AWS Big Data Blog

Category: AWS Big Data

Building a cost efficient, petabyte-scale lake house with Amazon S3 lifecycle rules and Amazon Redshift Spectrum: Part 1

The continuous growth of data volumes combined with requirements to implement long-term retention (typically due to specific industry regulations) puts pressure on the storage costs of data warehouse solutions, even for cloud native data warehouse services such as Amazon Redshift. The introduction of the new Amazon Redshift RA3 node types helped in decoupling compute from […]

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Run Apache Spark 3.0 workloads 1.7 times faster with Amazon EMR runtime for Apache Spark

With Amazon EMR release 6.1.0, Amazon EMR runtime for Apache Spark is now available for Spark 3.0.0. EMR runtime for Apache Spark is a performance-optimized runtime for Apache Spark that is 100% API compatible with open-source Apache Spark. In our benchmark performance tests using TPC-DS benchmark queries at 3 TB scale, we found EMR runtime […]

The following architecture diagram shows SingleStore connecting with AWS Glue for an ETL job.

Building fast ETL using SingleStore and AWS Glue

Disparate data systems have become a norm in many companies. The reasons for this vary: different teams in the organization select data system best suited for its primary function, the responsibility for choosing these data systems may have been decentralized across different departments, a merged company may still use separate data systems from the formerly […]

Validate, evolve, and control schemas in Amazon MSK and Amazon Kinesis Data Streams with AWS Glue Schema Registry

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Data streaming technologies like Apache Kafka and Amazon Kinesis Data Streams capture and distribute data generated by thousands or millions of applications, websites, or machines. These technologies […]

The following diagram illustrates a high-level integration of AWS Systems Manager interaction with an EMR cluster.

Securing access to EMR clusters using AWS Systems Manager

Organizations need to secure infrastructure when enabling access to engineers to build applications. Opening SSH inbound ports on instances to enable engineer access introduces the risk of a malicious entity running unauthorized commands. Using a Bastion host or jump server is a common approach used to allow engineer access to Amazon EMR cluster instances by […]

The state machine transforms data using AWS Glue.

Building complex workflows with Amazon MWAA, AWS Step Functions, AWS Glue, and Amazon EMR

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your extract, transform, and load (ETL) jobs and data pipelines. You can use AWS Step Functions as a serverless function orchestrator to build scalable […]

The following diagram illustrates the architecture for this solution.

Introducing Amazon EMR integration with Apache Ranger

This post was last updated July 2022. Data security is an important pillar in data governance. It includes authentication, authorization , encryption and audit. Amazon EMR enables you to set up and run clusters of Amazon Elastic Compute Cloud (Amazon EC2) instances with open-source big data applications like Apache Spark, Apache Hive, Apache Flink, and Presto. You may […]

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Estimating scoring probabilities by preparing soccer matches data with AWS Glue DataBrew

In soccer (or football outside of the US), players decide to take shots when they think they can score. But how do they make that determination vs. when to pass or dribble? In a fraction of a second, in motion, while chased from multiple directions by other professional athletes, they think about their distance from […]

We use Amazon SNS for sending notifications to users, and EventBridge is integrated to schedule running the Step Functions workflow.

Orchestrating an AWS Glue DataBrew job and Amazon Athena query with AWS Step Functions

As the industry grows with more data volume, big data analytics is becoming a common requirement in data analytics and machine learning (ML) use cases. Also, as we start building complex data engineering or data analytics pipelines, we look for a simpler orchestration mechanism with graphical user interface-based ETL (extract, transform, load) tools. Recently, AWS […]