the leading in-app monetization and video advertising platform, making free-to-play and free-to-use possible for over 1.5B people around […]

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.

The AWS Certified Big Data — Specialty certification is a great option to help grow your career. AWS Certification shows prospective employers that you have the technical skills and expertise required to perform complex data analyses using core AWS Big Data services like Amazon EMR, Amazon Redshift, Amazon QuickSight, and more. This certification validates your understanding of data collection, storage, processing, analysis, visualization, and security.

This post discusses a new account level feature called Block Public Access (BPA) configuration that helps administrators enforce a common public access rule across all of their EMR clusters in a region.

This post provides a step-by-step guide for configuring Okta as your IdP, and using IAM roles to enable single sign-on to Amazon QuickSight. It also shows how users and groups can be managed using the Amazon QuickSight API.

Amazon QuickSight recently launched Level Aware Aggregations (LAA), which enables you to perform calculations on your data to derive advanced and meaningful insights. In this blog post, we go through examples of applying these calculations to a sample sales dataset so that you can start using these for your own needs.