Category: Learning Levels

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. […]

Real-time data streaming and event processing present scalability and management challenges. AWS offers a broad selection of managed real-time data streaming services to effortlessly run these workloads at any scale. In this post, Nexthink shares how Amazon Managed Streaming for Apache Kafka (Amazon MSK) empowered them to achieve massive scale in event processing. Experiencing business […]

In Part 2 of this series, we discussed how to enable AWS Glue job observability metrics and integrate them with Grafana for real-time monitoring. Grafana provides powerful customizable dashboards to view pipeline health. However, to analyze trends over time, aggregate from different dimensions, and share insights across the organization, a purpose-built business intelligence (BI) tool […]

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. In this post, we discuss how to successfully conduct a proof of concept in Amazon Redshift by going through the main stages of the process, available tools that accelerate implementation, and common use cases.

We are delighted to announce the latest version of the Data Analytics Lens, an AWS Well-Architected whitepaper. AWS Well-Architected provides a consistent approach to evaluate architectures and implement scalable designs. The AWS Well-Architected Framework is based on six pillars—operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. With the framework, cloud architects, system architects, engineers, and developers can build secure, high-performance, resilient, and efficient infrastructure for their applications and workloads.