Category: Intermediate (200)

Customers face a challenge when distributing cloud resources between different teams running workloads such as development, testing, or production. The resource distribution challenge also occurs when you have different line-of-business users. The objective is not only to ensure sufficient resources be consistently available to production workloads and critical teams, but also to prevent adhoc jobs […]

In today’s digital age, data is at the heart of every organization’s success. One of the most commonly used formats for exchanging data is XML. Analyzing XML files is crucial for several reasons. Firstly, XML files are used in many industries, including finance, healthcare, and government. Analyzing XML files can help organizations gain insights into […]

Amazon OpenSearch Service is a managed service that makes it simple to secure, deploy, and operate OpenSearch clusters at scale in the AWS Cloud. Amazon OpenSearch clusters are comprised of data nodes and cluster manager nodes. The cluster manager nodes elect a leader among themselves. The leader node is the authority on the metadata in […]

AWS Glue interactive sessions offer a powerful way to iteratively explore datasets and fine-tune transformations using Jupyter-compatible notebooks. Interactive sessions enable you to work with a choice of popular integrated development environments (IDEs) in your local environment or with AWS Glue or Amazon SageMaker Studio notebooks on the AWS Management Console, all while seamlessly harnessing […]

AWS Glue interactive sessions allow you to run interactive AWS Glue workloads on demand, which enables rapid development by issuing blocks of code on a cluster and getting prompt results. This technology is enabled by the use of notebook IDEs, such as the AWS Glue Studio notebook, Amazon SageMaker Studio, or your own Jupyter notebooks. […]

Streaming data has become an indispensable resource for organizations worldwide because it offers real-time insights that are crucial for data analytics. The escalating velocity and magnitude of collected data has created a demand for real-time analytics. This data originates from diverse sources, including social media, sensors, logs, and clickstreams, among others. With streaming data, organizations […]

Amazon’s serverless Apache Kafka offering, Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless, is attracting a lot of interest. It’s appreciated for its user-friendly approach, ability to scale automatically, and cost-saving benefits over other Kafka solutions. However, a hurdle encountered by many users is the requirement of MSK Serverless to use AWS Identity and Access Management (IAM) access control. At the time of writing, the Amazon MSK library for IAM is exclusive to Kafka libraries in Java, creating a challenge for users of other programming languages. In this post, we aim to address this issue and present how you can use Amazon API Gateway and AWS Lambda to navigate around this obstacle.

In this post we discuss how we can build and orchestrate in a few steps an ETL process for Amazon Redshift using Amazon S3 Event Notifications for automatic verification of source data upon arrival and notification in specific cases. And we show how to use AWS Step Functions for the orchestration of the data pipeline. It can be considered as a starting point for teams within organizations willing to create and build an event driven data pipeline from data source to data warehouse that will help in tracking each phase and in responding to failures quickly. Alternatively, you can also use Amazon Redshift auto-copy from Amazon S3 to simplify data loading from Amazon S3 into Amazon Redshift.

To improve a Spark application’s efficiency, it’s essential to monitor its performance and behavior. In this post, we demonstrate how to publish detailed Spark metrics from Amazon EMR to Amazon CloudWatch. This will give you the ability to identify bottlenecks while optimizing resource utilization.

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 […]