AWS Big Data Blog

Category: Intermediate (200)

Introducing Amazon MWAA support for Apache Airflow version 2.9.2

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed orchestration service for Apache Airflow that significantly improves security and availability, and reduces infrastructure management overhead when setting up and operating end-to-end data pipelines in the cloud. Today, we are announcing the availability of Apache Airflow version 2.9.2 environments on Amazon MWAA. Apache Airflow […]

Run Apache XTable on Amazon MWAA to translate open table formats

In this post, we show you how to get started with Apache XTable on AWS and how you can use it in a batch pipeline orchestrated with Amazon Managed Workflows for Apache Airflow (Amazon MWAA). To understand how XTable and similar solutions work, we start with a high-level background on metadata management in an OTF and then dive deeper into XTable and its usage.

Amazon DataZone enhances data discovery with advanced search filtering

Amazon DataZone, a fully managed data management service, helps organizations catalog, discover, analyze, share, and govern data between data producers and consumers. We are excited to announce the introduction of advanced search filtering capabilities in the Amazon DataZone business data catalog. With the improved rendering of glossary terms, you can now navigate large sets of […]

Implement disaster recovery with Amazon Redshift

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 […]

Access Amazon Redshift data from Salesforce Data Cloud with Zero Copy Data Federation

This post is co-authored by Vijay Gopalakrishnan, Director of Product, Salesforce Data Cloud. In today’s data-driven business landscape, organizations collect a wealth of data across various touch points and unify it in a central data warehouse or a data lake to deliver business insights. This data is primarily used for analytical and machine learning purposes, […]

Run Apache Spark 3.5.1 workloads 4.5 times faster with Amazon EMR runtime for Apache Spark

The Amazon EMR runtime for Apache Spark is a performance-optimized runtime that is 100% API compatible with open source Apache Spark. It offers faster out-of-the-box performance than Apache Spark through improved query plans, faster queries, and tuned defaults. Amazon EMR on EC2, Amazon EMR Serverless, Amazon EMR on Amazon EKS, and Amazon EMR on AWS […]

Image showing multiple producers and consumers each publishing to a stream-per-tenant

Stream multi-tenant data with Amazon MSK

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.

Apply fine-grained access and transformation on the SUPER data type in Amazon Redshift

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per […]

Ingest and analyze your data using Amazon OpenSearch Service with Amazon OpenSearch Ingestion

In today’s data-driven world, organizations are continually confronted with the task of managing extensive volumes of data securely and efficiently. Whether it’s customer information, sales records, or sensor data from Internet of Things (IoT) devices, the importance of handling and storing data at scale with ease of use is paramount. A common use case that […]

Optimize storage costs in Amazon OpenSearch Service using Zstandard compression

As part of an indexing operation, the ingested documents are stored as immutable segments. Each segment is a collection of various data structures, such as inverted index, block K dimensional tree (BKD), term dictionary, or stored fields, and these data structures are responsible for retrieving the document faster during the search operation. Out of these data structures, stored fields, which are largest fields in the segment, are compressed when stored on the disk and based on the compression strategy used, the compression speed and the index storage size will vary. In this post, we discuss the performance of the Zstandard algorithm, which was introduced in OpenSearch v2.9, amongst other available compression algorithms in OpenSearch.