Category: AWS Big Data

This post discusses how Vortexa harnesses the power of Apache Kafka to improve real-time data accuracy and accelerate time-to-market by using a combination of Lenses.io for greater observability and Amazon Managed Streaming for Apache Kafka (Amazon MSK) to create clusters on demand.

This is a guest post by Sara Miller, Head of Data Management and Data Lake, Direct Energy; and Zhouyi Liu, Senior AWS Developer, Direct Energy. Enterprise companies like Direct Energy migrate on-premises data warehouses and services to AWS to achieve fully manageable digital transformation of their organization. Freedom from traditional data warehouse constraints frees up […]

Amazon QuickSight recently introduced four new features—embedded authoring, namespaces for multi-tenancy, custom user permissions, and account-level customizations—that, with existing dashboard embedding and API capabilities available in the Enterprise Edition, allow you to integrate advanced dashboarding and analytics capabilities within SaaS applications. Developers and independent software vendors (ISVs) who build these applications can now offer embedded, […]

New Relic can now ingest data directly from Amazon Kinesis Data Firehose, expanding the insights New Relic can give you into your cloud stacks so you can deliver more perfect software. Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to AWS services like Amazon Simple Storage Service (Amazon S3), Amazon […]

Amazon Kinesis Data Firehose now provides an easy-to-configure and straightforward process for streaming data to a third-party service for analysis, including logs from AWS services. Due to the varying formats and high volume of this data, it’s a complex challenge to identify and correlate key event details and data points to fix issues and improve […]

The value of data is time sensitive. Streaming data services can help you move data quickly from data sources to new destinations for downstream processing. For example, Amazon Kinesis Data Firehose can reliably load streaming data into data stores like Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES), and Splunk. […]

This post shares the simple steps you can take to use the new Amazon Redshift usage controls feature to monitor and control your usage and associated cost for Amazon Redshift Spectrum and Concurrency Scaling features. Redshift Spectrum enables you to power a lake house architecture to directly query and join data across your data warehouse and data lake, and Concurrency Scaling enables you to support thousands of concurrent users and queries with consistently fast query performance.

In the first post of this series, Federating access to your Amazon Redshift cluster with Active Directory: Part 1, you set up Microsoft Active Directory Federation Services (AD FS) and Security Assertion Markup Language (SAML) based authentication and tested the SAML federation using a web browser. In Part 2, you learn to set up an […]

Many customers request detailed steps to set up federated single sign-on (SSO) using Microsoft Active Directory Federation Services (AD FS) for Amazon Redshift. In this two-part series, you will find detailed steps to achieve federated SSO using AD FS. Part 1 contains steps to set up a Windows 2016 domain controller and AD FS and […]

This post explores how you can use AWS Lake Formation integration with Amazon EMR (still in beta) to implement fine-grained column-level access controls while using Spark in a Zeppelin Notebook. My previous post Extract Salesforce.com data using AWS Glue and analyzing with Amazon Athena showed you a simple use case for extracting any Salesforce object data using AWS Glue and Apache Spark, saving it to Amazon Simple Storage Service (Amazon S3), cataloging the data using the Data Catalog in Glue, and querying it using Amazon Athena.