AWS Big Data Blog

Category: Analytics

Streaming Architecture

Apache Iceberg optimization: Solving the small files problem in Amazon EMR

Currently, Iceberg provides a compaction utility that compacts small files at a table or partition level. But this approach requires you to implement the compaction job using your preferred job scheduler or manually triggering the compaction job. In this post, we discuss the new Iceberg feature that you can use to automatically compact small files while writing data into Iceberg tables using Spark on Amazon EMR or Amazon Athena.

Non-JSON ingestion using Amazon Kinesis Data Streams, Amazon MSK, and Amazon Redshift Streaming Ingestion

Organizations are grappling with the ever-expanding spectrum of data formats in today’s data-driven landscape. From Avro’s binary serialization to the efficient and compact structure of Protobuf, the landscape of data formats has expanded far beyond the traditional realms of CSV and JSON. As organizations strive to derive insights from these diverse data streams, the challenge […]

Process and analyze highly nested and large XML files using AWS Glue and Amazon Athena

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 […]

Architecture Diagram

Build event-driven architectures with Amazon MSK and Amazon EventBridge

Based on immutable facts (events), event-driven architectures (EDAs) allow businesses to gain deeper insights into their customers’ behavior, unlocking more accurate and faster decision-making processes that lead to better customer experiences. In EDAs, modern event brokers, such as Amazon EventBridge and Apache Kafka, play a key role to publish and subscribe to events. EventBridge is […]

Network connectivity patterns for Amazon OpenSearch Serverless

Amazon OpenSearch Serverless is an on-demand, auto-scaling configuration for Amazon OpenSearch Service. OpenSearch Serverless enables a broad set of use cases, such as real-time application monitoring, log analytics, and website search. OpenSearch Serverless lets you use OpenSearch without having to worry about scaling and managing an OpenSearch cluster. A collection can be accessed over the […]

Improved resiliency with cluster manager task throttling for Amazon OpenSearch Service

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 […]

Introducing hybrid access mode for AWS Glue Data Catalog to secure access using AWS Lake Formation and IAM and Amazon S3 policies

To ease the transition of data lake permissions from an IAM and S3 model to Lake Formation, we’re introducing a hybrid access mode for AWS Glue Data Catalog. This feature lets you secure and access the cataloged data using both Lake Formation permissions and IAM and S3 permissions. Hybrid access mode allows data administrators to onboard Lake Formation permissions selectively and incrementally, focusing on one data lake use case at a time. For example, say you have an existing extract, transform and load (ETL) data pipeline that uses the IAM and S3 policies to manage data access. Now you want to allow your data analysts to explore or query the same data using Amazon Athena. You can grant access to the data analysts using Lake Formation permissions, to include fine-grained controls as needed, without changing access for your ETL data pipelines.

Using Experian identity resolution with AWS Clean Rooms to achieve higher audience activation match rates

This is a guest post co-written with Tyler Middleton, Experian Senior Partner Marketing Manager, and Jay Rakhe, Experian Group Product Manager. As the data privacy landscape continues to evolve, companies are increasingly seeking ways to collect and manage data while protecting privacy and intellectual property. First party data is more important than ever for companies […]

Use the new SQL commands MERGE and QUALIFY to implement and validate change data capture in Amazon Redshift

Amazon Redshift has added many features to enhance analytical processing like ROLLUP, CUBE and GROUPING SETS, which were demonstrated in the post Simplify Online Analytical Processing (OLAP) queries in Amazon Redshift using new SQL constructs such as ROLLUP, CUBE, and GROUPING SETS. Amazon Redshift has recently added many SQL commands and expressions. In this post, we talk about two new SQL features, the MERGE command and QUALIFY clause, which simplify data ingestion and data filtering.

Stitch Fix seamless migration: Transitioning from self-managed Kafka to Amazon MSK

Stitch Fix is a personalized clothing styling service for men, women, and kids. In this post, we will describe how and why we decided to migrate from self-managed Kafka to Amazon Managed Streaming for Apache Kafka (Amazon MSK).