Remove Dashboards Remove Data Lake Remove Data Processing Remove Reference
article thumbnail

Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

Data analytics on operational data at near-real time is becoming a common need. Due to the exponential growth of data volume, it has become common practice to replace read replicas with data lakes to have better scalability and performance. Apache Hudi connector for AWS Glue For this post, we use AWS Glue 4.0,

article thumbnail

Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 112
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Implement alerts in Amazon OpenSearch Service with PagerDuty

AWS Big Data

You can use this proactive alerting to monitor data patterns for existing data, monitor clusters, detect patterns, and more. OpenSearch Dashboard provides an alerting plugin that you can use to set up various types of monitors and alerts. For instructions, refer to Creating and managing Amazon OpenSearch Service domains.

article thumbnail

Access Amazon Athena in your applications using the WebSocket API

AWS Big Data

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products.

article thumbnail

Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

AWS Big Data

For more details, refer to Amazon QuickSight resource type reference. In this post, we show how to automate the deployment of a QuickSight analysis connecting to an Amazon Redshift data warehouse with a CloudFormation template. For instructions, refer to Authorizing connections from Amazon QuickSight to Amazon Redshift clusters.

article thumbnail

Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

article thumbnail

Governing data in relational databases using Amazon DataZone

AWS Big Data

It also makes it easier for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization to discover, use, and collaborate to derive data-driven insights. Note that a managed data asset is an asset for which Amazon DataZone can manage permissions.