Category: Big Data

[ad_1] Recently, AWS introduced over 50 new capabilities across its streaming services, significantly enhancing performance, scale, and cost-efficiency. Some of these innovations have tripled performance, provided 20 times faster scaling, and reduced failure recovery times by up to 90%. We have made it nearly effortless for customers to bring real-time context to AI applications and…

Read More “Top 6 game changers from AWS that redefine streaming data” »

[ad_1] This blog was co-authored by DeNA Co., Ltd. and Amazon Web Services Japan. DeNA Co., Ltd. (DeNA) engages in a variety of businesses, from games and live communities to sports & the community and healthcare & medical, under our mission to delight people beyond their wildest dreams. Among these, the healthcare & medical business…

Read More “How DeNA Co., Ltd. accelerated anonymized data quality tests up to 100 times faster using Amazon Redshift Serverless and dbt” »

[ad_1] The need to integrate diverse data sources has grown exponentially, but there are several common challenges when integrating and analyzing data from multiple sources, services, and applications. First, you need to create and maintain independent connections to the same data source for different services. Second, the data connectivity experience is inconsistent across different services….

Read More “Introducing a new unified data connection experience with Amazon SageMaker Lakehouse unified data connectivity” »

[ad_1] This post is co-written by Adam Gaulding, Solution Architect at Satori. In this post, we continue from Accelerate Amazon Redshift secure data use with Satori – Part 1, and explain how Satori, an Amazon Redshift Ready partner, simplifies both the user experience of gaining access to data and the admin practice of granting and…

Read More “Accelerate Amazon Redshift secure data use with Satori – Part 2” »

[ad_1] One-time and complex queries are two common scenarios in enterprise data analytics. One-time queries are flexible and suitable for instant analysis and exploratory research. Complex queries, on the other hand, refer to large-scale data processing and in-depth analysis based on petabyte-level data warehouses in massive data scenarios. These complex queries typically involve data sources…

Read More “Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt” »

[ad_1] Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Data…

Read More “An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)” »

[ad_1] As data is generated at an unprecedented rate, streaming solutions have become essential for businesses seeking to harness near real-time insights. Streaming data—from social media feeds, IoT devices, e-commerce transactions, and more—requires robust platforms that can process and analyze data as it arrives, enabling immediate decision-making and actions. This is where Apache Spark Structured…

Read More “Run Apache Spark Structured Streaming jobs at scale on Amazon EMR Serverless” »

[ad_1] Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. With its massively parallel processing (MPP) architecture and columnar data storage, Amazon Redshift delivers high price-performance for complex analytical queries against large datasets. To interact with and analyze data stored in Amazon…

Read More “Federate to Amazon Redshift Query Editor v2 with Microsoft Entra ID” »

[ad_1] In today’s data-driven world, tracking and analyzing changes over time has become essential. As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis. It enables organizations to maintain audit trails, perform trend analysis, identify data…

Read More “Implement historical record lookup and Slowly Changing Dimensions Type-2 using Apache Iceberg” »

[ad_1] Given the importance of data in the world today, organizations face the dual challenges of managing large-scale, continuously incoming data while vetting its quality and reliability. The importance of publishing only high-quality data can’t be overstated—it’s the foundation for accurate analytics, reliable machine learning (ML) models, and sound decision-making. Equally crucial is the ability…

Read More “Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality” »