AWS Big Data Blog

Category: AWS Big Data

Simplify your Spark dependency management with Docker in EMR 6.0.0

Apache Spark is a powerful data processing engine that gives data analyst and engineering teams easy to use APIs and tools to analyze their data, but it can be challenging for teams to manage their Python and R library dependencies. Installing every dependency that a job may need before it runs and dealing with library […]

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Expertise validation in AWS data analytics with AWS Certification

AWS Training and Certification now has a new exam version for the AWS Certified Data Analytics – Specialty certification, which validates expertise in designing, building, and maintaining analytics solutions that are efficient, cost-effective, and secure.

The new exam version includes updated content across all domains: collection, storage and data management, processing, analysis and visualization, and security. Earning AWS Certified Data Analytics – Specialty shows that you meet the standard set by AWS data analytics experts.

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Speeding up Etleap models at AXS with Amazon Redshift materialized views

The materialized views feature in Amazon Redshift is now generally available and has been benefiting customers and partners in preview since December 2019. One customer, AXS, is a leading ticketing, data, and marketing solutions provider for live entertainment venues in the US, UK, Europe, and Japan. Etleap, an Amazon Redshift partner, is an extract, transform, […]

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Ingest Excel data automatically into Amazon QuickSight

Amazon QuickSight is a fast, cloud-powered, business intelligence (BI) service that makes it easy to deliver insights to everyone in your organization. This post demonstrates how to build a serverless data ingestion pipeline to automatically import frequently changed data into a SPICE (Super-fast, Parallel, In-memory Calculation Engine) dataset of Amazon QuickSight dashboards. It is sometimes […]

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Lower your costs with the new pause and resume actions on Amazon Redshift

Today’s analytics workloads typically require a data warehouse to be available 24 hours a day, 7 days a week. However, there may be times when you need an Amazon Redshift cluster for a short duration of time at frequent (or infrequent) intervals. For example, you may run a periodic ETL job or use a cluster […]

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Tune Hadoop and Spark performance with Dr. Elephant and Sparklens on Amazon EMR

This post demonstrates how to install Dr. Elephant and Sparklens on an Amazon EMR cluster and run workloads to demonstrate these tools’ capabilities. Amazon EMR is a managed Hadoop service offered by AWS to easily and cost-effectively run Hadoop and other open-source frameworks on AWS.

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Govern how your clients interact with Apache Kafka using API Gateway

In this blog post, we will show you how Amazon API Gateway can answer these questions as a component between your Amazon MSK cluster and your clients. Amazon MSK is a fully managed service for Apache Kafka that makes it easy to provision Kafka clusters with just a few clicks without the need to provision servers, manage storage, or configure Apache Zookeeper manually. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications.

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Integrate Power BI with Amazon Redshift for insights and analytics

Amazon Redshift is a fast, fully managed, cloud-native data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Microsoft Power BI is a business analytics service that delivers insights to enable fast, informed decisions. With Power BI, you can perform ad-hoc query […]

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Streaming ETL with Apache Flink and Amazon Kinesis Data Analytics

Most businesses generate data continuously in real time and at ever-increasing volumes. Data is generated as users play mobile games, load balancers log requests, customers shop on your website, and temperature changes on IoT sensors. You can capitalize on time-sensitive events, improve customer experiences, increase efficiency, and drive innovation by analyzing this data quickly. The […]

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