Category: Intermediate (200)

AWS recently announced that Apache Flink is generally available for Amazon EMR on Amazon Elastic Kubernetes Service (EKS). Apache Flink is a scalable, reliable, and efficient data processing framework that handles real-time streaming and batch workloads (but is most commonly used for real-time streaming). Amazon EMR on EKS is a deployment option for Amazon EMR […]

Welcome back to our exciting exploration of architectural patterns for real-time analytics with Amazon Kinesis Data Streams! In this fast-paced world, Kinesis Data Streams stands out as a versatile and robust solution to tackle a wide range of use cases with real-time data, from dashboarding to powering artificial intelligence (AI) applications. In this series, we […]

In this post, we show how to create an AWS Glue job that measures and monitors the data quality of a data pipeline using dynamic rules. We also show how to take action based on the data quality results.

In this post, we explore how to use Zingg’s entity resolution capabilities within an AWS Glue notebook, which you can later run as an extract, transform, and load (ETL) job. By integrating Zingg in your notebooks or ETL jobs, you can effectively address data governance challenges and provide consistent and accurate data across your organization.

In today’s data-driven landscape, the efficiency and accessibility of querying tools play a crucial role in driving businesses forward. Amazon Redshift recently announced integration with Visual Studio Code (), an action that transforms the way data practitioners engage with Amazon Redshift and reshapes your interactions and practices in data management. This innovation not only unlocks […]

In today’s data-driven landscape, managing and analyzing vast amounts of data, especially logs, is crucial for organizations to derive insights and make informed decisions. However, handling this data efficiently presents a significant challenge, prompting organizations to seek scalable solutions without the complexity of infrastructure management. Amazon OpenSearch Serverless lets you run OpenSearch in the AWS […]

In this post, we discuss how to implement bucketing on AWS data lakes, including using Athena CTAS statement and AWS Glue for Apache Spark. We also cover bucketing for Apache Iceberg tables.

Starting from release 6.14, Amazon EMR Studio supports interactive analytics on Amazon EMR Serverless. You can now use EMR Serverless applications as the compute, in addition to Amazon EMR on EC2 clusters and Amazon EMR on EKS virtual clusters, to run JupyterLab notebooks from EMR Studio Workspaces. EMR Studio is an integrated development environment (IDE) […]

Many enterprises are migrating their on-premises data stores to the AWS Cloud. During data migration, a key requirement is to validate all the data that has been moved from source to target. This data validation is a critical step, and if not done correctly, may result in the failure of the entire project. However, developing […]

Customers are using AWS and Snowflake to develop purpose-built data architectures that provide the performance required for modern analytics and artificial intelligence (AI) use cases. Implementing these solutions requires data sharing between purpose-built data stores. This is why Snowflake and AWS are delivering enhanced support for Apache Iceberg to enable and facilitate data interoperability between data services. Apache Iceberg is an open-source table format that provides reliability, simplicity, and high performance for large datasets with transactional integrity between various processing engines.