Category: Learning Levels

To enable your workforce users for analytics with fine-grained data access controls and audit data access, you might have to create multiple AWS Identity and Access Management (IAM) roles with different data permissions and map the workforce users to one of those roles. Multiple users are often mapped to the same role where they need […]

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) […]

Amazon Managed Workflow for Apache Airflow (Amazon MWAA) is a managed service that allows you to use a familiar Apache Airflow environment with improved scalability, availability, and security to enhance and scale your business workflows without the operational burden of managing the underlying infrastructure. In Airflow, Directed Acyclic Graphs (DAGs) are defined as Python code. […]

Amazon OpenSearch Service recently introduced the OpenSearch Optimized Instance family (OR1), which delivers up to 30% price-performance improvement over existing memory optimized instances in internal benchmarks, and uses Amazon Simple Storage Service (Amazon S3) to provide 11 9s of durability. With this new instance family, OpenSearch Service uses OpenSearch innovation and AWS technologies to reimagine […]

Our zero-ETL integration with Amazon Redshift facilitates point-to-point data movement to get it ready for analytics, artificial intelligence (AI) and machine learning (ML) using Amazon Redshift on petabytes of data. In this post, we provide step-by-step guidance on how to get started with near real time operational analytics using the Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift.

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 […]

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. This information empowers end-users to make informed decisions as to whether or not to use specific assets. In this post, we discuss the latest features of Amazon DataZone for data quality, the integration between Amazon DataZone and AWS Glue Data Quality and how you can import data quality scores produced by external systems into Amazon DataZone via API.

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.

You can use Amazon Data Firehose to aggregate and deliver log events from your applications and services captured in Amazon CloudWatch Logs to your Amazon Simple Storage Service (Amazon S3) bucket and Splunk destinations, for use cases such as data analytics, security analysis, application troubleshooting etc. By default, CloudWatch Logs are delivered as gzip-compressed objects. […]