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Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

Data is at the center of every application, process, and business decision. Customers across industries are becoming more data driven and looking to increase revenue, reduce cost, and optimize their business operations by implementing near real time analytics on transactional data, thereby enhancing agility.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

We’re living in the age of real-time data and insights, driven by low-latency data streaming applications. The volume of time-sensitive data produced is increasing rapidly, with different formats of data being introduced across new businesses and customer use cases.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Businesses are constantly evolving, and data leaders are challenged every day to meet new requirements. Customers are using AWS and Snowflake to develop purpose-built data architectures that provide the performance required for modern analytics and artificial intelligence (AI) use cases.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

FMs are multimodal; they work with different data types such as text, video, audio, and images. Large language models (LLMs) are a type of FM and are pre-trained on vast amounts of text data and typically have application uses such as text generation, intelligent chatbots, or summarization.

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Synchronize your Salesforce and Snowflake data to speed up your time to insight with Amazon AppFlow

AWS Big Data

Customers across industries seek meaningful insights from the data captured in their Customer Relationship Management (CRM) systems. To achieve this, they combine their CRM data with a wealth of information already available in their data warehouse, enterprise systems, or other software as a service (SaaS) applications.

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Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

This allows you to simplify security and governance over transactional data lakes by providing access controls at table-, column-, and row-level permissions with your Apache Spark jobs. Many large enterprise companies seek to use their transactional data lake to gain insights and improve decision-making.

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How Gupshup built their multi-tenant messaging analytics platform on Amazon Redshift

AWS Big Data

Objective Gupshup wanted to build a messaging analytics platform that provided: Build a platform to get detailed insights, data, and reports about WhatsApp/SMS campaigns and track the success of every text message sent by the end customers. Additionally, extract, load, and transform (ELT) data processing is sped up and made easier.