Tag: AWS Glue

In today’s data-driven world, the ability to seamlessly integrate and utilize diverse data sources is critical for gaining actionable insights and driving innovation. As organizations increasingly rely on data stored across various platforms, such as Snowflake, Amazon Simple Storage Service (Amazon S3), and various software as a service (SaaS) applications, the challenge of bringing these […]

In today’s landscape, data streams continuously from countless sources such as social media interactions to Internet of Things (IoT) device readings. This torrent of real-time information presents both a challenge and an opportunity for businesses. To harness the power of this data effectively, organizations need robust systems for ingesting, processing, and analyzing streaming data at […]

AWS Glue is a fully managed, serverless data integration service provided by Amazon Web Services (AWS) that uses Apache Spark as one of its backend processing engines (as of this writing, you can use Python Shell, Spark, or Ray). Data skew occurs when the data being processed is not evenly distributed across the Spark cluster, […]

AWS Glue is a serverless data integrating service that you can use to catalog data and prepare for analytics. With AWS Glue, you can discover your data, develop scripts to transform sources into targets, and schedule and run extract, transform, and load (ETL) jobs in a serverless environment. AWS Glue jobs are responsible for running […]

Many organizations, small and large, are working to migrate and modernize their analytics workloads on Amazon Web Services (AWS). There are many reasons for customers to migrate to AWS, but one of the main reasons is the ability to use fully managed services rather than spending time maintaining infrastructure, patching, monitoring, backups, and more. Leadership […]

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting […]

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Over the years, data lakes on Amazon Simple Storage […]

AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started). In AWS ProServe-led customer engagements, the use cases we work on usually come with technical complexity and scalability requirements. In this post, we discuss a common use case in relation to operational data processing and the solution we built using Apache Hudi and AWS Glue.

The post Archive and Purge Data for Amazon RDS for PostgreSQL and Amazon Aurora with PostgreSQL Compatibility using pg_partman and Amazon S3 proposes data archival as a critical part of data management and shows how to efficiently use PostgreSQL’s native range partition to partition current (hot) data with pg_partman and archive historical (cold) data in […]

In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]