This article explains how AutoMQ makes Kafka entirely run on object storage, enhancing scalability and performance by separating storage from computing. It covers key aspects like cache management, Write Ahead Log (WAL), object storage, recovery processes, and metadata management.
Understanding how to estimate GPU memory requirements is crucial for deploying Large Language Models (LLMs) like GPT or LLaMA. This article provides a formula to calculate the necessary GPU memory based on model parameters, precision, and overhead, ensuring efficient hardware utilization and avoiding bottlenecks during model deployment.
Databricks open-sourced Unity Catalog to strengthen the open data ecosystem and highlight the maturity of lakehouse architecture. This move, alongside their acquisition of Tabular, is poised to significantly impact the data analytics landscape and boost the importance of open-source solutions.
Explore how Uber uses Apache Pinot for over 100 low-latency analytics use cases. Read about Pinot's integration with batch sources like Apache Hive, enabling high-performance queries on large datasets through a self-serve platform for seamless data ingestion.
This tutorial delves into data quality in streaming systems, focusing on Apache Flink. It covers key aspects like completeness, uniqueness, timeliness, validity, accuracy, and consistency and how to implement them in a streaming architecture for high-quality data.
Explore the Kimball dimensional modeling framework, its core concepts, lifecycle, and how modern tools like dbt can enhance the process.
This article will guide you on constructing a multi-agent AI application with GraphRAG retrieval system, which operates entirely on your local machine and is available at no charge.
In the episode, Adel and Steve explore generative AI opportunities, building a GenAI program, use-case prioritization, fostering an AI-first culture, skills transformation, governance as a competitive edge, scaling challenges, future AI trends, and more.
Explore how the combined strengths of Dagster’s orchestration and SDF’s transformation capabilities can enhance your developer experience, streamline your data pipelines, reduce costs, and enhance data quality and reliability.
Key Takeaways:
- Unified Workflow Management: Seamlessly integrate and manage your data workflows.
- Enhanced Data Quality: Ensure consistent and reliable data through advanced transformation techniques.
- Improved Developer Experience: Experience lightning-fast execution and robust SQL validation with SDF
Explore Motherduck's innovative features powered by DuckDB. Learn how it enhances the data stack, use cases, and upcoming integrations.
Zhamak Dehgani introduced data mesh principles five years ago to decentralize data ownership and improve scalability. Organizations have since experimented with this approach. Join a webinar to learn about early insights, practical versions, and tips for successful implementation.