Category: Amazon EMR

Large language models (LLMs) are becoming increasing popular, with new use cases constantly being explored. In general, you can build applications powered by LLMs by incorporating prompt engineering into your code. However, there are cases where prompting an existing LLM falls short. This is where model fine-tuning can help. Prompt engineering is about guiding the […]

With Amazon EMR 6.15, we launched AWS Lake Formation based fine-grained access controls (FGAC) on Open Table Formats (OTFs), including Apache Hudi, Apache Iceberg, and Delta lake. This allows you to simplify security and governance over transactional data lakes by providing access controls at table-, column-, and row-level permissions with your Apache Spark jobs. Many […]

As data engineering becomes increasingly complex, organizations are looking for new ways to streamline their data processing workflows. Many data engineers today use Apache Airflow to build, schedule, and monitor their data pipelines. However, as the volume of data grows, managing and scaling these pipelines can become a daunting task. Amazon Managed Workflows for Apache […]

Amazon EMR is pleased to announce integration with Amazon Simple Storage Service (Amazon S3) Access Grants that simplifies Amazon S3 permission management and allows you to enforce granular access at scale. With this integration, you can scale job-based Amazon S3 access for Apache Spark jobs across all Amazon EMR deployment options and enforce granular Amazon […]

In this era of big data, organizations worldwide are constantly searching for innovative ways to extract value and insights from their vast datasets. Apache Spark offers the scalability and speed needed to process large amounts of data efficiently. Amazon EMR is the industry-leading cloud big data solution for petabyte-scale data processing, interactive analytics, and machine […]

In this post, we show how you can authenticate into SageMaker Studio using an existing Active Directory (AD), with authorized access to both Amazon S3 and Hive cataloged data using AD entitlements via Apache Ranger integration and AWS IAM Identity Center (successor to AWS Single Sign-On). With this solution, you can manage access to multiple SageMaker environments and SageMaker Studio notebooks using a single set of credentials. Subsequently, Apache Spark jobs created from SageMaker Studio notebooks will access only the data and resources permitted by Apache Ranger policies attached to the AD credentials, inclusive of table and column-level access.

Amazon EMR Studio is an integrated development environment (IDE) that makes it straightforward for data scientists and data engineers to develop, visualize, and debug data engineering and data science applications written in R, Python, Scala, and PySpark. EMR Studio provides fully managed Jupyter notebooks and tools such as Spark UI and YARN Timeline Server via […]

This is a guest post co-written with Mukul Sharma, Software Development Engineer, and Ozcan IIikhan, Director of Engineering from GoDaddy. GoDaddy empowers everyday entrepreneurs by providing all the help and tools to succeed online. With more than 20 million customers worldwide, GoDaddy is the place people come to name their ideas, build a professional website, […]

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Using Spark SQL to run Hive workloads provides not only the simplicity of SQL-like queries but also taps into the exceptional speed and performance provided by Spark. Spark SQL is an Apache Spark module for structured data processing. One […]

Amazon EMR Serverless provides a serverless runtime environment that simplifies the operation of analytics applications that use the latest open source frameworks, such as Apache Spark and Apache Hive. With EMR Serverless, you don’t have to configure, optimize, secure, or operate clusters to run applications with these frameworks. You can run analytics workloads at any scale with automatic […]