Category: Best Practices

Customers with data engineers and data scientists are using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) as a central orchestration platform for running data pipelines and machine learning (ML) workloads. To support these pipelines, they often require additional Python packages, such as Apache Airflow Providers. For example, a pipeline may require the Snowflake provider […]

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This enables you to use your data to acquire new insights for your business and customers. The objective of a disaster recovery plan is […]

AWS helps SaaS vendors by providing the building blocks needed to implement a streaming application with Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK), and real-time processing applications with Amazon Managed Service for Apache Flink. In this post, we look at implementation patterns a SaaS vendor can adopt when using a streaming platform as a means of integration between internal components, where streaming data is not directly exposed to third parties. In particular, we focus on Amazon MSK.

In this post, we delve into the key aspects of using Amazon EMR for modern data management, covering topics such as data governance, data mesh deployment, and streamlined data discovery. One of the key challenges in modern big data management is facilitating efficient data sharing and access control across multiple EMR clusters. Organizations have multiple […]

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. It also helps you securely access your data in operational databases, data lakes, or third-party datasets with minimal movement or copying of data. Tens of thousands […]

Managed Service for Apache Flink is a fully managed, serverless experience in running Apache Flink applications, and now supports Apache Flink 1.18.1, the latest released version of Apache Flink at the time of writing. In this post, we explore in-place version upgrades, a new feature offered by Managed Service for Apache Flink. We provide guidance on getting started and offer detailed insights into the feature. Later, we deep dive into how the feature works and some sample use cases.

Apache Hudi was originally developed by Uber in 2016 to bring to life a transactional data lake that could quickly and reliably absorb updates to support the massive growth of the company’s ride-sharing platform. Apache Hudi is now widely used to build very large-scale data lakes by many across the industry. Today, Hudi is the […]

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 […]

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, […]

The construction of big data applications based on open source software has become increasingly uncomplicated since the advent of projects like Data on EKS, an open source project from AWS to provide blueprints for building data and machine learning (ML) applications on Amazon Elastic Kubernetes Service (Amazon EKS). In the realm of big data, securing […]