AWS Big Data Blog

Apache Spark is a powerful big data engine used for large-scale data analytics. Its in-memory computing makes it great for iterative algorithms and interactive queries. You can use Apache Spark to process streaming data from a variety of streaming sources, including Amazon Kinesis Data Streams for use cases like clickstream analysis, fraud detection, and more. Kinesis Data Streams is a serverless streaming data service that makes it straightforward to capture, process, and store data streams at any scale.

With the new open source Amazon Kinesis Data Streams Connector for Spark Structured Streaming, you can use the newer Spark Data Sources API. It also supports enhanced fan-out for dedicated read throughput and faster stream processing. In this post, we deep dive into the internal details of the connector and show you how to use it to consume and produce records from and to Kinesis Data Streams using Amazon EMR.

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.

In this post, we show how to create an AWS Glue job that measures and monitors the data quality of a data pipeline using dynamic rules. We also show how to take action based on the data quality results.

In this post, we explore how to use Zingg’s entity resolution capabilities within an AWS Glue notebook, which you can later run as an extract, transform, and load (ETL) job. By integrating Zingg in your notebooks or ETL jobs, you can effectively address data governance challenges and provide consistent and accurate data across your organization.

Amazon OpenSearch Ingestion is a fully managed serverless pipeline that allows you to ingest, filter, transform, enrich, and route data to an Amazon OpenSearch Service domain or Amazon OpenSearch Serverless collection. OpenSearch Ingestion is capable of ingesting data from a wide variety of sources and has a rich ecosystem of built-in processors to take care […]

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 […]

AVB Marketing delivers custom digital solutions for their members across a wide range of products. LINQ, AVB’s proprietary product information management system, empowers their appliance, consumer electronics, and furniture retailer members to streamline the management of their product catalog. In this post, we share how AVB reduced their average search time from 3 seconds to 300 milliseconds in LINQ by adopting Amazon OpenSearch Service while processing 14.5 million record updates daily.

We’re excited to announce the launch of the Apache Iceberg on AWS technical guide. Whether you are new to Apache Iceberg on AWS or already running production workloads on AWS, this comprehensive technical guide offers detailed guidance on foundational concepts to advanced optimizations to build your transactional data lake with Apache Iceberg on AWS.

Today, we are announcing the general availability of Amazon DocumentDB (with MongoDB compatibility) zero-ETL integration with Amazon OpenSearch Service. Amazon DocumentDB provides native text search and vector search capabilities. With Amazon OpenSearch Service, you can perform advanced search analytics, such as fuzzy search, synonym search, cross-collection search, and multilingual search, on Amazon DocumentDB data. Zero-ETL […]

Today, we are announcing broker removal capability for Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned clusters, which lets you remove multiple brokers from your provisioned clusters. You can now reduce your cluster’s storage and compute capacity by removing sets of brokers, with no availability impact, data durability risk, or disruption to your data streaming […]